IoT device fog networking operation

Abstract

Method comprising: Receiving IoT device data generated by an IoT device (106; 314, 320; 506) while the IoT device (106) is operating via a LAN (108; 704) on a local infrastructure network device (104; 304, 306; 706) of a plurality of local infrastructure network devices (104; 304, 306; 706), wherein the IoT device data is received as part of access by the IoT device (106) to network services via the LAN (108; 704); Performing load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using at least one of the plurality of local infrastructure network devices (104; 304, 306; 706); Analyzing the IoT device data at at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to locally generate IoT device analysis data as part of analyzing the IoT device data through fog networking; Providing the IoT device analysis data generated at the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) by the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to a remote system as part of analyzing the IoT device data through fog networking; Determining properties of the IoT device (106; 314, 320; 506) during operation to generate the IoT device data; and Transmitting the IoT device analytics data from the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to the remote system through application-based routing based on the characteristics of the IoT device (106; 314, 320; 506) in operation.

Description

STATE OF THE ART

[0001] US 2017 / 0201585 A1 describes techniques for the distributed processing of Internet of Things (IoT) device data by edge systems located within a globally distributed group of colocation facilities provided and managed by a colocation facility provider. For example, a method comprises selecting, by at least one of a plurality of edge computing systems located in respective colocation facilities, each provided and managed by a single colocation facility provider, a selected edge computing system from the plurality of edge computing systems to process data related to events generated by an IoT device.The method also includes providing an API (Application Programming Interface) endpoint for communication with the IoT device at the selected edge computing system and receiving, by the selected edge computing system at the endpoint device, the data associated with the events generated by the IoT device and processing, by the selected edge computing system, the data associated with the events generated by the IoT device.

[0002] US 9 860 677 B1 describes an Internet of Things (IoT) system and method with IoT sensors for measuring data and forwarding the data to gateway devices, the gateway devices receiving the data and delivering the data to a cloud infrastructure, and a coordinator for assigning ownership of the IoT sensors to the gateway devices.

[0003] US 2016 / 0 127 073 A1 describes a communication system comprising a transmitter circuit for receiving a plurality of input data streams and for applying a different orthogonal function to each of the plurality of input data streams. The transmitter circuit processes each of the plurality of input data streams to which the different orthogonal function has been applied to spatially locate a first group of the plurality of input data streams to which the different orthogonal function has been applied on a first carrier signal and to spatially locate a second group of the plurality of input data streams to which the different orthogonal function has been applied on a second carrier signal. The transmitter circuit temporally associates the first carrier signal and the second carrier signal with a third carrier signal and transmits the third carrier signal over a communication link.A receiver circuit receives the third carrier signal over the communication link and temporally separates the third carrier signal into the first carrier signal and the second carrier signal. The receiver then separates the multiple input data streams to which the different orthogonal function has been applied into the multiple input data streams to which the different orthogonal function has been applied. Finally, the receiver removes the orthogonal function from each of the plurality of input data streams and outputs the plurality of input data streams. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 illustrates a diagram of an example system for generating IoT device analytics on a local infrastructure network device using fog networking. Fig. Figure 2 illustrates a flowchart of an example method for analyzing IoT device data on local infrastructure network devices using fog networking. Fig. Figure 3 illustrates a diagram of an example system for load balancing across local infrastructure network devices for the purpose of analyzing IoT device data through fog networking. Fig. 4 illustrates a flowchart of an example method for load balancing between local infrastructure network devices for the purpose of analyzing IoT device data through fog networking. Fig. 5 illustrates a diagram of an example local IoT device analytics system 502. Fig. 6 illustrates a flowchart of an example method for analyzing IoT device data through fog networking on a local infrastructure network device. Fig. Figure 7 illustrates a diagram of an example system for analyzing IoT device data generated by an IoT camera device through fog networking. Fig. 8 illustrates a flowchart of an example method for analyzing camera data at a local infrastructure network device through fog networking. DETAILED DESCRIPTION

[0004] Fig. 1 shows a diagram 100 of an example of a system for generating IoT device analytics (English: "Internet of Things"; German: "Internet of Things", hereinafter referred to as "IoT") on a local infrastructure network device using fog networking. The system using the example of Fig. 1 includes a computer-readable medium 102, a multi-protocol infrastructure network device 104-1 ... multi-protocol infrastructure network device 104-n (hereinafter referred to as "multi-protocol infrastructure network devices 104"), an IoT device 106-1 ... IoT device 106-n (hereinafter referred to as "IoT devices 106"), a local area network 108 (hereinafter referred to as "LAN 108"), and a fog networking-based IoT device analysis system 110. In the Fig. In the example system illustrated in Figure 1, the IoT devices 106 are operatively coupled to the multi-protocol infrastructure network devices 104 via the LAN 108. Although LAN 108 is illustrated as only a local area network, LAN 108 may include a plurality of separate LANs that couple the IoT devices 106 to the multi-protocol infrastructure network devices 104.

[0005] Computer-readable medium 102 and other computer-readable media discussed in this document are intended to include all legally required media (e.g., in the United States, under 35 USC 101) and specifically exclude all non-legally required media to the extent the exclusion is necessary to the validity of a claim containing the computer-readable medium. Known legally required computer-readable media include hardware (e.g., registers, random access memory (RAM), non-volatile storage (NV), to name a few), but may also be limited to hardware.

[0006] The computer-readable medium 102 and other computer-readable media discussed in this paper are intended to represent a variety of potentially applicable technologies. For example, the computer-readable medium 102 may be used to form a network or part of a network. When two components are co-located on a device, the computer-readable medium 102 may include a bus or other data line or layer. When a first component is co-located on one device and a second component is co-located on another device, the computer-readable medium 102 may include a wireless or wired backend network, or LAN. The computer-readable medium 102 may also include a relevant part of a WAN or other network, if applicable.

[0007] Assuming a computer-readable medium includes a network, the network may be any applicable communications network, such as the Internet or an infrastructure network. The term "Internet" as used in this paper refers to a network of networks that utilize certain protocols, such as the TCP / IP protocol, and possibly other protocols, such as the Hypertext Transfer Protocol (hereafter referred to as "HTTP") for Hypertext Markup Languages ​​(hereafter referred to as "HTML"), that constitute the World Wide Web (hereafter referred to as "the Web"). Networks may include private enterprise networks and virtual private networks (collectively, private networks). As the name suggests, private networks are under the control of a single entity. Private networks may include a central office and optional regional office locations (collectively, office locations).Many office locations allow remote users to connect to the private network office locations through another network, such as the Internet.

[0008] The devices, systems, and computer-readable media described in this paper may be implemented as a computer system or parts of a computer system or a plurality of computer systems. In general, a computer system includes a processor, memory, non-volatile memory, and an interface. A typical computer system usually includes at least one processor, memory, and a device (e.g., a bus) that connects the memory to the processor. The processor may, for example, be a general-purpose central processing unit (CPU), such as a microprocessor, or a special-purpose processor, such as a microcontroller.

[0009] Memory may include, but is not limited to, random-access memory (RAM), such as dynamic RAM (DRAM) and static RAM (SRAM). Memory may be local, remote, or distributed. The bus may also couple the processor to non-volatile memory. Non-volatile memory is often a magnetic floppy disk or hard disk, a magnetic-optical disk, an optical disk, read-only memory (ROM) such as CD-ROM, EPROM, or EEPROM, a magnetic or optical card, or another form of storing large amounts of data. Some of this data is often written to memory by a direct memory access process during software execution on the computer system. Non-volatile memory may be local, remote, or distributed. Non-volatile memory is optional because systems can be built with all relevant data in memory.

[0010] Software is typically stored in non-volatile memory. In fact, for large programs, it may not even be possible to store the entire program in memory. Nevertheless, it should be understood that the software is moved, when necessary, to a computer-readable location suitable for processing, and that this location is referred to in this paper as memory. Even when software is moved to memory for execution, the processor typically uses hardware registers to store the values ​​associated with the software and a local cache, ideally configured to speed up execution. As used herein, when a software program is referred to as being "implemented on a computer-readable storage medium," it is assumed that the software program is stored in a suitable known or convenient location (from non-volatile memory to hardware registers).A processor is considered to be “configured to execute a program” if at least one value associated with the program is stored in a register readable by the processor.

[0011] In one example of operation, a computer system may be controlled by operating system software, which is a software program that includes a file management system, such as a disk operating system. An example of operating system software with associated file management software is the Windows® family of operating systems from Microsoft Corporation of Redmond, Washington, and its associated file management systems. Another example of operating system software with associated file management software is the Linux operating system and its associated file management system. The file management system is typically stored in non-volatile memory and causes the processor to perform the various actions required by the operating system to input and output data and to store data in memory, including storing files on the non-volatile memory.

[0012] The bus may also couple the processor to the interface. The interface may include one or more input and / or output devices (I / O devices). Depending on implementation-specific or other considerations, the I / O devices may include, for example, a keyboard, a mouse or other pointing device, hard disk drives, printers, a scanner, and other I / O devices, including a display device. The display device may include, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), or any other applicable known or practical display device. The interface may include one or more modem or network interfaces. Note that a modem or network interface may be considered part of the computer system. The interface may be an analog modem, ISDN modem, cable modem, token ring interface, satellite transmission interface (e.g.,"Direct PC") or other interfaces for connecting one computer system to other computer systems. Interfaces enable the connection of computer systems and other devices in a network.

[0013] The computing systems may be compatible with, implemented as part of, or over a cloud-based computing system. As used in this paper, a cloud-based computing system is a system that provides virtualized computing resources, software, and / or information to end devices. The computing resources, software, and / or information may be virtualized by managing centralized services and resources that the edge devices can access through a communications interface, such as a network. As used in this paper, edge devices include applicable devices at the edge of a network or combination of networks, such as a LAN, a WLAN, a consumer network, or an enterprise network. For example, an edge device may be a network device at the edge of a LAN that provides wireless access to network services over a WLAN.In another example, an edge device may be an IoT device that accesses network services over a LAN. In yet another example, an edge device may be an IoT device that transmits data over at least part of a wired connection, e.g., a Universal Serial Bus (hereafter "USB") connection. "Cloud" may be a marketing term and, for the purposes of this paper, may include any of the networks described herein. The cloud-based computing system may include a subscription to services or use a usage-based pricing model. Users may access the protocols of the cloud-based computing system through a web browser or other container application residing on their end device.

[0014] A computer system can be implemented as a machine, as part of a machine, or across multiple machines. As used in this paper, a machine includes one or more processors or a portion thereof. A portion of one or more processors may include a portion of hardware that is smaller than all of the hardware comprising one or more processors, such as a subset of registers, the portion of the processor being dedicated to one or more threads of a multithreaded processor, a time interval during which all or part of the processor is dedicated to executing a portion of the machine's functionality, or the like. Thus, a first machine and a second machine may have one or more dedicated processors, or a first machine and a second machine may share one or more processors with each other or with other machines.Depending on implementation-specific or other considerations, a machine can be centralized or its functionality distributed. A machine can include hardware, firmware, or software embodied in a computer-readable medium for execution by the processor. That is, the machine includes hardware. The processor transforms data into new data using implemented data structures and methods, as described with reference to the figures in this paper.

[0015] The machines described in this paper, or the machines through which the systems and devices described in this paper may be implemented, may be cloud-based machines. As used in this paper, a cloud-based machine is a machine capable of executing applications and / or functionality using a cloud-based computing system. All or portions of the applications and / or functionality may be distributed across multiple computing devices and need not be limited to just one computing device. In some embodiments, the cloud-based machines may execute functionality and / or modules accessed by end users via a web browser or container application, without the functionality and / or modules being installed locally on the end users' computing devices.

[0016] As used in this paper, data stores are intended to include repositories with any organization of data, including tables, comma-separated values ​​(CSV) files, traditional databases (e.g., SQL), or other known or convenient organizational formats. Data stores may be implemented, for example, as software embodied in a physical, computer-readable medium on a particular machine, in firmware, in hardware, in a combination thereof, or in any applicable, known, or suitable device or system. Data store-associated components, such as database interfaces, may be considered "part" of a data store, a part of another system component, or a combination thereof, although the physical location and other characteristics of data store-associated components are not critical to understanding the techniques described in this paper.

[0017] Data storage can include data structures. As used in this paper, a data structure is associated with a specific way of storing and organizing data in a computer so that it can be used efficiently in a particular context. Data structures are generally based on the ability of a computer to fetch and store data anywhere in its memory, specified by an address, a bit sequence that can itself be stored in memory and manipulated by the program. For example, some data structures are based on calculating the addresses of data elements using arithmetic operations, while other data structures are based on storing addresses of data elements within the structure itself. Many data structures utilize both principles, sometimes combined in non-trivial ways.Implementing a data structure typically involves writing a set of procedures that create and manipulate instances of that structure. The data stores described in this paper may be cloud-based data stores. A cloud-based data store is a data store compatible with cloud-based computing systems and machines.

[0018] Cloud computing is a type of wide-area computing that provides shared resources and data to computers and other devices on demand. It is a model for ubiquitous, on-demand access to a common pool of configurable computing resources (e.g., computer networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort. Cloud computing and storage solutions offer users and businesses various options for storing and processing their data, either in private or off-site data centers that may be located far from users—where the distance could span across a city or across the world. Cloud computing is based on sharing resources to achieve coherence and economies of scale, much like a utility (like the electrical grid) across an electrical network.

[0019] Coming back to the Fig. In the example system illustrated in Figure 1, the multi-protocol infrastructure network devices 104 are intended to represent devices that send and receive data used in providing access to network services of a network. For example, the multi-protocol infrastructure network devices 104 may enable network service access from IoT devices over a LAN. In a particular implementation, the multi-protocol infrastructure network devices 104 receive signals from an IoT device, to which the multi-protocol infrastructure network devices 104 may or may not grant access to network services (depending on implementation-related, configuration-related, network-related, or device-specific considerations).In another implementation, the multi-protocol infrastructure network devices 104 send signals to an IoT device to which the multi-protocol infrastructure network devices 104 may or may not allow access to network services (depending on implementation-related, configuration-related, network-related, or device-specific considerations).

[0020] The multi-protocol infrastructure network devices 104 and other network devices described in this paper include or function as routers, switches, access points, gateways, including wireless gateways, repeaters, a combination thereof, or other applicable devices. As gateways, the multi-protocol infrastructure network devices 104 can send and receive data between a WAN via a LAN backend and IoT devices coupled to the multi-protocol infrastructure network devices 104. As access points, the multi-protocol infrastructure network devices 104 can couple an IoT device to a network device forming a LAN backend and then a WAN via a LAN.For example, the multi-protocol infrastructure network devices 104 may be used to route data between IoT devices coupled to the multi-protocol infrastructure networks 104 as part of a personal area network (hereinafter referred to as a “PAN”) formed at least in part by the IoT devices and the multi-protocol infrastructure networks 104.

[0021] In a particular implementation, the multi-protocol infrastructure network devices 104 may operate according to applicable wireless communication protocols or standards, e.g., WiFi, ZigBee®, Bluetooth®, applicable low-power communication standards, or applicable PAN wireless communication standards. For example, the multi-protocol infrastructure network devices 104 may wirelessly pair with IoT devices via wireless connections constructed according to a Wi-Fi protocol and then communicate with the IoT devices via the wireless connections constructed according to the Wi-Fi protocol. In another example, the multi-protocol infrastructure network devices 104 may wirelessly pair with IoT devices via wireless connections constructed according to a Bluetooth® protocol and then communicate with the IoT devices via the wireless connections constructed according to the Bluetooth® protocol.

[0022] In a particular implementation, the multi-protocol infrastructure network devices 104 function to communicate with IoT devices over a wired connection. Specifically, the multi-protocol infrastructure network devices 104 may be coupled to IoT devices via suitable wired connections and communicate with the IoT devices over the wired connections. For example, the multi-protocol infrastructure network devices 104 may be coupled to an IoT device over a USB connection and receive data generated at the IoT device over the USB connection.

[0023] The multi-protocol infrastructure network devices 104 are intended to represent infrastructure network devices capable of providing IoT devices with access to network services via wired and wireless connections established and maintained according to different protocols, thereby forming different LANs, e.g., different WLANs. For example, the multi-protocol infrastructure network devices 104 can provide network service access to a first IoT device via a Bluetooth® connection and network service access to a second IoT device via a Wi-Fi connection. Furthermore, the multi-protocol infrastructure network devices 104 can simultaneously provide multiple IoT devices with access to network services of a network via different wired or wireless connections.For example, the multi-protocol infrastructure network devices 104 may provide network service access to a first IoT device over a Bluetooth® connection and simultaneously provide network service access to a second IoT device over a Wi-Fi connection.

[0024] When IoT devices can access network services across different LANs, the multi-protocol infrastructure network devices 104 enable IoT devices configured to access network services across the different LANs to communicate with each other across the different LANs. For example, a first IoT device may send data to a unique multi-protocol infrastructure network device across a Bluetooth®-based WLAN, and the same or a different unique multi-protocol infrastructure network device may send the data to a second IoT device across a ZigBee®-based WLAN. This allows the first and second IoT devices to communicate with each other even though the first IoT device is configured to access network services across a Bluetooth®-based WLAN and the second IoT device is configured to access network services across a ZigBee®-based WLAN.Data may be translated or transformed into another protocol for transmission, e.g., from a Wi-Fi-connected device to a ZigBee®-connected device, at an applicable network device within a LAN, e.g., at the multi-protocol infrastructure network devices 104, a fog networking system, or a cloud-based system.

[0025] Fog networking, also known as "fogging," is an architecture that uses one or more collaborative multiple end-user clients or near-user edge devices to perform a significant amount of storage (and not primarily in cloud data centers), communication (and not over a WAN backbone), control, configuration, measurement, and management (and not primarily over network gateways). In the examples discussed in this paper, some of the storage, communication, and management is performed on the multi-protocol infrastructure network devices 104, the corresponding LAN, and / or the enterprise network to which the resources belong.

[0026] The multi-protocol infrastructure network devices 104 function as infrastructure devices by being formed as part of a local area network infrastructure for providing network service access over LANs. For example, the multi-protocol infrastructure network devices 104 may be forwarding devices of a LAN network infrastructure and at least partially form a network backend. In another example, the multi-protocol infrastructure network devices 104 may be edge devices of a LAN network infrastructure and at least partially form a ZigBee® network or a Wi-Fi network.

[0027] In a particular implementation, the multi-protocol infrastructure network devices 104 include protocol-compatible or protocol-specific hardware used in establishing and maintaining a LAN according to a particular protocol. For example, the multi-protocol infrastructure network devices 104 may include ZigBee® interfaces configured to enable the multi-protocol infrastructure network devices 104 to establish and maintain ZigBee®-based WLANs. Furthermore, the multi-protocol infrastructure network devices 104 may include ZigBee® interfaces as part of removable dongles paired with the multi-protocol infrastructure network devices 104 or as part of fixed hardware integrated into the multi-protocol infrastructure network devices 104. In another example, the multi-protocol infrastructure network devices 104 include hardware used to access network services over a Wi-Fi WLAN.In yet another example, the multi-protocol infrastructure network devices 104 include a USB interface that enables an IoT device to couple to the multi-protocol infrastructure network devices 104 via the USB interface to form at least a portion of a LAN.

[0028] In a particular implementation, the multi-protocol infrastructure network devices 104 function as direct portals to a LAN backend. As direct portals to a LAN backend, the multi-protocol infrastructure network devices 104 are connected directly to a network switch, e.g., a router or bridge, of a LAN backend. The multi-protocol infrastructure network devices 104 can each be directly connected to corresponding individual network switches of a LAN backend, or a plurality of the multi-protocol infrastructure network devices 104 can be directly connected to a single network switch of the LAN backend. A LAN backend to which the multi-protocol infrastructure network devices 104 are coupled can be formed by either a combination of a wired or wireless backend, or both.

[0029] In a particular implementation, a single device of the multi-protocol infrastructure network devices 104 may serve as a direct portal to a LAN backend and provide direct access to network services of a network without having to exchange data with other devices at the edge of a LAN, e.g., with other multi-protocol infrastructure network devices 104. For example, if an IoT device is coupled to a multi-protocol infrastructure network device via a ZigBee® connection as part of a ZigBee® network, data may be exchanged between the IoT device and a LAN backend via the multi-protocol infrastructure network device without having to exchange the data with another multi-protocol infrastructure network device or a node-forming IoT device as part of the ZigBee® network.By directly accessing a LAN backend without exchanging data from other edge devices, the power consumption of the other edge devices can be reduced and / or the network traffic on the other edge devices can be reduced, resulting in increased network throughput.

[0030] In a specific implementation, the multi-protocol infrastructure network devices 104 are directly connected edge devices of a LAN. Directly connected edge devices are edge devices that are directly connected to each other, independent of couplings provided via a LAN backend. In particular, the multi-protocol infrastructure network devices 104 can be coupled to each other via wireless connections at the edge of the LAN and outside of a LAN backend. The multi-protocol infrastructure network devices 104 can exchange data directly with each other, without the use of a LAN backend, via wired or wireless connections formed between the multi-protocol infrastructure network devices 104. By directly connecting edge devices, the multi-protocol infrastructure network devices 104 can communicate with each other at a LAN backend and / or a WAN.The multi-protocol infrastructure network devices 104 may use connections that directly connect the multi-protocol infrastructure network devices 104 to manage IoT devices over LANs. For example, as directly connected edge devices, the multi-protocol infrastructure network devices 104 may locally exchange data between IoT devices as part of IoT policy enforcement when a WAN fails and Internet access is interrupted, at least partially using wired or wireless connections that directly connect the multi-protocol infrastructure network devices 104 to each other, independent of a LAN backend.

[0031] In a particular implementation as part of IoT device management, the multi-protocol infrastructure network devices 104 act as a local controller of IoT devices over LANs. The multi-protocol infrastructure network devices 104 can control an IoT device according to an IoT policy. For example, an IoT policy can specify that a camera be turned off at a specific time, and the multi-protocol infrastructure network devices 104 can instruct the camera to turn off at a specific time over LANs according to the IoT policy. Furthermore, the multi-protocol infrastructure network devices 104 can control an IoT device according to a user's instructions. For example, if a user checks the status of the lights in their office,by displaying published IoT device data, and gives the instruction to turn off the lights via the LAN, then the multi-protocol infrastructure network devices 104 can instruct the lights to turn off according to the instructions received from the user.

[0032] The functions and data received and generated by the functioning of the multi-protocol infrastructure network devices 104 may be distributed across multiple multi-protocol infrastructure network devices 104 or executed by a single multi-protocol infrastructure network device. For example, a single multi-protocol infrastructure network device may be configured to receive data from an IoT device and appropriately update an IoT policy used by other multi-protocol infrastructure network devices. In another example, a plurality of network devices with multi-local protocol infrastructure may manage movement pattern records of IoT devices for use in maintaining an IoT policy.

[0033] To access the Fig. Returning to the example system illustrated in Figure 1, IoT devices 106 are intended to represent devices used to access network services of an enterprise or consumer network over a LAN. When accessing network services, IoT devices 106 may use a LAN to either send or receive data. For example, IoT devices 106 may send and receive data over a WLAN. Depending on implementation-specific or other considerations, IoT devices 106 may communicate over a LAN with other IoT devices or sources or systems over a WAN. IoT devices 106 may include wired and wireless interfaces through which IoT devices 106 can send and receive data over wired and wireless connections. Examples of IoT devices include thermostats, mobile devices, biological managers, sensory devices, and function-oriented devices.When sending and receiving data over a wireless connection, the IoT devices 106 may communicate according to an applicable wireless communication protocol or standard, e.g., Wi-Fi, ZigBee®, Bluetooth®, Z-Wave®, applicable low-power communication standards, or applicable PAN wireless communication standards. For example, the IoT devices 106 may send and receive data over a ZigBee® WLAN conforming to IEEE 802.15-4, which is hereby incorporated by reference. The IoT devices 106 may include unique identifiers that may be used to facilitate access to network services by the IoT devices 106.

[0034] In a particular implementation, the IoT devices 106 act as, or include, stations by including a wireless interface through which the IoT devices 106 can be coupled to a multi-protocol infrastructure network device via Wi-Fi connections. A station, as used in this paper, may be referred to as a device having a Media Access Control (MAC) address and a Physical Layer (PHY) interface to a wireless medium conforming to the IEEE 802.11 standard. For example, the IoT devices 106 may be referred to as a station, where appropriate. IEEE 802.11a-1999, IEEE 802.11b-1999, IEEE 802.11g-2003, IEEE 802.11-2007, IEEE 802.11n TGn Draft 8.0 (2009), and IEEE 802.11ac-2013 are incorporated by reference. As used in this paper, a system that is 802.11 standards-compliant or 802.11 standards-compliant, at least some of at least one or more of the requirements and / or recommendations of the integrated documents, or requirements and / or recommendations from earlier drafts of the documents, and includes Wi-Fi systems. Wi-Fi is a non-technical description generally correlated with the IEEE 802.11, Wi-Fi Protected Access (WPA) and WPA2 security standards, and the Extensible Authentication Protocol (EAP) standard. In alternative implementations, a station may conform to a standard other than Wi-Fi or IEEE 802.11, be referred to as something other than a "station," and have different interfaces to a wireless or other medium.

[0035] IEEE 802.3 is a working group and a collection of IEEE standards created by the working group that defines the MAC of wired Ethernet at the physical layer and the data link layer. This is typically a LAN technology with some wide area network applications. Physical connections are typically established between nodes and / or network devices, such as infrastructure network devices (hubs, switches, routers), using various types of copper or fiber optic cables. IEEE 802.3 is a technology that supports the IEEE 802.1 network architecture. As is known in the relevant technical terminology, IEEE 802.11 is a working group and collection of standards for implementing wireless LAN computer communications in the 2.4, 3.6, and 5 GHz frequency bands. The base version of the IEEE 802.11-2007 standard was subsequently amended. These standards form the basis for wireless networking products under the Wi-Fi brand. IEEE 802.1 and 802.3 are incorporated by reference.

[0036] In a particular implementation, the IoT devices 106 can only transmit data generated at the IoT devices 106. By transmitting only data generated at the IoT devices 106, the IoT devices 106 can access network services of a network provided over a LAN using a multi-protocol infrastructure network device. For example, the IoT devices 106 can include beacons configured to transmit beacon signals.

[0037] In a particular implementation, the IoT devices 106 include or are otherwise coupled to a media capture device. A media capture device includes a suitable device for capturing media. Media includes applicable content that can be perceived by a human. For example, media may include a video feed. An example of a media capture device includes a camera. A camera serving as one of the IoT devices 106 may be connected to a multi-protocol infrastructure network device via a wired or wireless connection. For example, a camera serving as one of the IoT devices 106 may be coupled to a multi-protocol infrastructure network device via a Wi-Fi-based wireless connection.In another example, a camera serving as one of the IoT devices 106 may be coupled to a multi-protocol infrastructure network device via a wired connection established via a USB connection between the camera and the multi-protocol infrastructure network device.

[0038] With reference to the Fig. In the example system illustrated in Figure 1, the LAN 108 is intended to represent a network that provides wired or wireless communication channels over which IoT devices can access network services of a network, e.g., an enterprise or consumer network. The LAN 108 may be configured and maintained according to applicable wired or wireless communication protocols or standards, e.g., Wi-Fi, ZigBee®, Bluetooth®, applicable low-power communication standards, or applicable wired or wireless PAN communication standards. The LAN 108 may be used to transmit signals to and from IoT devices as part of the devices that access network services over a multi-protocol infrastructure network device.

[0039] In the example of Fig. 1, LAN 108 includes a computer-readable medium, but is distinguished from computer-readable medium 102 to illustrate the branching of the medium by multi-protocol infrastructure network devices 104. While LAN 108 represents only a single LAN, it may represent a variety of different LANs, possibly configured and maintained according to different standards. For example, LAN 108 may include a ZigBee®-based WLAN operating concurrently with a Wi-Fi-based WLAN.

[0040] In a particular implementation, LAN 108 is used to transfer IoT device data from an IoT device to a suitable device to enable the IoT device to access network services, such as the multi-protocol infrastructure network devices described in this paper. IoT device data includes applicable data generated at an IoT device. For example, IoT device data may include images or a feed of captured images that form a video generated at a camera. Further, for example, LAN 108 may be formed via a USB connection and used to transfer the captured images or the feed of captured images from the camera to a suitable device to enable the camera to access network services, such as the multi-protocol infrastructure network devices described in this paper.

[0041] With reference to the Fig. In the example system illustrated in Figure 1, the fog networking-based IoT device analysis system 110 is intended to represent a system configured to analyze IoT device data through fog networking. When analyzing IoT device data through fog networking, the fog networking-based IoT device analysis system 110 generates IoT device analysis data based on an analysis of the IoT device data. The IoT device analysis data generated by the fog networking-based IoT device analysis system 110 includes applicable data generated as part of analyzing the IoT device data generated by IoT devices. For example, IoT device analysis data may include portions of IoT device data, preprocessed IoT device data generated on IoT devices, patterns recognized from IoT device data generated on IoT devices, and applicable conclusions or inferences reached by analyzing IoT device data generated on IoT devices.In another example, IoT device analytics data may include movement patterns of people in a store determined from video feeds received from one or more cameras in the store.

[0042] The fog networking-based IoT device analysis system 110 is fog networking-based in that it analyzes IoT device data through fog networking using suitable devices that are part of a local network infrastructure consisting of one or a combination of a LAN, a WLAN, a consumer network, and an enterprise network, hereinafter referred to as local infrastructure network devices. For example, the IoT device analysis system 110 may use a network device acting as an access point or router in a LAN to analyze IoT device data through fog networking. When analyzing IoT device data using local infrastructure network devices, the fog networking-based IoT device analysis system 110 may be implemented at least partially on one or a combination of local infrastructure network devices.For example, the fog networking-based IoT device analysis system 110 can be implemented on a suitable edge device to enable IoT devices to access network services over a LAN, such as the multi-protocol infrastructure network devices described in this paper. Furthermore, when analyzing IoT device data with local infrastructure network devices, the fog networking-based IoT device analysis system 110 can analyze the IoT device data to generate IoT device analysis data on the local infrastructure network devices. For example, the fog networking-based IoT device analysis system 110 can analyze IoT device data to generate IoT device analysis data on a variety of applicable edge devices to enable IoT devices to access network services over a LAN, such as the multi-protocol infrastructure network devices described in this paper.

[0043] In a particular implementation, the fog networking-based IoT device analytics system 110 analyzes the IoT device data generated and received by a camera. For example, the fog networking-based IoT device analytics system 110 may analyze a video feed from a camera in a store to determine the movement patterns of consumers in the store and then generate IoT device analytics data indicating the determined movement patterns. The fog networking-based IoT device analytics system 110 may analyze IoT device data generated and received by a camera via fog networking. When analyzing IoT device data generated and received by a camera via fog networking, the fog networking-based IoT device analytics system 110 may analyze the IoT device data on local infrastructure network devices.For example, the fog networking-based IoT device analysis system 110 can analyze a video feed captured and received by a camera on local infrastructure network devices. Furthermore, the fog networking-based IoT device analysis system 110 can analyze the video feed captured and received by the camera on a suitable edge device that is part of a local network infrastructure and is configured to provide the camera with access to network services, such as the multi-protocol infrastructure network devices described in this paper.

[0044] In a particular implementation, the fog networking-based IoT device analytics system 110 is fog networking-based in that it provides generated IoT device analytics data to an applicable remote system, e.g., a cloud-based system, over a WAN as part of analyzing IoT device data through fog networking. A remote system, as used in this paper, includes an applicable system or device outside of a LAN. For example, the fog networking-based IoT device analytics system 110 may generate IoT device analytics data from received IoT device data and then forward the IoT device analytics data to a remote server over a WAN. Furthermore, in the example, a user may access the IoT device analytics data on the remote server over the WAN or the Internet.

[0045] In a particular implementation, the fog networking-based IoT device analysis system 110 is fog networking-based in that it selectively provides certain portions of the received IoT device data to an applicable remote system, e.g., a cloud-based system, over a WAN as part of analyzing IoT device data through fog networking. For example, the fog networking-based IoT device analysis system 110 can selectively choose whether to provide certain portions of a received video transmission to a remote server over a WAN. Furthermore, a user in the example can access the specific portions of the received video input on the remote server over the WAN or the Internet.

[0046] In a particular implementation, the fog networking-based IoT device analytics system 110 is configured to transmit IoT device analytics data and / or selected portions of the received IoT device data to a remote system according to an applicable data transport or messaging protocol. In particular, the fog networking-based IoT device analytics system 110 may transmit IoT device analytics data and / or selected portions of the received IoT device data using an applicable lightweight messaging protocol. For example, the fog networking-based IoT device analytics system 110 may transmit IoT device analytics data and / or selected portions of the received IoT device data according to an MQ telemetry transport protocol (hereinafter referred to as "MQTT"). It should be noted that the "MQ" of "MQTT" originates from IBM's MQ Series Message Queuing product line, but queuing may not be supported as a standard feature in all situations.The MQTT International Organization for Standardization (“ISO”) standard (ISO / IEC PRF 20922) is incorporated by reference.

[0047] In a particular implementation, when analyzing IoT device data via fog networking, the fog networking-based IoT device analytics system 110 is configured to selectively refrain from providing all or certain portions of the IoT device data to an applicable remote system over a WAN. For example, the fog networking-based IoT device analytics system 110 may selectively provide portions of the raw video captured by a camera along with IoT device analytics data generated from the entire raw video captured by the camera.By selectively omitting all or certain portions of the IoT device data from being made available to a remote system over a WAN as part of analyzing the IoT device data through fog networking, the fog networking-based IoT device analytics system 110 can reduce traffic through an applicable network that is or may be used to transmit the IoT device data to the remote system. For example, the fog networking-based IoT device analytics system 110 can reduce data traffic over an enterprise-wide WAN network used to communicate with a remote system. This, in turn, can reduce the latency and amount of consumer bandwidth or consumed network resources of a network or combination of networks, such as a LAN, a WLAN, a consumer network, or an enterprise network.

[0048] In a particular implementation, the fog networking-based IoT device analytics system 110 is configured to manage the selective local storage of received IoT device data and / or generated IoT device analytics data. The fog networking-based IoT device analytics system 110 may manage the selective local storage of received IoT device data and / or generated IoT device analytics data as part of the analysis of IoT device data through fog networking. Local data storage, as used in this paper, involves the local storage of data on network devices of the local infrastructure. For example, the fog networking-based IoT device analytics system 110 may manage the selective local storage of received IoT device data and / or generated IoT device analytics data on local infrastructure network devices.The fog networking-based IoT device analytics system 110 may manage the selective local storage of received IoT device data and / or generated IoT device analytics data as part of load balancing between local infrastructure network devices when analyzing IoT device data through fog networking.

[0049] In a particular implementation, when managing the local storage of received IoT device data and / or generated IoT device analytics data, the fog networking-based IoT device analytics system 110 is configured to select specific data for local storage. For example, the fog networking-based IoT device analytics system 110 may choose whether to store IoT device data locally if it has been used to generate IoT device analytics data or useful IoT device analytics data. Furthermore, the fog networking-based IoT device analytics system 110 may cause selected IoT device data and generated IoT device analytics data to actually be stored locally on devices within a network infrastructure to manage the local storage of data.When selecting IoT device data and / or IoT device analytics data to be stored locally, the fog networking-based IoT device analytics system 110 may select data to be stored locally based on the available storage capacity on the device within a network. For example, the fog networking-based IoT device analytics system 110 may select IoT device data and IoT device analytics data to be stored locally based on storage capacity on network devices of the local infrastructure.

[0050] In a particular implementation, when managing the local storage of received IoT device data and / or generated IoT device analytics data, the fog networking-based IoT device analytics system 110 is configured to select specific data to be removed from the local storage. For example, the fog networking-based IoT device analytics system 110 may delete locally stored IoT device data if it has not been used to generate IoT device analytics data or useful IoT device analytics data. Furthermore, the fog networking-based IoT device analytics system 110 may cause selected IoT device data and generated IoT device analytics data to be actually removed from the local storage of devices within a network infrastructure to manage the local storage of data.When selecting data from one or both IoT device data and IoT device analytics data to be deleted from local storage, the fog networking-based IoT device analytics system 110 may select data to be removed from local storage based on the available storage capacity on the device within a network. For example, the fog networking-based IoT device analytics system 110 may select IoT device data and IoT device analytics data to be deleted from local storage based on the storage capacity of the local infrastructure network devices.

[0051] In a particular implementation, the fog networking-based IoT device analysis system 110 is configured to perform load balancing across applicable devices used in analyzing IoT device data through fog networking. Specifically, the fog networking-based IoT device analysis system 110 may perform local balancing across local infrastructure network devices used in analyzing IoT device data through fog networking. The fog networking-based IoT device analysis system 110 may perform load balancing across local infrastructure network devices to analyze IoT device data through fog networking.For example, the fog networking-based IoT device analytics system 110 may cause IoT device data and / or IoT device analytics data received at a first local infrastructure network device to be sent from the first local infrastructure network device to a second local infrastructure network device for analyzing the IoT device data at the second local infrastructure network device. Furthermore, the fog networking-based IoT device analytics system may use the second local infrastructure network device or cause the second local infrastructure network device to analyze the IoT device data to generate IoT device analytics data. In the example, the fog networking-based IoT device analytics system may use the second local infrastructure network device or cause the second local infrastructure network device to provide either the IoT device analytics data or portions of the IoT device data to a remote system as part of analyzing IoT device data through fog networking.

[0052] In a specific implementation, the fog networking-based IoT device analysis system 110 is configured to perform load balancing for the purpose of analyzing IoT device data through fog networking based on factors related to providing network service access to IoT devices via local infrastructure network devices. Factors related to providing network service access to IoT devices via local infrastructure network devices include applicable factors related to providing network service access to IoT devices via local infrastructure network devices.Examples of factors related to providing network service access to IoT devices via local infrastructure network devices include an amount of available computing resources on a local infrastructure network device, an amount of local storage available on a local infrastructure network device, a number of IoT devices that provide a local infrastructure network device with access to network services, an amount of computing resources used on a local infrastructure network device to provide IoT devices with access to network services, and an amount of computing resources used on other local infrastructure network devices to provide IoT devices with access to network services.For example, if a local infrastructure network device lacks the available computing resources to analyze IoT device data through fog networking, then the fog networking-based IoT device analysis system 110 may cause the local infrastructure network device to send the IoT device data to another local infrastructure network device to analyze the IoT device data on the other device through fog networking.

[0053] In a specific implementation, the fog networking-based IoT device analysis system 110 is configured to perform load balancing for the purpose of analyzing IoT device data through fog networking based on factors related to analyzing IoT device data. Factors related to analyzing IoT device data include applicable factors related to analyzing IoT device data through fog networking. Examples of factors related to analyzing IoT device data include whether a local infrastructure network device is assigned as the central IoT device data analysis device, the use of a lot of computing resources at a local infrastructure network device when analyzing IoT device data through fog networking, and the use of computing resources by other local infrastructure network devices when analyzing IoT device data through fog networking.For example, if a specific local infrastructure network device is designated as a central IoT data analysis device for the purpose of analyzing IoT device data, the fog networking-based IoT device analysis system 110 may cause other local infrastructure network devices to provide receive IoT device data to the specific local infrastructure network device for analyzing the data through fog networking.

[0054] In a particular implementation, the fog networking-based IoT device analytics system 110 is configured to perform load balancing across local infrastructure network devices to manage the local storage of IoT device data and / or IoT device analytics data on the local infrastructure network devices. For example, the fog networking-based IoT device analytics system 110 may cause a first local infrastructure network device to send received IoT device data to a second local infrastructure network device for storage on the second local infrastructure network device. The fog networking-based IoT device analytics system 110 may perform load balancing across local infrastructure network devices to manage the local storage of data based on the available storage amounts on the local infrastructure network devices.For example, if a first local infrastructure network device receives IoT device data and does not have the capacity to store the IoT device data locally, then the fog networking-based IoT device analytics system 110 may cause the first local infrastructure network device to send the IoT device data to a second local infrastructure network device to store the IoT device data locally on the second local infrastructure network device.

[0055] In a particular implementation, the fog networking-based IoT device analysis system 110 is configured to configure an IoT device over a LAN that connects the IoT device to an applicable local infrastructure network device, such as the multi-protocol infrastructure network devices described in this paper. When configuring an IoT device, the fog networking-based IoT device analysis system 110 may configure an IoT device to not perform preprocessing of the IoT device data generated by the IoT device. Furthermore, when configuring an IoT device, the fog networking-based IoT device analysis system 110 may configure an IoT device to forgo analyzing IoT device data at the IoT device, thereby reducing the amount of data the IoT device must send and reducing the amount of computing resources the IoT device must use during normal operation.

[0056] In an example of the operation of the Fig. 1, the IoT devices 106 generate IoT device data and make the IoT device data available to the multi-protocol infrastructure network devices 104 via the LAN 108. In the operating example of the system shown in Fig. 1, the IoT devices 106 provide the IoT device data to the multi-protocol infrastructure network devices 104 either via a wired or wireless connection formed as at least a portion of the LAN 108. Furthermore, the fog networking-based IoT device analysis system 110 in the operational example of the system shown in Fig. 1, a system performs load balancing across the multi-protocol infrastructure network devices 104 to analyze the IoT device data received from the IoT devices 106 using fog networking.

[0057] In the operating example of the Fig. 1, the fog networking-based IoT device analysis system 110 analyzes the IoT device data to generate IoT device analysis data on the multi-protocol infrastructure network devices 104 by performing load balancing across the multi-protocol infrastructure network devices 104 to analyze the IoT device data received from the IoT devices 106 using fog networking. In addition, the fog networking-based IoT device analysis system 110 in the operating example of the system shown in Fig. 1, the IoT device analytics data generated at the multi-protocol infrastructure network devices is made available to a remote system as part of the analysis of the IoT device data through fog networking.

[0058] Fig. 2 illustrates a flowchart 200 of an example method for analyzing IoT device data at local infrastructure network devices using fog networking. The flowchart 200 begins at module 202, where the IoT device data generated on an IoT device is received at a local infrastructure network device of a plurality of local infrastructure network devices over a LAN. A local infrastructure network device of a plurality of local infrastructure network devices may include an edge device or a forwarding device, forming a local network infrastructure from a network or combination of networks, such as a LAN, a WLAN, a consumer network, and an enterprise network.For example, a local infrastructure network device configured to receive IoT device data created at an IoT device may include a suitable device for providing the IoT device access to network services, such as the multi-protocol infrastructure network devices described in this paper. A local infrastructure network device configured to receive IoT device data over one or both wired or wireless connections used to provide an IoT device access to network services.For example, data from IoT devices can be received at a local infrastructure network device via a Wi-Fi connection established between an IoT device and the local infrastructure network device, with the local infrastructure network device providing network service access to the IoT device acting as a station.

[0059] Flowchart 200 continues with module 204, where load balancing is performed across the plurality of local infrastructure network devices to analyze the IoT device data using fog networking using at least one of the plurality of local infrastructure network devices. When performing load balancing for the purpose of using fog networking to analyze the IoT device data, the IoT device data may be maintained on the local infrastructure network device that receives the IoT device data, where the IoT device data may then be analyzed using fog networking.Alternatively, when performing load balancing, the IoT device data may be forwarded to another local infrastructure network device among the plurality of local infrastructure network devices for the purpose of using fog networking to analyze the IoT device data, and then analyzed on the other local infrastructure network device using fog networking. An applicable system for managing IoT device analytics using fog networking, such as the fog networking-based IoT device analytics systems described in this paper, may perform load balancing across the plurality of local infrastructure network devices for the purpose of using fog networking to analyze the IoT device data.

[0060] Flowchart 200 continues with module 206, where the IoT device data is analyzed locally at at least one of a plurality of local infrastructure network devices to analyze the IoT device data using fog networking. An applicable system for managing IoT device analytics using fog networking, such as the fog networking-based IoT device analytics systems described in this paper, may analyze the IoT device data locally at at least one of a plurality of local infrastructure network devices. Analyzing the IoT device data for the purpose of fog networking to analyze the IoT device data generates IoT device analytics data. For example, if the IoT device data is a captured video feed of a view within a store, IoT device analytics data may be generated indicating customer movement patterns within the store as determined from the IoT device data.

[0061] Flowchart 200 continues with module 208, where the IoT device analytics data from at least one of the plurality of local infrastructure network devices is provided to a remote system as part of analyzing the IoT device data through fog networking. For example, the IoT device analytics data may be provided to a remote server implemented in the cloud, allowing a user to access the IoT device analytics data. An applicable system for managing IoT device analytics using fog networking, such as the fog networking-based IoT device analytics systems described in this paper, may provide the IoT device analytics data from at least one of the plurality of local infrastructure network devices to a remote system as part of analyzing the IoT device data through fog networking.Furthermore, portions of the IoT device data, along with the IoT device analytics data, may be provided by at least one of the plurality of local infrastructure network devices to a remote system as part of the analysis of the IoT device data through fog networking. When providing the IoT device analytics data, and possibly only portions of the IoT device data, without transmitting all IoT device data to the remote system, the utilization of network resources across an applicable network or combination of networks, e.g., a LAN, a WLAN, a consumer network, and an enterprise network, is reduced, resulting in lower latency in providing IoT device access to network services.

[0062] Fig. 3 illustrates a diagram 300 of an example system for load balancing across local infrastructure network devices for the purpose of analyzing IoT device data through fog networking. Fig. 3 includes a first local infrastructure network device 304, a second local infrastructure network device 306, and a fog networking-based IoT device analysis load balancing system 308. In the Fig. 3, the first local infrastructure network device 304 and the second local infrastructure network device 306 are coupled to the fog networking-based IoT device analytics load balancing system 308 via the computer-readable medium 302. By coupling the first local infrastructure network device 304 and the second local infrastructure network device 306 via the computer-readable medium 302, all or part of the fog networking-based IoT device analytics load balancing system 308 can be implemented at either the first local infrastructure network device 304 and / or the second local infrastructure network device 306. Furthermore, the fog networking-based IoT device analytics load balancing system 308 can be coupled, in whole or in part, to a device remote from and coupled to the first and second local infrastructure network devices 304 and 306 via the computer-readable medium 302.

[0063] In the Fig. 3, the first local infrastructure network device 304 and the second local infrastructure network device 306 are coupled to each other via the computer-readable medium 302. When coupling the first and second local infrastructure network devices 304 and 306, the computer-readable medium 302 may include one or both wired or wireless connections. For example, the computer-readable medium 302 may include a wired LAN backend connecting the first local infrastructure network device 304 to the second local infrastructure network device 306. In another example, the computer-readable medium 302 may include a wireless connection directly connecting the first local infrastructure network device 304 to the second local infrastructure network device 306, which acts as a mesh node in a wireless mesh network of local infrastructure network devices.

[0064] With reference to the Fig. 3, the first local infrastructure network device 304 and the second local infrastructure network device 306 are intended to represent devices that function as local infrastructure network devices. The first local infrastructure network device 304 and the second local infrastructure network device 306 may be an edge device and / or a forwarding device in a network or combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network. For example, the first local infrastructure network device 304 and / or the second local infrastructure network device 306 may be applicable edge devices for forming a LAN with an IoT device and enabling the IoT device to access network services over the LAN, such as the multi-protocol infrastructure network devices described in this paper.In another example, the first local infrastructure network device 304 and / or the second local infrastructure network device 306 may be forwarding devices configured to forward data to and from edge devices by providing network service access to IoT devices via the edge devices.

[0065] In a specific implementation, the first local infrastructure network device 304 and the second local infrastructure network device 306 are capable of receiving IoT device data generated by an IoT device for the purpose of analyzing the IoT device data through fog networking. The first local infrastructure network device 304 and the second local infrastructure network device 306 may receive IoT device data generated by an IoT device as part of the IoT device accessing network services via a LAN via an applicable device, such as the multi-protocol infrastructure network devices described in this paper.For example, either or both the first local infrastructure network device 304 and the second local infrastructure network device 306 may function as multi-protocol infrastructure network devices by enabling IoT devices to access network services over a LAN and subsequently receiving IoT device data from the IoT devices as part of the IoT devices' access to network services.

[0066] In a specific implementation, the first local infrastructure network device 304 and the second local infrastructure network device 306 are operable to receive IoT device data from local infrastructure network devices. For example, the first local infrastructure network device 304 may act as a forwarding device within a LAN and receive IoT device data from an edge device of the LAN, enabling an IoT device to access network services. In another example, the first local infrastructure network device 304 may transmit IoT device data to the second local infrastructure network device 306, and vice versa.

[0067] In a specific implementation, the first local infrastructure network device 304 and the second local infrastructure network device 306 function to analyze IoT device data and generate IoT device analytics data. The first local infrastructure network device 304 and the second local infrastructure network device 306 may generate IoT device analytics data from IoT device data as part of analyzing the IoT device data through fog networking. IoT device data analyzed at the first local infrastructure network device 304 and the second local infrastructure network device 306 may be received and stored locally at the respective devices 304 and 306. For example, the second local infrastructure network device 306 may receive IoT device data from the first local infrastructure network device 304 and then analyze the IoT device data to generate IoT device analytics data at the second local infrastructure network device 306.

[0068] In a specific implementation, the first local infrastructure network device 304 and the second local infrastructure network device 306 are capable of receiving IoT device analytics data as part of analyzing IoT device data through fog networking. The first local infrastructure network device 304 and the second local infrastructure network device 306 may receive IoT device analytics data from local infrastructure network devices. For example, the first local infrastructure network device 304 may generate IoT device analytics data and subsequently provide the IoT device analytics data to the second local infrastructure network device 306. Furthermore, the second local infrastructure network device 306 may store the received IoT device analytics data locally, e.g., at the second local infrastructure network device 306, and / or provide the IoT device analytics data to a remote system, e.g., in the cloud, as part of analyzing IoT device data through fog networking.

[0069] In a particular implementation, IoT device data and / or IoT device data are transmitted to and from the first and second local infrastructure network devices 304 and 306 to enable load balancing across local infrastructure network devices when analyzing IoT device data through fog networking. For example, the first local infrastructure network device 304 may send IoT device data it receives to the second local infrastructure network device 306 if it determines that the second local infrastructure network device 306 should analyze the IoT device data for the purpose of load balancing across local infrastructure network devices.In another example, the second local infrastructure network device 306 may transmit IoT device data that it receives to the first local infrastructure network device 304 if it determines that the first local infrastructure network device has storage space to store the IoT device data for the purpose of load balancing between local infrastructure network devices.

[0070] In a particular implementation, IoT device data and / or IoT device analytics data is transmitted to and from the first and second local infrastructure network devices 304 and 306 for the purpose of load balancing based on access to the IoT device network service over a LAN. In particular, data may be transmitted to and from the first and second local infrastructure network devices 304 and 306 based on factors related to providing network service access to IoT devices via local infrastructure devices. For example, if the first local infrastructure network device 304 consumes 80% of its computing resources to provide IoT devices with access to network services, then the first local infrastructure network device 304 may transmit IoT device data to the second local infrastructure network device 306 for the purpose of load balancing across local infrastructure network devices.Furthermore, the second local infrastructure network device 306 may analyze the received IoT device data to generate IoT device analysis data as part of load balancing between local infrastructure network devices for analyzing IoT device data through fog networking. In another example, if the first local infrastructure network device 304 provides network service access to a threshold number of IoT devices, then the first local infrastructure network device 304 may provide IoT device data to the second local infrastructure network device 306 as part of load balancing across local infrastructure network devices.

[0071] In a particular implementation, IoT device data and / or IoT device data are transmitted to and from the first and second local infrastructure network devices 304 and 306 to perform load balancing based on the functioning of local infrastructure network devices for analyzing IoT device data through fog networking. In particular, data may be transmitted to and from the first and second local infrastructure network devices 304 and 306 based on factors related to the analysis of IoT device data. For example, if the second local infrastructure network device 306 is assigned as a central IoT infrastructure network device, then the first local infrastructure network device 304 may provide IoT device data to the second local infrastructure network device 306.Furthermore, the second local infrastructure network device 306 may subsequently function as a central IoT device data analyzer by analyzing the IoT device data received from the first local infrastructure network device 304.

[0072] In the Fig. In the example system illustrated in Figure 3, the first local infrastructure network device 304 includes an IoT device data transmission engine 310, an IoT device data reception engine 312, and an IoT device data storage 314. The IoT device data transmission engine 310, the IoT device data reception engine 312, and the IoT device data storage 314 may be implemented as part of an applicable system for managing the analysis of IoT device data through fog networking, such as the fog networking-based IoT device analytics systems described in this paper. The IoT device data transmission engine 310 is intended to represent a machine configured to transmit IoT device data and IoT device analytics data from the first local infrastructure network device 304.The IoT device data transfer engine 310 may transfer either or both IoT device data and IoT device analytics data from the first local infrastructure network device 304 to other local infrastructure network devices in a network or a combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network. For example, the IoT device data transfer engine 310 may transfer IoT device data and / or IoT device analytics data from the first local infrastructure network device 304 to a neighboring local infrastructure network device via a LAN backend. Furthermore, the IoT device data transfer engine 310 may transfer IoT device data and / or IoT device analytics data across local infrastructure network devices as part of load balancing to analyze IoT device data through fog networking.

[0073] The IoT device data receiving engine 312 is intended to represent a machine configured to receive IoT device data and IoT device analytics data on the first local infrastructure network device 304. The IoT device data receiving engine 312 may receive IoT device data and / or IoT device analytics data on the first local infrastructure network device 304 from other local infrastructure network devices in a network or a combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network. For example, the IoT device data receiving engine 312 may receive IoT device data and / or IoT device analytics data on the first local infrastructure network device 304 from a neighboring local infrastructure network device via a LAN backend.Additionally, the IoT device data receiving engine 312 may receive IoT device data and / or IoT device analytics data as part of load balancing via local infrastructure network devices to analyze IoT device data through fog networking.

[0074] The IoT device data store 314 is intended to represent a data store configured to store IoT device data and IoT device analytics data on the first local infrastructure network device 304. IoT device data and IoT device analytics data stored in the IoT device data store 314 on the first local infrastructure network device 304 can be received either from another local infrastructure network device and / or directly from an IoT device via a LAN. Additionally, IoT device data and IoT device analytics data stored in the IoT device data store 314 on the first local infrastructure network device 304 can be received via load balancing across local infrastructure network devices to analyze IoT device data through fog networking.IoT device data stored in the IoT device data store 314 may be analyzed on the first local infrastructure network device 304 to generate IoT device analytics data through fog networking on the first local infrastructure network device 304.

[0075] In the Fig. In the system example illustrated in Figure 3, the second local infrastructure network device 306 includes an IoT device data receiving engine 316, an IoT device data transmitting engine 318, and an IoT device data storage 320. The IoT device data receiving engine 316, the IoT device data transmitting engine 318, and the IoT device data storage 320 may be implemented as part of an applicable system for managing the analysis of IoT device data through fog networking, such as the fog networking-based IoT device analytics systems described in this paper. The IoT device data receiving engine 316 is intended to represent a machine configured to receive IoT device data and IoT device analytics data at the second local infrastructure network device 306.The IoT device data reception engine 316 may receive IoT device data and / or IoT device analytics data at the second local infrastructure network device 306 from other local infrastructure network devices in a network or a combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network. For example, the IoT device data reception engine 316 may receive IoT device data and / or IoT device analytics data at the second local infrastructure network device 306 from a neighboring local infrastructure network device via a LAN backend. Furthermore, the IoT device data reception engine 316 may receive IoT device data and / or IoT device analytics data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0076] The IoT device data transfer engine 318 is intended to represent a machine configured to transfer IoT device data and IoT device analytics data from the second local infrastructure network device 306. The IoT device data transfer engine 318 may transfer IoT device data and / or IoT device analytics data from the second local infrastructure network device 306 to other local infrastructure network devices in a network or a combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network. For example, the IoT device data transfer engine 318 may transfer IoT device data and / or IoT device analytics data from the second local infrastructure network device 306 to a neighboring local infrastructure network device via a LAN backend.Additionally, the IoT device data transmission engine 318 may transmit IoT device data and / or IoT device analytics data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0077] The IoT device data store 320 is intended to represent a data store configured to store IoT device data and IoT device analytics data on the second local infrastructure network device 306. IoT device data and IoT device analytics data stored in the IoT device data store 320 on the second local infrastructure network device 306 can be received from other local infrastructure network devices and / or directly from an IoT device via a LAN. Additionally, IoT device data and IoT device analytics data stored in the IoT device data store 320 on the second local infrastructure network device 306 can be received via load balancing across local infrastructure network devices to analyze IoT device data through fog networking.IoT device data stored in the IoT device data store 320 may be analyzed on the second local infrastructure network device 306 to generate IoT device analytics data through fog networking on the second local infrastructure network device 306.

[0078] With reference to the Fig. 3, the fog networking-based IoT device analytics load balancing system 308 is intended to represent a system configured to manage load balancing across local infrastructure network devices for the purpose of analyzing IoT device data through fog networking. When managing load balancing across local infrastructure network devices, the fog networking-based IoT device analytics load balancing system 308 may cause network devices to transmit IoT device data and / or IoT device analytics data to each other. Furthermore, when managing load balancing across local infrastructure network devices, the fog networking-based IoT device analytics load balancing system 308 may cause or otherwise direct an infrastructure network device to analyze IoT device data through fog networking.

[0079] In a specific implementation, the fog networking-based IoT device analytics load balancing system 308 is implemented as part of an applicable system for managing the analysis of IoT device data through fog networking, such as the fog networking-based IoT device analytics system described in this paper. As part of an applicable system for managing the analysis of IoT device data through fog networking, the fog networking-based IoT device analytics load balancing system 308 can be implemented on one or across a plurality of local infrastructure network devices. For example, the fog networking-based IoT device analytics load balancing system 308 can be implemented across edge devices of a LAN.

[0080] In a particular implementation, the fog networking-based IoT device analytics load balancing system 308 is configured to manage load balancing across local infrastructure network devices based on access to IoT device network services over a LAN. Specifically, the fog networking-based IoT device analytics load balancing system 308 may manage load balancing across local infrastructure network devices based on the factor resulting from IoT device network service access provision over the local infrastructure network devices.For example, if a local infrastructure network device consumes 80% of its computing resources for IoT device access to network services, the fog networking-based IoT device analytics load balancing system 308 may cause the local infrastructure network device to send IoT device data to another local infrastructure network device for load balancing across local infrastructure network devices. Furthermore, the fog networking IoT device analytics load balancing system 308 may instruct or otherwise cause the other local infrastructure network device to analyze the received IoT device data to generate IoT device analytics data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0081] In a specific implementation, the fog networking IoT device analytics load balancing system 308 is configured to manage load balancing across local infrastructure network devices based on the functionality of local infrastructure network devices to analyze IoT device data through fog networking. Specifically, the fog networking-based IoT device analytics system 308 may manage load balancing across local infrastructure network devices based on factors related to analyzing IoT device data. For example, if a local infrastructure network device consumes 80% of its computing resources analyzing IoT device data, the fog networking-based IoT device analytics load balancing system 308 may cause the local infrastructure network device to send IoT device data to another local infrastructure network device to enable load balancing across local infrastructure network devices.Furthermore, the fog networking IoT device analytics load balancing system 308 may instruct or otherwise cause the other local infrastructure network device to analyze the received IoT device data to generate IoT device analytics data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0082] In the Fig. In the example system illustrated in Figure 3, the fog networking IoT device analytics load balancing system 308 includes an IoT device network service access factor identification engine 322, an IoT device analytics factor identification engine 324, and an IoT device analytics load balancing engine 326. The IoT device network service access factor identification engine 322 is intended to represent an engine configured to determine factors related to providing network service access to IoT devices via local infrastructure network devices. For example, the IoT device network service access factor identification engine 322 may determine an amount of computing resources used by a local infrastructure network device at a given time to provide IoT devices with access to network services over a LAN.In another example, the IoT device network service access factor identification engine 322 may determine an amount of local storage available on a local infrastructure network device. The IoT device network service access factor identification engine 322 may determine factors related to providing IoT device network service access via local infrastructure network devices to any number of applicable infrastructure network devices across a network or combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network. For example, the IoT device network service access factor identification engine 322 may determine factors related to providing IoT device network service access across all multi-protocol infrastructure network devices that are part of a LAN.

[0083] The IoT device analysis factor identification engine 324 is intended to represent an engine configured to determine factors related to analyzing IoT device data. For example, the IoT device analysis factor identification engine 324 may determine an amount of computing resources a local infrastructure network device is using at a given time when analyzing IoT device data through fog networking. In another example, the IoT device analysis factor identification engine 324 may determine whether a local infrastructure network device is assigned as a central IoT device data analyzer. The IoT device analysis factor identification engine 324 may determine factors related to analyzing IoT device data for any number of applicable infrastructure network devices across a network or combination of networks, such as a WLAN, a LAN, a consumer network, and an enterprise network.For example, the IoT Device Analysis Factors Identification Engine 324 may determine factors related to the analysis of IoT device data across all multi-protocol infrastructure network devices that are part of a LAN.

[0084] The IoT device analytics load balancing engine 326 is intended to represent a machine configured to perform load balancing across local infrastructure network devices for the purpose of analyzing IoT device data through fog networking. When performing load balancing between local infrastructure network devices for the purpose of analyzing IoT device data through fog networking, the IoT device analytics load balancing engine 326 may cause local infrastructure network devices to transfer either or both IoT device data and IoT device analytics data to each other. Furthermore, when performing load balancing between local infrastructure network devices for the purpose of analyzing IoT device data through fog networking, the IoT device analytics load balancing engine 326 may cause a local infrastructure network group to analyze IoT device data on the device to generate IoT device analytics data using fog networking.

[0085] In a specific implementation, the IoT device analytics load balancing engine 326 is configured to manage load balancing across local infrastructure network devices based on factors related to the provision of network service access to IoT devices via the local infrastructure network devices. For example, if a local infrastructure network device only has 20% of its local memory available, the IoT device analytics load balancing engine 326 may cause the local infrastructure network device to send IoT device data to another local infrastructure network device to facilitate load balancing across local infrastructure network devices.Furthermore, the IoT device analytics load balancing engine 326 may instruct or otherwise cause the other local infrastructure network device to analyze the received IoT device data to generate IoT device analytics data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0086] In a specific implementation, the IoT device analytics load balancing engine 326 is configured to manage load balancing across local infrastructure network devices based on factors related to analyzing IoT device data. For example, if a local infrastructure network device consumes 80% of its computing resources analyzing IoT device data, the IoT device analytics load balancing engine 326 may cause the local infrastructure network device to send IoT device data to another local infrastructure network device to facilitate load balancing across local infrastructure network devices.Furthermore, the IoT device analytics load balancing engine 326 may instruct or otherwise cause the other local infrastructure network device to analyze the received IoT device data to generate IoT device analytics data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0087] In an operational example of the Fig. 3, the IoT device data receiving engine 312 on the first local infrastructure network device 304 receives IoT device data from an IoT device that is generated by an IoT device as part of the IoT device's access to network services. In the example of operation of the Fig. 3, the IoT device network service access factor identification engine 322 determines factors related to the provision of network service access of the IoT devices to the first local infrastructure network device 304 and the second local infrastructure network device 306. Furthermore, in the operational example of the system illustrated in Fig. 3 illustrates factors relating to the analysis of IoT device data at the first local infrastructure network device 304 and the second local infrastructure network device 306. In the example of the operation of the system shown in Fig. 3, the IoT device analysis load balancing engine 326 manages the load balancing of the analysis of the IoT device data between the first local infrastructure network device 304 and the second local infrastructure network device when determining the IoT device analysis data from the IoT device data through fog networking. Additionally, the IoT device analysis load balancing engine 326 uses Fig. 3 illustrates the factors related to analyzing IoT device data and the factors related to providing IoT device network service access to manage load balancing between the first local infrastructure network device 304 and the second local infrastructure network device 306.

[0088] Fig. 4 illustrates a flowchart 400 of an example method for load balancing between local infrastructure network devices for the purpose of analyzing IoT device data through fog networking. The flowchart 400 begins at module 402, wherein IoT device data generated at an IoT device is received over a LAN at a local infrastructure network device having a plurality of local infrastructure network devices. An applicable engine for receiving IoT device data at a local infrastructure network device, such as the IoT device data receiving engines, may receive the IoT device at one of a plurality of local infrastructure network devices. A local infrastructure network device of a plurality of local infrastructure network devices may receive IoT device data over one or both of the wired or wireless connections used to provide an IoT device access to network services.For example, data from IoT devices can be received at a local infrastructure network device via a Wi-Fi connection established between an IoT device and the local infrastructure network device, with the local infrastructure network device providing network service access to the IoT device acting as a station.

[0089] Flowchart 400 continues with module 404, where factors related to providing IoT device network service access across the plurality of local infrastructure network devices are determined. An applicable engine for determining factors related to providing IoT device network service access, such as the IoT device identification engines described in this paper, may determine factors related to providing IoT device network service access across the plurality of local infrastructure network devices. For example, it may determine how many computing resources any number of the plurality of local infrastructure network devices are using to provide network service access to IoT devices.

[0090] Flowchart 400 continues with module 406, where factors related to the analysis of IoT device data by the plurality of local infrastructure network devices are determined. An applicable engine for determining factors related to the analysis of IoT device data on local infrastructure network devices through fog networking, such as the IoT device analysis factor identification engines described in this paper, may determine factors related to the analysis of IoT device data by the plurality of local infrastructure network devices. For example, an amount of local storage used by a local infrastructure network device when analyzing IoT device data through fog networking may be determined.

[0091] Flowchart 400 continues with module 408, in which load balancing is managed across the plurality of local infrastructure network devices when analyzing IoT device data through fog networking based on the factors related to providing IoT device service access and / or the factors related to analyzing IoT device data. An applicable engine for managing load balancing between local infrastructure network devices when analyzing IoT device data through fog networking, such as the IoT device analysis load balancing engines described in this paper, may manage load balancing between the plurality of local infrastructure network devices when analyzing IoT device data through fog networking based on the factors related to providing IoT device network service access and / or the factors related to analyzing IoT device data.When managing load balancing across the plurality of local infrastructure network devices, the local infrastructure network devices may be instructed or otherwise caused to transmit the IoT device data and / or the IoT device analytics data among themselves. Furthermore, when managing load balancing across the plurality of local infrastructure network devices, the local infrastructure network devices may be instructed or otherwise caused to generate IoT device analytics data from the IoT device data. Furthermore, when managing load balancing across the plurality of local infrastructure network devices, the local infrastructure network devices may be instructed or otherwise caused to locally store the IoT device data and / or the IoT device analytics data.

[0092] Fig. 5 illustrates a diagram 500 of an example local IoT device analytics system 502. The local IoT device analytics system 502 is intended to represent a system configured to manage the local analysis of IoT device data on local infrastructure network devices within the context of fog networking. When managing the local analysis of IoT device data on local infrastructure network devices within the context of fog networking, the local IoT device analytics system 502 may be implemented as part of an applicable system for managing the analysis of IoT device data through fog networking, such as the fog networking-based IoT device analytics systems described in this paper. As part of an applicable system for managing the analysis of IoT device data through fog networking, the local IoT device analytics system 502 may be implemented at least partially on a local infrastructure network device.For example, the local IoT device analysis system 502 may be implemented on a suitable device to provide network service access to IoT devices, such as the multi-protocol infrastructure network devices described in this paper.

[0093] In a specific implementation, when managing local analysis of IoT device data through fog networking on local infrastructure network devices, the local IoT device analytics system 502 is configured to locally analyze IoT device data received on a local infrastructure network device to generate IoT device analytics data. For example, if a local infrastructure network device receives a video feed of a consumer in a store, the local IoT device analytics system 502 may perform facial recognition on portions of the video feed to identify the consumer. Further, the local IoT device analytics system 502 may subsequently generate IoT device analytics data indicating identification of the consumer. The local IoT device analytics system 502 may locally analyze IoT device data on a local infrastructure network device in response to receiving IoT device data.For example, the local IoT device analytics system 502 may automatically analyze IoT device data locally at a local infrastructure network device in response to receiving the IoT device data at the local infrastructure network device. Alternatively, the local IoT device analytics system 502 may analyze IoT device data locally upon receiving instructions from a suitable system for load balancing across local infrastructure network devices to analyze IoT device data through fog networking, such as the fog networking-based IoT device analytics load balancing systems described in this paper.

[0094] In a particular implementation, when managing local analytics of IoT device data through fog networking on local infrastructure network devices, the local IoT device analytics system 502 is configured to manage the routing of IoT device data and / or IoT device analytics data to a remote system. When managing the routing of IoT device data and IoT device analytics data to a remote system, the local IoT device analytics system 502 may transmit the data using a suitable data transport or messaging protocol. For example, the local IoT device analytics system 502 may use an applicable lightweight messaging protocol to transmit IoT device data and IoT device analytics data to a remote system.Furthermore, when managing the routing of IoT device data and IoT device analytics data to a remote system, the local IoT device analytics system 502 may select specific IoT device data and IoT device analytics data to be transmitted to the remote system. For example, the local IoT device analytics system 502 may select a subset of IoT device data to be transmitted to a remote system without transmitting all IoT device data to the remote system.

[0095] In a particular implementation, the local IoT device analytics system 502 is configured to route IoT device data and / or IoT device analytics data to a remote system via application-based routing. Application-based routing may include one or a combination of selecting a remote system to transmit data to, tunnels or appropriate routes to use in transmitting data, and protocols for transmitting data. For example, if a remote system is configured to process IoT device analytics data generated for a particular type of IoT device, the local IoT device analytics system 502 may forward the IoT device analytics data generated for an IoT device of the particular type to the remote system. When routing data via application-based routing, the local IoT device analytics system 502 may transmit data according to the characteristics of the IoT device's operation.IoT device operation characteristics include applicable characteristics related to the operation of an IoT device. For example, IoT device operation characteristics may include an IoT device type, a user operating an IoT device, applications used in the operation of an IoT device, and applications related to the operation of an IoT device in accessing network services.

[0096] In a specific implementation, when managing local analysis of IoT device data through fog networking on local infrastructure network devices, the local IoT device analytics system 502 is configured to manage the local storage of IoT device data and / or IoT device analytics data on a local infrastructure network device. When managing the local storage of IoT device data and IoT device analytics data on a local infrastructure network device, the local IoT device analytics system 502 may determine whether to store IoT device data and IoT device analytics data locally and then store the data locally on a local infrastructure network device.For example, the local IoT device analytics system 502 may determine that certain portions of IoT device data are stored on a local infrastructure network device and then cause the certain portion of IoT device data to be stored on the local infrastructure network device. Furthermore, by managing the local storage of IoT device data and IoT device analytics data, the local IoT device analytics system 502 may remove data from the local storage on a local infrastructure network device. For example, the local IoT device analytics system 502 may remove certain portions of IoT device data that are not used to generate IoT device analytics data from the local storage on a local infrastructure network device.The local IoT device analytics system 502 may manage the local storage of IoT device data and IoT device analytics data on a local infrastructure network device in response to instructions from an applicable system for load balancing across local infrastructure network devices when analyzing IoT device data through fog networking, such as the fog networking-based IoT device analytics load balancing systems described in this paper.

[0097] The Fig. The local IoT device analytics system 502 illustrated in Figure 5 includes an IoT device data reception engine 504, an IoT device data store 506, a local IoT device data analytics engine 508, an IoT device data store management engine 510, an IoT device analytics data store management engine 512, and a remote system routing engine 514. The IoT device data reception engine 504 is intended to represent an engine operative to receive IoT device data at a local infrastructure network device, such as the IoT device data reception engines described in this paper. The IoT device data reception engine 504 can receive IoT device data directly from an IoT device accessing network services.For example, the IoT device data reception engine 504 may be implemented on a multi-protocol infrastructure network device that provides an IoT device with access to network services and receives IoT device data from the IoT device as part of accessing the network services, where the IoT device data is generated by the IoT device. Furthermore, the IoT device data reception engine 504 may receive IoT device data from other local infrastructure network devices. For example, the IoT device data reception engine may be implemented on a forwarding device within a LAN and receive IoT device data from an edge device on the LAN that provides access to network services for an IoT device over a WLAN. The IoT device data reception engine 504 may receive IoT device data as part of load balancing via a local infrastructure network device configured to analyze IoT device data through fog networking.

[0098] The IoT device data store 506 is intended to represent a data store that stores IoT device data and / or IoT device analytics data, such as the IoT device data stores described in this paper. IoT device data stored in the IoT device data store 506 may be stored as part of the selective local storage of IoT device data on a local infrastructure network device on which the IoT device data store 506 is implemented. Furthermore, the IoT device analytics data stored in the IoT device data store 506 may be generated locally on a local infrastructure network device on which the IoT device data store 506 is implemented.

[0099] The local IoT device data analysis engine 508 is intended to represent a machine configured to locally analyze IoT device data through fog networking. The local IoT device data analysis engine 508 locally analyzes IoT device data through fog networking on a local infrastructure network device on which the local IoT device data analysis engine 508 is at least partially implemented. When locally analyzing IoT device data via fog networking on a local infrastructure network device, the local IoT device data analysis engine 508 may generate IoT device analysis data reflecting an analysis of the IoT device information. For example, the local IoT device data analysis engine 508 may generate a consumer movement pattern map from a video feed as indicated by IoT device data captured and received by a camera in a store.The local IoT device data analysis engine 508 may analyze IoT devices in response to receiving the IoT device and / or receiving instructions from an applicable system for managing load balancing of local infrastructure network devices to analyze IoT device data through fog networking, such as the fog networking IoT device analysis load balancing systems described in this paper. For example, the local IoT device data analysis engine 508 may automatically analyze IoT device data when it receives the IoT device data at a local infrastructure network device on which the local IoT device data analysis engine 508 is implemented. Furthermore, the local IoT device data analysis engine 508 may analyze IoT device data as part of load balancing across local infrastructure network devices to analyze IoT device data through fog networking.

[0100] In a particular implementation, the local IoT device data analysis engine 508 is configured to analyze IoT device data according to the characteristics of the IoT device operation. When analyzing IoT device data according to the characteristics of the IoT device operation, the local IoT device data analysis engine 508 may determine the characteristics of the IoT device operation. For example, the local IoT device data analysis engine 508 may determine an IoT device type that has generated specific IoT device data for analysis. Furthermore, when analyzing IoT device data according to the characteristics of the IoT device operation, the local IoT device data analysis engine 508 may analyze the IoT device data based on a format of the content of the IoT device data.For example, if specific rules and methods are uniquely used to analyze IoT device data content of a particular type, the local IoT device data analysis engine 508 may analyze IoT device data content of the particular type using the specific methods and following the specific rules. Furthermore, when analyzing IoT device data according to the characteristics of the IoT device's operation, the local IoT device data analysis engine 508 may analyze the IoT device based on a type of IoT device used to generate IoT device data and / or an application used to generate the IoT device data. For example, if a camera generates IoT device data, the local IoT device data analysis engine 508 may use appropriate image processing techniques to analyze the IoT device data.

[0101] To access the Fig. Returning to the example of the local IoT device analytics system 502 illustrated in Figure 5, the IoT device data storage management engine 510 is intended to represent a machine configured to manage the storage of IoT device data on a local infrastructure network device. When managing the storage of IoT device data on a local infrastructure network device, the IoT device data storage management engine 510 may select specific IoT device data to be stored on the device and then store the specific IoT device data on the local infrastructure network device. For example, the IoT device data storage management engine 510 may select and then store only IoT device data that will be analyzed through fog networking in the creation of IoT device data.The IoT device data storage management engine 510 may store IoT device data locally on a local infrastructure network device to enable load balancing of local network devices when analyzing IoT device data through fog networking. For example, the IoT device data storage management engine 510 may store specific IoT device data on a local infrastructure network device in response to instructions received from an applicable system for managing load balancing of local infrastructure network devices for analyzing IoT device data through fog networking, such as the fog networking IoT device analysis load balancing systems described in this paper.

[0102] In a particular implementation, when managing the storage of IoT device data on a local infrastructure network device, the IoT device data storage management engine 510 is configured to remove IoT device data from the storage on the network device as part of selective local storage. For example, the IoT device data storage management engine 510 may remove IoT device data from the local storage of a local infrastructure network device when the IoT device data is no longer used to generate IoT device analytics data. The IoT device data storage management engine 510 may remove IoT device data from the storage of a local infrastructure network device as part of load balancing local infrastructure network devices for analyzing IoT device data via fog networking.For example, the IoT device data storage management engine 510 may remove IoT device data from the local storage of a local infrastructure network device based on instructions received from an applicable system for managing load balancing of local infrastructure network devices for analyzing IoT device data through fog networking, such as the fog networking-based IoT device analysis load balancing systems described in this paper.

[0103] With reference to the Fig. In the local IoT device analytics system 502 illustrated in Figure 5, the IoT device analytics data storage management engine 512 is intended to represent a machine configured to manage the storage of IoT device analytics data on a local infrastructure network device. When managing the storage of IoT device analytics data on a local infrastructure network device, the IoT device analytics data storage management engine 512 may select specific IoT device analytics data to be stored on the device and then store the specific IoT device analytics data on the local infrastructure network device. For example, the IoT device analytics data storage management engine 512 may only select and then store IoT device analytics data that is sent to a remote system as part of fog networking.The IoT device analytics data storage management engine 512 may store IoT device analytics data locally on a local infrastructure network device as part of load balancing local network devices when analyzing IoT device data through fog networking. For example, the IoT device analytics data storage management engine 512 may store specific IoT device analytics data on a local infrastructure network device in response to instructions received from an applicable system for managing load balancing of local infrastructure network devices for analyzing IoT device data through fog networking, such as the fog networking-based IoT device analytics load balancing systems described in this paper.

[0104] In a particular implementation, when managing the storage of IoT device analytics data on a local infrastructure network device, the IoT device analytics data storage management engine 512 is configured to remove IoT device analytics data from storage on the network device as part of selective local storage. For example, the IoT device analytics data storage management engine 512 may remove IoT device analytics data from local storage on a local infrastructure network device after the IoT device analytics data has been sent to a remote system. The IoT device analytics data storage management engine 512 may remove IoT device analytics data from storage on a local infrastructure network device as part of load balancing local infrastructure network devices for analyzing IoT device data via fog networking.For example, the IoT device analytics data storage management engine 512 may remove IoT device analytics data from the local storage of a local infrastructure network device based on instructions received from an applicable system for managing load balancing of local infrastructure network devices for analyzing IoT device data through fog networking, such as the fog networking-based IoT device analytics load balancing systems described in this paper.

[0105] With reference to the Fig. In the local IoT device analytics system 502 illustrated in Figure 5, the remote system routing engine 514 is intended to represent a system configured to transmit IoT device data and / or IoT device analytics data to a remote system. The remote system routing engine 514 may transmit IoT device data and IoT device analytics data as part of analyzing IoT device data through fog networking. When transmitting IoT device data and IoT device analytics data to a remote system, the remote system routing engine 514 may select specific data to transmit and subsequently transmit the data. For example, the remote system routing engine 514 may select a specific subset of IoT device data to transmit to a remote system and subsequently transmit the specific subset of IoT device data to the remote system.The remote system routing engine 514 may transmit IoT device data and / or IoT device analytics data to a remote system via an applicable data transport or messaging protocol. For example, the remote system routing engine 514 may transmit IoT device analytics data to a remote system according to the MQTT protocol.

[0106] In a particular implementation, the remote system routing engine 514 is configured to transmit IoT device data and / or IoT device analytics data to a remote system via application-based routing. When using application-based routing to transmit IoT device data and / or IoT device analytics data to a remote system, the remote system routing engine 514 may transmit the data based on the characteristics of the IoT device operation. For example, if IoT device data was generated by a particular type of IoT device and a particular remote system is configured to process data for the particular type of IoT device, the remote system routing engine 514 may transmit IoT device analytics data generated from the IoT device data to the particular remote system.In another example, if IoT device analytics data is generated based on a specific application running on an IoT device, and a specific remote system is configured to process data from the specific application, then the remote system routing engine 514 may transmit the IoT device analytics data to the specific remote system. When transmitting data based on the characteristics of the IoT device's operation, the remote system routing engine 514 may determine the characteristics of the IoT device's operation.

[0107] In an operational example of the Fig. In the exemplary local IoT device analysis system 502 shown in Figure 5, the IoT device data receiving engine 504 at a local infrastructure network device receives the IoT device data generated by an IoT device as part of the IoT device accessing network services over a LAN. In the operational example of the system shown in Fig. 5, the local IoT device data analysis engine 508 determines the characteristics of the IoT device during operation to generate the IoT device data. Furthermore, in the operating example of the system shown in Fig. 5, the system uses the IoT device data based on the characteristics of the IoT device during operation to generate the IoT device data to generate IoT device analytics data.

[0108] In the example of the operation of the Fig. 5, the IoT device data storage management engine 510 manages the local storage of the received IoT device data in the IoT device data storage 506 on the local infrastructure network device. In addition, the IoT device analytics data storage management engine 512 manages the example of the system shown in Fig. 5, the generated IoT device analysis data is stored locally in the IoT device data store 506 on the local infrastructure network device. In the example operation of the system shown in Fig. 5, the remote system routing engine 514 transmits a portion of the IoT device data and / or the IoT device analytics data to a remote system.

[0109] Fig. 6 illustrates a flowchart 600 of an example method for analyzing IoT device data through fog networking at a local infrastructure network device. The flowchart 600 begins at module 602, where IoT device data generated by an IoT device is received at a local infrastructure network device as part of the IoT device's access to network services. An applicable IoT device data receiving engine, such as the IoT device data receiving engines described in this paper, may receive IoT device data generated at the IoT device as part of an IoT device's access to network services. IoT device data may be received at a local infrastructure network device directly from an IoT device that generated the IoT device data or another local infrastructure network device.For example, IoT device data can be received at a local infrastructure network device as part of load balancing across a variety of network devices to analyze IoT device data through fog networking. IoT device data can be received at a local infrastructure network device configured as a suitable device for providing IoT device network service access over a LAN, such as the multiprotocol infrastructure network devices described in this paper.

[0110] Flowchart 600 continues with module 604, where the operational characteristics of the IoT device in operation to generate the IoT device data are determined. An applicable engine for determining characteristics of IoT device operation, such as the local IoT device data analysis engines and / or the remote system routing engines described in this paper, may determine characteristics of the IoT devices in operation to generate the IoT device data. For example, an IoT device type of the IoT device that generated the IoT device data may be determined. In another example, an application for generating the IoT device data at the IoT device may be determined.

[0111] Flowchart 600 continues with module 606, where the IoT device data is analyzed at the local infrastructure network device to generate IoT device analytics data through fog networking using the operational characteristics of the IoT device. An applicable engine for analyzing IoT device data through fog networking, such as the local IoT device data analytics engines described in this paper, can analyze the IoT device data at the local infrastructure network device to generate IoT device analytics data through fog networking using the operational characteristics of the IoT device.For example, if a certain type of IoT device data is generated by the IoT device, as indicated by the operational characteristics of the IoT device, rules specific to analyzing the specific type of IoT device data can be applied when analyzing the IoT device data to generate IoT device analytics data on the local infrastructure network device.

[0112] Flowchart 600 continues with module 608, where the local storage of the IoT device data on the local infrastructure network device is managed. An applicable engine for managing the local storage of IoT device data on a local infrastructure network device, such as the IoT device data storage management engines described in this paper, may manage the local storage of the IoT device data on the local infrastructure network device. The local storage of the IoT device data on the local infrastructure network device may be managed as part of load balancing when analyzing IoT device data through fog networking.For example, IoT device data can be stored locally on the local infrastructure network device based on instructions received from an applicable system for managing load balancing across local infrastructure network devices when analyzing IoT device data through fog networking, such as the fog networking-based IoT device analytics load balancing systems described in this paper.

[0113] Flowchart 600 continues with module 610, where the local storage of the IoT device analytics data on the local infrastructure network device is managed. An applicable engine for managing the local storage of IoT device analytics data on a local infrastructure network device, such as the IoT device analytics data storage management engines described in this paper, may manage the local storage of the IoT device analytics data on the local infrastructure network device. The local storage of the IoT device analytics data on the local infrastructure network device may be managed as part of load balancing for analyzing IoT device data through fog networking.For example, IoT device analytics data can be stored locally on the local infrastructure network device based on instructions received from an applicable system for managing load balancing across local infrastructure network devices when analyzing IoT device data through fog networking, such as the fog networking-based IoT device analytics load balancing systems described in this paper.

[0114] Flowchart 600 continues with module 612, where portions of the IoT device data and / or the IoT device analytics data are transmitted to a remote system as part of analyzing the IoT device data through fog networking. An applicable engine for transmitting data to a remote system as part of analyzing IoT device data through fog networking, such as the remote system routing engines described in this paper, may transmit a portion of the IoT device data and / or the IoT device analytics data to a remote system as part of analyzing the IoT device data through fog networking. A portion of the IoT device data and / or the IoT device analytics data may be transmitted to a remote system via application-based routing according to the operational characteristics of the IoT devices.

[0115] Fig. 7 illustrates a diagram 700 of an example system for analyzing IoT device data generated by an IoT camera device through fog networking. Fig. The example system illustrated in Figure 7 includes an IoT camera device 702, a LAN 704, a local infrastructure network device 706, a computer-readable medium 708, and a local IoT device analytics system 710. The IoT camera device 702 is intended to represent a device configured to capture media that can be perceived by a user either before or after processing the data generated by the IoT camera device 702. For example, the IoT camera device 702 may be a camera configured to capture a live feed of a plurality of images that can be perceived by a human. The IoT camera device 702 may be positioned relative to a device configured to enable IoT devices to access network services over a network, such as the multi-protocol infrastructure network devices described in this paper.The IoT camera device 702 may generate camera data included as part of the IoT device data. The camera data includes data that can be used to render media in a form that can be perceived by a human.

[0116] In the Fig. In the example system illustrated in Figure 7, the IoT camera device 702 is coupled to the local infrastructure network device 706 via a LAN 704. The LAN 704 may include either a wired or wireless connection connecting the local infrastructure network device 706 to the IoT camera device 702. For example, the LAN 704 may include a wireless connection via Wi-Fi connecting the IoT camera device 702 to the local infrastructure network device 706. In another example, the LAN 704 may include a wired USB connection connecting the IoT camera device 702 to the local infrastructure network device 706.

[0117] The local infrastructure network device 706 is intended to represent a device configured to facilitate the provision of network service access to IoT devices. The local infrastructure network device 706 may be an edge device and / or a forwarding device. For example, the local infrastructure network device 706 may be an edge device configured to enable IoT devices to access network services, such as the multi-protocol infrastructure network devices described in this paper. In another example, the local infrastructure network device 706 may be a forwarding device integrated as part of a LAN backend and configured to forward data used in providing IoT devices with network service access.The local infrastructure network device 706 may receive camera data generated by the IoT camera device 702 as part of the IoT camera device 702 accessing network services. For example, the local infrastructure network device 706 may receive camera data of a video captured by the IoT camera device 702 over the LAN 108 as part of the IoT camera device 702 accessing network services over the LAN 108.

[0118] The local IoT device analysis system 710 is coupled to the local infrastructure network device 706 via the computer-readable medium 708. The local IoT device analysis system 710 is intended to represent a system configured to analyze IoT device data through fog networking, such as the local IoT device analysis systems described in this paper. The local infrastructure network device 706 may be implemented as part of, or otherwise on, the local infrastructure network device 706 to analyze IoT device data on the local infrastructure network device 706 through fog networking.

[0119] In the Fig. In the example system illustrated in Figure 7, the local IoT device analytics system 710 includes an image processing engine 712 and a user recognition engine 714. The image processing engine 712 is intended to represent an engine configured to process images in camera data captured by an IoT camera device. When processing images in camera data captured by an IoT camera device, the image processing engine 712 may perform preprocessing on camera data included as part of the IoT device data received from an IoT camera device. Preprocessed data generated by the image processing engine 712 may be included as part of the IoT device data analytics data generated by fog networking and subsequently provided to a remote system as part of the analysis of IoT device data by fog networking.

[0120] The user recognition engine 714 is intended to represent an engine configured to recognize user identifications from camera data, regardless of whether the data is preprocessed or not. For example, the user recognition engine 714 may recognize user identifications in camera data and / or preprocessed camera data. User identifications recognized by the user recognition engine 714 may be represented by IoT device analytics data. Additionally, user identifications recognized by the user recognition engine 714 may be used to identify a device used by a user, possibly to grant the user access to network services via the device.For example, an identification of a user recognized by the user recognition engine 714 may be used to identify an identification of a device used by the user without using a MAC address of a device, which may change.

[0121] Fig.8 illustrates a flowchart 800 of an example method for analyzing camera data at a local infrastructure network device through fog networking. Flowchart 800 begins at module 802, where the camera data generated at an IoT camera device is received at a local infrastructure network device of a plurality of infrastructure network devices over a LAN as part of the IoT camera device accessing network services over the LAN. An applicable machine for receiving IoT device data at a local infrastructure network device is, for example, the IoT device data receiving machines described in this paper. A local infrastructure network device that receives the camera data may be an edge device of a LAN and / or a forwarding device of a LAN.For example, a local infrastructure network device receiving the camera data may be a multi-protocol infrastructure network device at a boundary of a LAN and configured to provide network service access to IoT devices over the LAN. In another example, a local infrastructure network device receiving the camera data may be a device that is part of a backend of a LAN used to provide network service access to IoT devices.

[0122] Flowchart 800 continues with module 804, where load balancing is performed across the plurality of local infrastructure network devices to analyze the camera data using fog networking on at least one of the plurality of local infrastructure network devices. An applicable system for managing load balancing between local infrastructure network devices for the purpose of analyzing IoT device data using fog networking, such as the fog networking-based IoT device analysis load balancing systems described in this paper, may perform load balancing among the plurality of local infrastructure network devices for the purpose of using fog networking to analyze the camera data on at least one of the plurality of local infrastructure network devices.For example, the camera data may be sent from the local infrastructure network device to another local infrastructure network device of the plurality of local infrastructure network devices to analyze the camera data on the other local infrastructure network device using fog networking. In another example, the camera data may be maintained on the local infrastructure network device using load balancing to analyze the camera data on the local infrastructure network device using fog networking.

[0123] Flowchart 800 continues with module 806, where the camera data is analyzed at at least one of the plurality of local infrastructure network devices to generate IoT device analytics data as part of the fog networking analysis of the camera data. An applicable system for analyzing IoT devices through fog networking at local infrastructure network devices, such as the local IoT device analytics systems described in this paper, may analyze the camera data locally at at least one of the plurality of local infrastructure network devices to generate IoT device analytics data locally as part of the fog networking analysis of the camera data at the at least one of the plurality of local infrastructure network devices. For example, the camera data may be preprocessed at at least one of the plurality of local infrastructure network devices to generate IoT device analytics data locally as part of the fog networking analysis of the camera data.In another example, the camera data may be analyzed to detect identities of users in images included as part of the camera data to generate IoT device analytics data indicating the identities of the users.

[0124] Flowchart 800 continues with module 808, where the IoT device analytics data from at least one of the plurality of local infrastructure network devices is provided to a remote system as part of analyzing the camera data through fog networking. An applicable engine for transmitting IoT device data and IoT device analytics data to a remote system, such as the remote system routing engines described in this paper, may provide the IoT device analytics data from at least one of the plurality of local infrastructure network devices to a remote system as part of analyzing the camera data through fog networking. The IoT device analytics data may be provided to a remote system via an applicable data transmission or messaging protocol. For example, the IoT device analytics data may be transmitted to a remote system via a lightweight messaging protocol.

[0125] These and other examples cited in this paper are intended to illustrate, but not necessarily limit, the described implementation. As used herein, the term "implementation" refers to an implementation that serves to exemplify, but not limit, the scope of the invention. The techniques described in the preceding text and figures can be mixed and matched if circumstances require the development of alternative implementations.

Claims

[1] Method comprising: Receiving IoT device data generated by an IoT device (106; 314, 320; 506) while the IoT device (106) is operating via a LAN (108; 704) on a local infrastructure network device (104; 304, 306; 706) of a plurality of local infrastructure network devices (104; 304, 306; 706), wherein the IoT device data is received as part of access by the IoT device (106) to network services via the LAN (108; 704); Performing load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using at least one of the plurality of local infrastructure network devices (104; 304, 306; 706); Analyzing the IoT device data at at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to locally generate IoT device analysis data as part of analyzing the IoT device data through fog networking; Providing the IoT device analysis data generated at the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) by the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to a remote system as part of analyzing the IoT device data through fog networking; Determining properties of the IoT device (106; 314, 320; 506) during operation to generate the IoT device data; and Transmitting the IoT device analytics data from the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to the remote system through application-based routing based on the characteristics of the IoT device (106; 314, 320; 506) in operation. [2] The method of claim 1, wherein at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) comprises the local infrastructure network device of the plurality of local network devices (104; 304, 306; 706) that receives the IoT device data, wherein the IoT device data is analyzed locally at the local infrastructure network device (104; 304, 306; 706) to generate the IoT device analysis data by fog networking. [3] The method of claim 1, wherein the local infrastructure network device is a multi-protocol infrastructure network device (104) configured to provide access to network services for IoT devices (106) acting as stations via a WLAN, and wherein at least one of the plurality of local infrastructure network devices (104) comprises the local infrastructure network device (104) of the plurality of local infrastructure network devices (104) that receives the IoT device data, and wherein the IoT device data is analyzed locally at the local infrastructure network device (104) to generate the IoT device analysis data by fog networking. [4] The method of claim 1, wherein the local infrastructure network device is a forwarding device (104; 304, 306; 706) that is part of a backend of the LAN (108; 704), and wherein at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) includes the local infrastructure network device (104; 304, 306; 706) of a plurality of local network devices (104; 304, 306; 706) that receives the IoT device data, and wherein the IoT device data is analyzed locally at the local infrastructure network device (104; 304, 306; 706) to generate the IoT device analysis data by fog networking. [5] The method of claim 1, further comprising: Determining factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) that provide IoT devices (106; 314, 320; 506), including IoT device network service access, over the LAN (108; 704); Performing load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using the plurality of local infrastructure network devices (104; 304, 306; 706) based on the factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) that provide network service access to the IoT devices (106; 314, 320; 506) via the LAN (108; 704). [6] The method of claim 1, further comprising: Determining factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) analyzing a plurality of IoT device data through fog networking; Performing load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using the plurality of local infrastructure network devices (104; 304, 306; 706) based on the factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) analyzing a plurality of IoT device data through fog networking. [7] The method of claim 1, further comprising: Determining characteristics of the IoT device (106; 314, 320; 506) during operation to generate the IoT device data; Analyzing the IoT device data at at least one of the plurality of local infrastructure network devices as part of analyzing the IoT device data through fog networking based on the characteristics of the IoT device (106; 314, 320; 506) in operation to generate the IoT device data. [8] The method of claim 1, wherein the IoT device (106; 314, 320; 506) includes an IoT camera device (702) and the IoT device data includes camera data of images captured by the IoT camera device (702). [9] The method of claim 1, wherein performing load balancing comprises: Sending the IoT device data and / or the IoT device analysis data from a second local infrastructure network device of the plurality of local infrastructure network devices (104; 304, 306; 706) to a first local infrastructure network device and storing the IoT device data and / or the IoT device analysis data on the first local infrastructure network device if the first local infrastructure network device of the plurality of local infrastructure network devices (104; 304, 306; 706) has storage capacity available. [10] The method of claim 1, wherein performing load balancing comprises: Sending the IoT device data from a second local infrastructure network device of the plurality of local infrastructure network devices (104; 304, 306; 706) to a first local infrastructure network device and analyzing the IoT device data on the first local infrastructure network device to generate the IoT device analysis data if the first local infrastructure network device of the plurality of local infrastructure network devices (104; 304, 306; 706) has computing resources available to analyze the IoT device data. [11] The method of claim 1, further comprising: Sending the IoT device data from a first local infrastructure network device of the plurality of local infrastructure network devices (104; 304, 306; 706) to a second local infrastructure network device of the plurality of local infrastructure network devices (104; 304, 306; 706) for analysis; where: the first local infrastructure network device is configured not to perform any preprocessing of the IoT device data; and å the second local infrastructure network device is assigned as a central IoT device data analysis device for analyzing the IoT device data to generate the IoT device analysis data. [12] System comprising: an IoT device data receiving engine (310, 316; 504) configured to receive IoT device data generated by an IoT device (106; 314, 320; 506) operating over a LAN (108; 704) at a local infrastructure network device (106; 314, 320; 706) of a plurality of local infrastructure network devices (104; 304, 306), wherein the IoT device data is received as part of access by the IoT device (106; 314, 320; 706) to network services over the LAN (108; 704); an IoT device analysis load balancing engine (326) configured to perform load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using at least one of the plurality of local infrastructure network devices (104; 304, 306; 706); a local IoT device data analysis engine (508) configured to analyze the IoT device data on at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to locally generate IoT device analysis data as part of analyzing the IoT device data through fog networking; a remote system routing engine (514) configured to provide the IoT device analysis data generated at the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) by the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to a remote system as part of analyzing the IoT device data by fog networking, to determine properties of the IoT device (106; 314, 320; 506) during operation to generate IoT device data; and transmit the IoT device analytics data from the at least one of the plurality of local infrastructure network devices to the remote system through application-based routing based on the characteristics of the IoT device in operation. [13] The system of claim 12, wherein at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) comprises the local infrastructure network device (104; 304, 306; 706) of the plurality of local network devices (104; 304, 306; 706) that receives the IoT device data, wherein the IoT device data is analyzed locally at the local infrastructure network device (104; 304, 306; 706) to generate the IoT device analysis data by fog networking. [14] The system of claim 12, wherein the local infrastructure network device is a multi-protocol infrastructure network device (104) configured to provide access to network services for IoT devices (106) acting as stations over a WLAN, and wherein at least one of the plurality of local infrastructure network devices (104) comprises the local infrastructure network device (104) of the plurality of local infrastructure network devices (104) that receives the IoT device data, and wherein the IoT device data is analyzed locally at the local infrastructure network device (104) to generate the IoT device analysis data by fog networking. [15] The system of claim 12, wherein the local infrastructure network device is a forwarding device (104; 304, 306; 706) that is part of a backend of the LAN (108; 704), and wherein at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) includes the local infrastructure network device (104; 304, 306; 706) of a plurality of local network devices (104; 304, 306; 706) that receives the IoT device data, and wherein the IoT device data is analyzed locally at the local infrastructure network device (104; 304, 306; 706) to generate the IoT device analysis data by fog networking. [16] The system of claim 12, further comprising: an IoT device network service access factor identification engine (322) configured to determine factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) that provide IoT devices (106; 314, 320; 506), including the IoT device network service access, over the LAN (108; 704); the IoT device analysis load balancing engine (326) further configured to perform load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using the plurality of local infrastructure network devices (104; 304, 306; 706) based on the factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) providing network service access to the IoT devices (106; 314, 320; 506) via the LAN (108; 704). [17] The system of claim 12, further comprising: an IoT device analysis factor identification engine (324) configured to determine factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) analyzing a plurality of IoT device data through fog networking; the IoT device analysis load balancing engine (326) further configured to perform load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using the plurality of local infrastructure network devices (104; 304, 306; 706) based on the factors associated with the plurality of local infrastructure network devices (104; 304, 306; 706) analyzing a plurality of IoT device data through fog networking. [18] The system of claim 12, wherein the local IoT device data analysis engine (508) is further configured to: Determining characteristics of the IoT device (106; 314, 320; 506) during operation to generate the IoT device data; Analyzing the IoT device data at at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) as part of analyzing the IoT device data through fog networking based on the characteristics of the IoT device (106; 314, 320; 506) in operation to generate IoT device data. [19] The system of claim 12, wherein the IoT device (106) includes an IoT camera device (702) and the IoT device data includes camera data of images captured by the IoT camera device (702). [20] System comprising: Means (310, 316; 504) for receiving IoT device data generated by an IoT device (106; 314, 320; 506) while the IoT device (106; 314, 320; 506) is operating via a LAN (108; 704) on a local infrastructure network device (104; 304, 306; 706) of a plurality of local infrastructure network devices (104; 304, 306; 706), wherein the IoT device data is received as part of the IoT device (106; 314, 320; 506) accessing network services via the LAN (108; 704); Means (326) for performing load balancing across the plurality of local infrastructure network devices (104; 304, 306; 706) for the purpose of using fog networking to analyze the IoT device data using at least one of the plurality of local infrastructure network devices (104; 304, 306; 706); Means (508) for analyzing the IoT device data at at least one of the plurality of local infrastructure network devices (104; 304, 306; 706), for locally generating IoT device analysis data as part of analyzing the IoT device data by fog networking; Means (514) for Providing the IoT device analysis data generated at the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) by the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to a remote system as part of analyzing the IoT device data by fog networking, Determining properties of the IoT device (106; 314, 320; 506) during operation to generate the IoT device data, and Transmitting the IoT device analytics data from the at least one of the plurality of local infrastructure network devices (104; 304, 306; 706) to the remote system through application-based routing based on the characteristics of the IoT device (106; 314, 320; 506) in operation.

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