Method and system for resource management in a networked ecosystem

Abstract

A method and system for resource management in a networked ecosystem. A resource management system for a networked or Internet of Things (IoT) ecosystem is described. The IoT ecosystem has a first initiator node, a second initiator node, multiple relay nodes, a first target node, and a second target node. The first target node is outside the range of the first initiator node, and the second target node is outside the range of the second initiator node. The IoT ecosystem employs an extended multi-hop proximity ranging protocol to measure a first range between the first initiator node and the first target node via a first subset of relay nodes. A master node determines a first parameter associated with a first range measurement request and a second parameter associated with a second range measurement request. The master node prioritizes the first range measurement request relative to the second range measurement request based on the first and second parameters.

Description

[0001] introduce

[0002] Advances in global automation technology have led to the adoption of network-based management for countless storage, diagnostics, maintenance, sensors, actuators, controls, and other operations. For example, home charging operations for modern electric vehicles (EVs) or plug-in hybrid electric vehicles (PHEVs) can be scheduled and managed using "smart garage" network connectivity. Other aspects of smart garage automation include smartphone-based monitoring and garage door opening / closing operations, as well as control of climate settings such as temperature, humidity, and air quality. Security systems can be similarly managed from remote locations. In various environments, such as garages or manufacturing plants, such automation also facilitates the hosting of inventory, tooling, and parts management along with other functions. Similar technologies can be applied to other environments, including but not limited to users' homes or offices, industrial applications such as manufacturing or assembly facilities, distribution centers, warehouses, etc.

[0003] Effective implementation of global automation solutions relies on accurate proximity ranging between connected devices—that is, knowledge of the distance between communication nodes. In the context of global smart garage automation and other exemplary Internet of Things (IoT) applications, proximity ranging typically refers to the process of determining the distance between such nodes. Common proximity ranging techniques using electromagnetic waves include estimating the distance between the transmitter and receiver based on received signal strength, the time it takes for a packet transmitted from the transmitter to reach the receiver (time of flight), and other techniques. The transmitted signal can be ultra-wideband (UWB), Bluetooth, etc. TM Low Energy (BLE), Wi-Fi, etc. However, such technologies can only measure the proximity range between two devices within a very close proximity. For some emerging home or industrial IoT use cases that require low latency, or those where not all IoT devices belong to the same network or trust circle, such maximum proximity limitations for range measurement can result in a suboptimal user experience.

[0004] There may be periods when multiple initiators within the networked ecosystem simultaneously request ranging intents, resulting in congestion and device conflicts. Summary of the Invention

[0005] This disclosure relates to resource management in a networked ecosystem, specifically the management of controller (or CPU) time, power, and bandwidth for proximity ranging protocols. The proposed technical solution, hereinafter referred to as "multi-hop" proximity ranging, aims to manage, prioritize, and schedule resources such as wireless bandwidth, local CPU, and cloud computing resources in Internet of Things (IoT) environments, such as the global smart garage application described above, or in which devices located on different wireless networks, including those located in different buildings or operating areas, are required to determine inter-nodal distance measurements or distances between them. The determined distances are used in industrial applications that trigger one or more actions. The dynamic aspect refers to when the initiator node, the target node, or one or more relay nodes used for multi-hop ranging are moving, or when a subset of relay nodes leaves the network, a new relay node joins the network, or when the characteristics of the relay nodes, such as their computing power or energy state, change.

[0006] One aspect of this disclosure may include a resource management system for a networked or Internet of Things (IoT) ecosystem. The IoT ecosystem has a first initiator node, a second initiator node, multiple relay nodes, a first target node, and a second target node, wherein the first target node is outside the range of the first initiator node and the second target node is outside the range of the second initiator node. One of the first initiator node, the second initiator node, the first target node, and the second target node is designated as a master node. The IoT ecosystem employs an extended multi-hop proximity ranging protocol to measure a first range between the first initiator node and the first target node via a first subset of the multiple relay nodes. The master node determines multiple first parameters associated with a first range measurement request, including determining the first range between the first initiator node and the first target node via a first subset of the multiple relay nodes using the extended multi-hop proximity ranging protocol. The master node determines multiple second parameters associated with a second range measurement request, including determining the second range between the second initiator node and the second target node via a second subset of the multiple relay nodes using the extended multi-hop proximity ranging protocol. The master node prioritizes a first range measurement request relative to a second range measurement request based on the multiple first parameters and the multiple second parameters.

[0007] Another aspect of this disclosure may include a plurality of first parameters associated with the adoption of an extended multi-hop proximity ranging protocol, which are parameters related to at least one of the urgency and criticality of a first range measurement request.

[0008] Another aspect of this disclosure may include a plurality of second parameters associated with the adoption of an extended multi-hop proximity ranging protocol, which are parameters related to at least one of the urgency and criticality of the second range measurement request.

[0009] Another aspect of this disclosure may include a first subset of multiple relay nodes that is not mutually exclusive with a second subset of multiple relay nodes.

[0010] Another aspect of this disclosure may include at least one of a plurality of first parameters related to measuring the range between a first initiator node and a first target node using an extended multi-hop proximity ranging protocol, which are the location of the first initiator node and the distance between the first initiator node and one of a first subset of a plurality of relay nodes.

[0011] Another aspect of this disclosure may include a plurality of second parameters related to measuring the range between the second initiator node and the second target node using an extended multi-hop proximity ranging protocol, which are at least one of the location of the second initiator node and the distance between the second initiator node and one of a second subset of a plurality of relay nodes.

[0012] Another aspect of this disclosure may include one of a plurality of relay nodes being anchored to a fixture.

[0013] Another aspect of this disclosure may include at least one target node being a first target node and a second target node; wherein the control node prioritizes a first range measurement request from a first subset of the first initiator node and the first target node via multiple relay nodes, based on multiple first parameters and multiple second parameters, relative to a second range measurement request from a second initiator node and the second target node via a second subset of the relay nodes.

[0014] Another aspect of this disclosure may include an IoT ecosystem that is further a centralized system, wherein the centralized system includes a centralized controller configured to perform an extended multi-hop proximity ranging protocol, wherein the centralized controller communicates with a first initiator node, a second initiator node, a plurality of relay nodes, a first target node, and a second target node.

[0015] Another aspect of this disclosure may include the IoT ecosystem being a decentralized system.

[0016] Another aspect of this disclosure may include a method for resource management for a networked ecosystem, the method comprising identifying a first initiator node, a second initiator node, a plurality of relay nodes, a first target node, and a second target node in the networked ecosystem, wherein the first target node is outside the range of the first initiator node and the second target node is outside the range of the second initiator node, wherein one of the first initiator node, the second initiator node, the first target node, and the second target node is a master node; determining a first range between the first initiator node and the first target node via an extended multi-hop proximity ranging protocol and via a first subset of the plurality of relay nodes; determining a plurality of first parameters associated with a first range measurement request, including determining the first range between the first initiator node and the first target node via a first subset of the plurality of relay nodes; determining a plurality of second parameters associated with a second range measurement request via the master node, including determining the second range between the second initiator node and the second target node via a second subset of the plurality of relay nodes using an extended multi-hop proximity ranging protocol; and prioritizing a first range measurement request relative to a second range measurement request based on the plurality of first parameters and the plurality of second parameters.

[0017] A resource management system for an Internet of Things (IoT) ecosystem includes: an IoT ecosystem having a first initiator node, a second initiator node, multiple relay nodes, a first target node, and a second target node, wherein the first target node is outside the range of the first initiator node and the second target node is outside the range of the second initiator node; wherein one of the first initiator node, the second initiator node, the first target node, and the second target node is designated as a master node; wherein the IoT ecosystem employs an extended multi-hop proximity ranging protocol to measure a first range between the first initiator node and the first target node via a first subset of the multiple relay nodes; wherein the IoT ecosystem employs an extended multi-hop proximity ranging protocol to measure a first range between the first initiator node and the first target node via the multiple relay nodes. The first subset measures a second range between a second initiator node and a second target node; wherein the master node determines multiple first parameters related to the first range measurement request, wherein the first range measurement request includes determining a first range between the first initiator node and the first target node via a first subset of multiple relay nodes using an extended multi-hop proximity ranging protocol; wherein the master node determines multiple second parameters related to a second range measurement request, wherein the second range measurement request includes determining a second range between the second initiator node and the second target node via a second subset of multiple relay nodes using an extended multi-hop proximity ranging protocol; and wherein the master node prioritizes the first range measurement request relative to the second range measurement request based on multiple first parameters and multiple second parameters. The multiple first parameters related to using the extended multi-hop proximity ranging protocol include parameters related to at least one of urgency and criticality of the first range measurement request. The multiple second parameters related to using the extended multi-hop proximity ranging protocol include parameters related to at least one of urgency and criticality of the second range measurement request. The first subset of multiple relay nodes is not mutually exclusive with the second subset of multiple relay nodes. The first parameters related to measuring the range between the first initiator node and the first target node using an extended multi-hop proximity ranging protocol include at least one of the location of the first initiator node and the distance between the first initiator node and one of a first subset of a plurality of relay nodes. The second parameters related to measuring the range between the second initiator node and the second target node using an extended multi-hop proximity ranging protocol include at least one of the location of the second initiator node and the distance between the second initiator node and one of a second subset of a plurality of relay nodes. One of the plurality of relay nodes is anchored to a fixed object. Based on the first and second parameters, the master node prioritizes a first range measurement request from the first initiator node and the first target node via a first subset of a plurality of relay nodes, relative to a second range measurement request from the second initiator node and the second target node via a second subset of a plurality of relay nodes.The IoT ecosystem further includes centralized systems, which include a centralized controller configured to execute an extended multi-hop proximity ranging protocol, wherein the centralized controller communicates with a first initiator node, a second initiator node, multiple relay nodes, a first target node, and a second target node. The IoT ecosystem also includes distributed systems.

[0018] A method for resource management for a networked ecosystem, the method comprising: identifying a first initiator node, a second initiator node, a plurality of relay nodes, a first target node, and a second target node in the networked ecosystem, wherein the first target node is outside the range of the first initiator node and the second target node is outside the range of the second initiator node, wherein one of the first initiator node, the second initiator node, the first target node, and the second target node is a master node; determining a first range between the first initiator node and the first target node via a first subset of the plurality of relay nodes via an extended multi-hop proximity ranging protocol; determining a second range between the second initiator node and the second target node via a second subset of the plurality of relay nodes via the extended multi-hop proximity ranging protocol; determining a plurality of first parameters associated with a first range measurement request via the master node, including determining a first range measurement between the first initiator node and the first target node via the first subset of the plurality of relay nodes; determining a plurality of second parameters associated with a second range measurement request via the master node, including determining a second range between the second initiator node and the second target node via the second subset of the plurality of relay nodes; and prioritizing a first range measurement request relative to a second range measurement request based on the plurality of first parameters and the plurality of second parameters. The determination of multiple first parameters related to a first range measurement request includes determining at least one parameter related to the urgency and criticality of the first range measurement request. The determination of multiple second parameters related to a second range measurement request includes determining at least one parameter related to the urgency and criticality of the second range measurement request. A first subset of multiple relay nodes is not mutually exclusive with a second subset of multiple relay nodes. The determination of multiple first parameters related to a first range measurement request includes determining at least one of the location of a first initiator node and a range between the first initiator node and one of the first subsets of multiple relay nodes. The determination of multiple second parameters related to a second range measurement request includes determining at least one of the location of a second initiator node and a range between the second initiator node and one of the second subsets of multiple relay nodes.

[0019] A resource management system for a networked ecosystem includes: a networked ecosystem having a control node, a first initiator node, a second initiator node, a plurality of relay nodes, a first target node, a second target node, and a master node, wherein one of the first or second target nodes is outside a range; wherein the networked ecosystem employs an extended multi-hop proximity ranging protocol to measure the range between the first initiator node and the first target node via the plurality of relay nodes, and employs an extended multi-hop proximity ranging protocol to measure the range between the second initiator node and the second target node via the plurality of relay nodes; wherein the master node determines a plurality of first parameters related to a first range measurement request, including determining a first range between the first initiator node and the first target node via a first subset of the plurality of relay nodes using the extended multi-hop proximity ranging protocol; wherein the master node determines a plurality of second parameters related to a second range measurement request, including determining a second range between the second initiator node and the second target node via a second subset of the plurality of relay nodes using the extended multi-hop proximity ranging protocol; and wherein the master node prioritizes the first range measurement request relative to the second range measurement request based on the plurality of first parameters and the plurality of second parameters. The master node includes one of the plurality of relay nodes. Each of the relay nodes selects the next relay node on the path to the target node based on a set of local metrics associated with the relay node. Determining the plurality of first parameters associated with the first range measurement request includes determining at least one of the location of the first initiator node and the distance between the first initiator node and one of the plurality of relay nodes in a first subset. Determining the plurality of second parameters associated with the second range measurement request includes determining at least one of the location of the second initiator node and the distance between the second initiator node and one of the plurality of relay nodes in a second subset.

[0020] The foregoing generalized features and other features, as well as the advantages of this disclosure, will readily become apparent from the following detailed description of illustrative examples and models used to implement this disclosure when considered in conjunction with the accompanying drawings and claims. Furthermore, this disclosure explicitly includes combinations and sub-combinations of the elements and features presented above and below. Attached Figure Description

[0021] Figure 1 This is an illustration of a representative networked ecosystem configured to use a centralized extended multi-hop proximity ranging strategy, according to this disclosure.

[0022] Figure 2 This is a block diagram illustrating a protocol for implementing a centralized extended multi-hop ranging strategy according to the present disclosure.

[0023] Figure 3A and Figure 3B The illustration shows a model for implementing a centralized extended multi-hop proximity ranging strategy according to the present disclosure.

[0024] Figure 4 A control routine in flowchart form is schematically illustrated, describing a method for implementing an extended multi-hop proximity ranging strategy according to the present disclosure.

[0025] Figure 5 A weighted round-robin control scheme is schematically illustrated, which, according to this disclosure, can be used in embodiments of a networked ecosystem to allocate bandwidth to various nodes to achieve multi-hop proximity ranging.

[0026] Figure 6 Elements of a centralized proximity ranging system and associated method for implementing extended multi-hop ranging in a networked ecosystem, according to the present disclosure, are schematically illustrated.

[0027] Figure 7 Elements of a distributed proximity ranging system and associated method for implementing extended multi-hop ranging in a networked ecosystem, according to the present disclosure, are schematically illustrated.

[0028] Figure 8 The illustration schematically depicts a multi-ecosystem / fabric model for consolidating messages in a scenario where initiator nodes in an IoT network send messages or reports via different IoT hubs, according to the present disclosure.

[0029] Figure 9 The illustration schematically depicts a multi-ecosystem / construction model for merging messages in a scenario where an initiator node in an IoT network sends a message or report via a master node.

[0030] This disclosure may be modified or embodied in alternative forms, wherein representative embodiments are shown in the accompanying drawings and described in detail below. The inventive step of this disclosure is not limited to the disclosed embodiments. Rather, this disclosure is intended to cover alternatives that fall within the scope of this disclosure as defined by the appended claims. Detailed Implementation

[0031] Referring now to the accompanying drawings, which are present throughout several views, the same reference numerals denote the same features. Figure 1 The diagram illustrates a local Internet of Things (IoT) networking ecosystem 10, in which multiple communication nodes communicate with each other as described in this article. Figure 1The networked ecosystem 10 shown is described as a non-limiting, globally automated smart garage of a smart home 11, also referred to as an IoT hub 11. In such an embodiment, the nodes mentioned above may include one or more of the following: for example, a wireless / Wi-Fi enabled thermostat 12, a garage door 13, a security camera 14, an appliance 9, a smartphone 16 or other smart devices, such as a smartwatch or another wearable device, a light bulb 17, a vehicle 18, etc. As will be described below, the networked ecosystem 10 also includes a computer-readable storage medium 19 having a recorded or stored activation profile 190, which, as described below, is the desired action or service of a target node or device. As part of this method, the activation profile 190 is accessible from the computer-readable storage medium 19. The actual host or location of the computer-readable storage medium 19 is some form of controller and may vary depending on the embodiment, and is therefore depicted as being connected to... Figure 1 Separate various networked devices in the process.

[0032] Alternatively, a networked ecosystem can be an automated industrial facility, such as a manufacturing plant, assembly plant, fulfillment center, or warehouse, with work areas for performing various related operations. For example, a networked ecosystem may include, among other possible areas or workspaces, inventory sections such as shelves or parts / component bins, one or more production lines, receiving areas, and office spaces. In such embodiments, the nodes mentioned above can correspond to various computers, wireless devices, sensors, smart devices, etc., including passive radio frequency identification (RFID) tags, barcode / barcode readers, and the like.

[0033] The following text is about Figure 1 The descriptions of smart home, smart garage implementations, and smart facilities are for illustrative purposes only, and the actual number and configuration of the constituent nodes participating in the networked ecosystem 10 will vary depending on the intended applications.

[0034] The term "controller," and related terms such as microcontroller, control, control unit, processor, etc., refer to one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), one or more electronic circuits, one or more central processing units, such as one or more microprocessors, and one or more associated non-transitory memory components in the form of memory and storage devices (read-only, programmable read-only, random access, hard disk drives, etc.). Non-transitory memory components are capable of storing machine-readable instructions in the form of one or more software or firmware programs or routines, one or more combinational logic circuits, one or more input / output circuits and devices, signal conditioning, buffer circuitry systems, and other components, which can be accessed and executed by one or more processors to provide the described functionality. One or more input / output circuits and devices include analog-to-digital converters and associated devices that monitor inputs from sensors, wherein such inputs are monitored at a preset sampling frequency or in response to a triggering event. Software, firmware, program, instructions, control routines, code, algorithms, and similar terms mean a set of instructions executable by the controller, including calibration and lookup tables. Each controller executes one or more control routines to provide the desired functionality. Routines may be executed at regular intervals, such as every 100 microseconds during ongoing operation. Alternatively, routines may be executed in response to the occurrence of a triggering event. Communication between controllers, actuators, and / or sensors may be achieved using direct wired point-to-point links, networked communication bus links, wireless links, or other communication links. Communication includes exchanging data signals via a conductive medium, including, for example, electrical signals; exchanging electromagnetic signals via air; exchanging optical signals via optical waveguides; and so on. Data signals may include discrete, analog, and / or digitized analog signals representing inputs from sensors, actuator commands, and communication between controllers.

[0035] The term "signal" refers to a physically identifiable indicator that conveys information and can be a suitable waveform (e.g., electrical, optical, magnetic, mechanical, or electromagnetic) that can travel through a medium, such as DC, AC, sine wave, triangle wave, square wave, vibration, and the like.

[0036] The terms “calibration,” “calibrated,” and related terms refer to the result or process that relates a desired parameter of a device or system to one or more sensed or observed parameters. Calibration, as described herein, can be simplified to a storable table of parameters, multiple executable equations, or another suitable form that can be used as part of a measurement or control routine.

[0037] A parameter is defined as a measurable quantity that represents a physical characteristic of a device or other component, which can be identified using one or more sensors and / or a physical model. Parameters can have discrete values, such as "1" or "0", or they can have infinitely variable values.

[0038] In this specification and the claims below, the term "cloud" and related terms can be defined as a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage devices, applications, and services) that can be rapidly provisioned via virtualization and released with minimal management effort or service provider interaction, and then scaled accordingly. The cloud model can consist of various features (e.g., on-demand self-service, extensive network access, resource pooling, rapid elasticity, measurement services, etc.), service models (e.g., Software as a Service (“SaaS”), Platform as a Service (“PaaS”), Infrastructure as a Service (“IaaS”), and deployment models (e.g., private cloud, community cloud, public cloud, hybrid cloud, etc.).

[0039] refer to Figure 3A and Figure 3B These two figures will be discussed in more detail below. The networked ecosystem 10 includes, for example, an initiator node 20I with ultra-wideband (UWB) capabilities, and a plurality of connected relay nodes B, C, D, E, F, and G, wherein each relay node includes at least one lower-capability transit node and at least one higher-capability "smart" node, as described in detail below. The networked ecosystem 10 also includes a target node 20T, which is located outside the range constraints of the initiator node 20I and therefore loses direct communication with it. In this embodiment, the computer-readable storage medium 19 mentioned above contains a recorded activation profile 190. The networked ecosystem 10 as described herein is also configured to use an extended multi-hop proximity ranging protocol 30 (… Figure 2 The system estimates the corresponding ranges of one or more neighboring nodes among multiple relay nodes B, C, D, E, F, and G within the range constraints of the initiator node 20I, and uses these ranges to dynamically determine the inter-node distance between the initiator node 20I and the target node 20T. One or more of the multiple relay nodes B, C, D, E, F, and G are anchored to a fixed object.

[0040] As this article envisions, Figure 1Proximity ranging between nodes in a networked ecosystem 10 involves accurately estimating the distance between nodes. For example, manufacturing, assembly, kitting, or order fulfillment operations may occur across multiple areas or buildings. Multiple buildings within a manufacturing plant will tend to have multiple controllers or routers, which in turn connect to a centralized controller, such as a local controller or a cloud-based controller. Ranging between two devices located in two different buildings may require cloud support. In such cases, the centralized extended multi-hop ranging method disclosed herein can be used.

[0041] As an example, modern proximity ranging technology used in typical smart home / garage, manufacturing plant, and other local network applications is based on the open-source Matter. TM The (MATTER) standard governs the management of communications for locally networked devices. In some applications, a device / node can send an activation command to a target node, at least in part, based on proximity. However, Figure 1 Users of the connected ecosystem 10, industrial IoT use cases, or other home, office, industrial, medical, or other use cases can benefit from the reduced latency and improved customer experience derived from it.

[0042] For example, from their smart home 11 ( Figure 1 Users walking from the kitchen to the garage may expect to find the garage door 13 fully open and their vehicle 18 disconnected from the charging station (not shown) upon arrival, and / or adjusted according to the user's customized settings approaching vehicle 18, which may include seat adjustment, mirror adjustment, cabin temperature setting, and one or more others. If a user is left waiting for the scheduled actions to complete before entering vehicle 18, the overall user experience may be degraded. Therefore, the extended multi-hop proximity ranging protocol 30 aims to extend communication distance and reduce response latency, prevent out-of-range activation errors, and improve features such as… Figure 1 The overall customer experience within a local network, such as a representative connected ecosystem 10.

[0043] Although for the sake of simplicity, some figures have been omitted from the illustrations. Figure 1 The hardware associated with each node can take the form of one or more application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), one or more electronic circuits, one or more central processing units, such as one or more microprocessors or processors, and associated computer-readable storage media / memory. Such non-transitory components of memory include... Figure 1The computer-readable storage medium 19 is capable of storing machine-readable instructions in the form of one or more software or firmware programs or routines, one or more combinational logic circuits, one or more input / output circuits and devices, signal conditioning and buffering circuit systems, and other components accessible by one or more processors to provide the aforementioned functionality. Therefore, using such hardware residing at various nodes and associated antennas, receivers, and transmitters, communication can be achieved, for example, via Wi-Fi, Zigbee, Bluetooth. TM Bluetooth TM Low energy (BLE) and other technologies enable wireless information exchange between nodes.

[0044] refer to Figure 2 The Extended Multi-Hop Proximity Ranging Protocol 30 can be used as follows (see below). Figure 3A and Figure 3B The centralized and decentralized alternative embodiments are described. For clarity, the extended multi-hop proximity ranging protocol 30 is illustrated as a block diagram. In the IoT context, an action is triggered at the target node based on a predetermined or pre-recorded user profile. For example, Figure 1 Users of the connected ecosystem 10, walking from the kitchen of the illustrated smart home 11 to the garage, expect to adjust the seats, mirrors, and / or temperature settings of the vehicle 18 according to their level of customization. Similarly, users moving around the smart home 11 can set profiles relative to their location within the smart home 11, such as when to turn on a light bulb 17, charge the vehicle 18, or stop charging the vehicle 18. Similar expectations can exist in industrial embodiments of other connected devices.

[0045] Despite this brief setup, as noted above, the extended multi-hop proximity ranging strategy disclosed herein allows for an extension of the distance between the initiating and target nodes relative to existing strategies. This extended range can lead to a better user experience, particularly for actions that take time to complete after initiation, such as opening / closing doors, disengaging the electric vehicle (EV) charging handle, or custom adjustments within the vehicle for a specific driver. Therefore, their earlier activation through enhanced proximity ranging enables helps reduce or eliminate the time the user must wait for them to complete. Consequently, programmed actions can begin much faster than they would otherwise not have the benefits of this teaching.

[0046] exist Figure 2 In the diagram, box 32 represents an activation profile that can be transmitted to an IoT-enabled controller 20CC, as indicated by arrow 33. This controller 20CC can be implemented in various ways, such as... Figure 3A The centralized ecosystem model shown is 10⁻¹ or 10⁻². Figure 3BThe master / “smart” node in the protocol is described below. The extended multi-hop proximity ranging protocol 30 also includes a proximity ranging box 34, which, as indicated by arrow 35, is deployed on or hosted by the initiator node 20I, such as... Figure 1 Vehicles 18, smartphones 16, etc. The proximity ranging frame 34 can provide the activation rules 34R required for operation according to this disclosure.

[0047] exist Figure 2 The extended multi-hop proximity ranging protocol 30 also includes various relay nodes, including IoT-capable / discoverable connected relay devices B, C, D, E, F, and G operating within the networked ecosystem 10 as either lower-capacity switching nodes or higher-capacity smart nodes. Switching nodes include, for example, RFID tags and other low-power IoT devices that may be in sleep mode. Smart nodes include devices with multiple antennas capable of determining the time of arrival (ToA) and / or angle of arrival (AoA). Box 36 represents such advanced technical capabilities of smart nodes, such as lower power limitations, higher computing power, angle of arrival (AoA) estimation capabilities, or other capabilities, while box 38 represents the lower capabilities of switching nodes, such as RFID tags and other possible low-power IoT devices that are typically in sleep mode and therefore require time to wake up and take actions such as proximity ranging. The extended multi-hop proximity ranging protocol 30 also considers the operation of the target node 20T, i.e., the intended executor of the action initiated via service activation from the initiator node 20I. The following examples rely on Figure 2 The architecture of the extended multi-hop proximity ranging protocol 30.

[0048] Figure 3A The schematic illustration depicts the elements of a networked ecosystem 10-1, which implements an embodiment of an extended multi-hop proximity ranging protocol 30 employing a centralized system. The centralized ecosystem model 10-1 illustrates various devices / nodes, which are nominally labeled AH for simplicity. Figure 3A This is an exemplary implementation in which a central controller, in this case cloud-based or other central controller, is utilized to reach a target node 20T outside the range in order to activate a desired action or service thereon. Such utilization can be performed using a cloud-based or external edge network. While this teaching is flexible enough for centralized multi-hop ranging with or without network separation, Figure 3A The illustration depicts a representative case where two areas (area #1 and area #2) are separated from each other by a boundary 21, which can be, for example, a wall between different structures, designated workspaces, buildings, or other areas. This teaching can be applied to ranging-to-session resource management and communication in this or other densely deployed network environments.

[0049] exist Figure 3A In this context, node A represents the initiator node 20I, which is the node / device that initiates a request to communicate with the target node 20T (node ​​H) located outside the initiator node's range and requests the desired action or service from the target node 20T (node ​​H). Nodes B, C, D, E, F, and G represent relay nodes, which can be configured as the aforementioned switching nodes, and none of the relay nodes can be configured as more computationally powerful intelligent nodes. Figure 3A The centralized ecosystem model 10-1 also includes additional network nodes, in this case a cloud-based controller 20 and a wireless router 22, such as Wi-Fi, MATTER or Zigbee routers, and border routers 24, are also Wi-Fi... MATTER or Zigbee border router. In this embodiment, nodes B and E act as so-called "anchor" nodes (described below), where the anchor state is... Figure 3A The asterisk (*) indicates the location. Generally, if a device operating in area #1 uses a wireless router 22 (in the form of a Zigbee network router) to activate a device in area #2, for example, via a border router 24 operating the MATTER network, then the device uses a given designated node in the MATTER network for ranging / location, which in this case is node E (*). Then, the designated node E will reach the target node 20T via one or more relay nodes in the MATTER network, such as node G.

[0050] In smart home 11 Figure 1 In home charging applications where an electric vehicle power supply device (EVSE) is connected to a charger, the charger can act as a designated node, where a user approaches a smart home 11 that is distanced via the designated node. Therefore, the use of a designated node can be used to enhance security. Thus, as described herein, identifying the distance path between an initiator node 20I and a target node 20T via a central controller 20 may require using a distance path that includes a designated node. This action may further include using the central controller 20... Figure 2 A proximity ranging protocol is used to estimate the proximity distances of one or more neighboring nodes of a plurality of relay nodes within the range limit of the initiator node 20I. In this exemplary case, the respective nodes of the one or more neighboring nodes are located in the initiator network or the target network. When estimating the range of one or more neighboring nodes, each neighboring node can be instructed to estimate the range between itself and the target node 20T.

[0051] exist Figure 3AIn this method, it can be assumed that the initiator node 20I is already part of the exemplary MATTER network. This assumption can be further extended. In some instances, the initiator node 20I or the target node 20T may undergo a commissioning process to join the MATTER network, but can still use the specified node mentioned above as a proxy to initiate a new multi-hop proximity ranging session / become part of a new multi-hop proximity ranging session. For example, vehicle 18 ( Figure 1 It may not be part of the MATTER network, but it still uses an Electric Vehicle Power Supply Equipment (EVSE) charging station (e.g., part of an Original Equipment Manufacturer (OEM) network or an exemplary MATTER network) as a proxy to invoke actions on another device, such as a television or lighting system, while the commissioning process continues.

[0052] In the deployment space illustrated, each node is capable of at least two functions: (i) communication and (ii) ranging. The ranging function may include performing time-of-arrival (ToA) and angle-of-arrival (AoA) measurements. Functionality (i) and (ii) may originate from the same wireless technology, such as Wi-Fi, or from different wireless technologies such as Wi-Fi and Ultra-Wideband (UWB). Additionally, each node connects to the central controller 20 (a local or cloud-based server or backend) via a corresponding wireless communication network with a corresponding gateway, such as Wi-Fi, THREAD, Zigbee, etc. The initiator node 20I may be part of an initiator network, while the target node 20T may be part of a target network, which may be, for example, a private network. In different embodiments of the initiator and target node networks, in various embodiments, the initiator node 20I communicates with the target node 20T via an intermediate central controller 20. Figure 3A As shown by the dashed lines BE and CF, this multi-hop method offers the flexibility to enable or disable inter-network ranging / location. In some implementations, the central controller 20 determines one or more ranging paths from one node to another, and particularly the ranging path from the initiator node 20I to the target node 20T.

[0053] As is understood in this field, Figure 3A The border router 24 can be used to connect the local network to the Internet via the wireless router 22, or to one or more wider networks. As its name suggests, the border router 24 can be located at the edge of the network, in which case it is the initiator network / first wireless network served by the wireless router 22. Functionally, the border router 24 is used to route data traffic and thus acts as a gateway between the local network and one or more external networks. The wireless router 22 is used to communicate with nodes within a given local network, such as... Figure 3AIn a non-limiting simplified embodiment, nodes A, B, C, and D are represented as link line 220. Wireless router 22 may also be connected to the Internet, for example, via an Ethernet box (not shown), a fiber optic or coaxial cable connection, a cellular link, or other means. As indicated by arrows CC1 and CC2, border router 24 connects other nodes, such as nominal nodes E, F, G, and H, to wireless router 22 via central controller 20. Figure 3A Inside, link lines 220 and 240 represent wireless communication paths within the networked ecosystem 10.

[0054] In the centralized extended multi-hop method and system disclosed in this paper, the following assumptions are made: (1) Some nodes are equipped with antenna arrays and are capable of measuring AoA and TOA, such as nodes with ultra-wideband (UWB) capabilities, such as smartphones or other mobile devices, or Figure 1 The vehicles 18, mobile automated robots, etc., and (2) each node with AoA / ToA capability are connected to the cloud-based controller 20 via various wireless networks as shown, such as an external central server capable of transmitting ranging parameters between the initiating network in region #1 and the target network in region #2 of Figure 3. The cloud-based controller 20 is also referred to herein as the central controller 20.

[0055] Figure 3A The centralized multi-hop strategy is characterized by local distance map creation, centralized multi-hop localization, and dynamic neighbor sampling. For local distance map creation, each node with AoA capability periodically scans neighboring nodes, the periodicity of which is determined by network mobility or the capabilities of neighboring nodes. According to the currently disclosed embodiments, communication includes a route from the local network in region #1 to the cloud-based controller 20 and ultimately to the target node 20T via the central controller 20. The embodiments require an alternative route when links BE and DF are unavailable; in this case, the initiator network can locate itself within its region, i.e., region #1, and communicate via the central controller 20 to infer the location of nodes in region #2. References are provided below. Figure 4This document describes a method for implementing centralized extended multi-hop localization according to embodiments. Regarding dynamic neighbor sampling, shortest distance or path algorithms can be used to find paths, adding scanning periodicity to the ranging path nodes. Therefore, in embodiments according to this disclosure, the proximity ranging method described herein may include using a shortest distance algorithm to determine a node path from the initiator node 20I through one or more neighboring nodes to the target node 20T, or determining the path based on nodes with higher power and / or computing power. For example, plugged-in nodes may be more suitable than battery-operated nodes. Nodes with higher charge states may be more suitable than those with lower charge states. Nodes with higher computing power may be more suitable than those with lower computing power.

[0056] Brief Reference Figure 3B According to another embodiment of the present disclosure, a centralized ecosystem model 10-2. Figure 3A The functions of the central controller 20 can be performed by other nodes. Bridge CC3 exists between wireless routers 22 and 24, for example, in a wireless point-to-point network connection. In a possible use case, a mobile device acting as an initiator node 20I in an original equipment manufacturer (OEM)-specific network can attempt to activate devices in an IoT network such as a MATTER network. In this case, for example... Figure 1 The smartphone 16's mobile device can communicate with designated nodes in the IoT network / OEM network, allowing the mobile device to communicate only via relay nodes in the IoT network, for example... Figure 3B In the simplified network example, nodes E, F, and G include a designated node that reaches a target node 20T. The designated node may be configured with authentication, security, and / or privileges to interact with either the initiator node 20I or the target node 20T. In one or more embodiments, the initiator node 20I and / or the target node 20T may not directly interact (or are not permitted to directly interact) with any other node on the network to which the target node 20T is a member.

[0057] refer to Figure 4 Embodiments of method 100 are described to illustrate aspects of this teaching. Generally, method 100 is directed to managing intra-node interactions in a networked IoT environment, wherein such a networked environment is embodied herein as... Figure 1The IoT environment 10. Method 100 may include identifying candidate smart devices among multiple neighboring nodes using a request service, possibly a high-power initiator node or IoT hub 11, from the networked ecosystem 10. Based on the initiator node's requirements, the candidate smart devices may be high-power, low-power, or a hybrid (flexibly operating as either a low-power or high-power device type). Method 100 includes selectively assigning candidate devices to trusted designated nodes within the networked ecosystem 10 based on the initiator node's battery level or other parameters. Method 100 also includes determining the optimal periodicity for transmitting status messages from the initiator node to the IoT hub 11 of the networked ecosystem 10 based on parameters of the initiator node. Method 100 includes transmitting status messages to the IoT hub 11 at the optimal periodicity using a trusted designated node. This action occurs via the designated node, such that when reporting status messages to the IoT hub 11, the trusted designated node negotiates the periodicity and acts as a proxy for the initiator node.

[0058] like Figure 4 A representative embodiment of method 100 shown begins at logic block B102. Here, method 100 includes initiating or requesting a service via an initiator node. Method 100 then proceeds to block B104.

[0059] Box B104 requires scanning neighboring nodes 15 for the candidate node, i.e., one or more nodes 15 that may act as the aforementioned trusted specified node. This may include scanning neighboring devices / nodes within their proximity that may act as the trusted specified node. Method 100 then proceeds to box B106.

[0060] At box B106, method 100 includes determining whether the initiator node is a multi-node device. When the initiator node is a multi-node device, method 100 proceeds to box B108, and alternatively, when the initiator node is a single-node device, method 100 proceeds to box B110.

[0061] After determining that the initiating node is a multi-node device, the process proceeds from box B106 to box B108. Box B108 includes locating intra-node neighbors. As appreciated in the art, intra-node neighbors in the networking ecosystem 10 are adjacent devices / nodes connected to or belonging to the same local network segment as the scanning device. For example, intra-node neighbors may share a router or network connector. After locating intra-node neighbors, method 100 proceeds to box B112.

[0062] At box B110, Figure 4 Method 100 continues to follow a single-node process, as shown in the reference above. Figure 1 The method 100 then completes, wherein a single trusted designated node subsequently operates as a proxy for message transmission by the initiator node.

[0063] Box B112 includes determining whether an intra-node neighbor is located at box B108. If an intra-node neighbor is located, method 100 proceeds to box B114. If no intra-node neighbor is located, method 100 instead proceeds to box B110.

[0064] Box B114 includes the above reference. Figure 8 The merged message groups are then proceeded to box B116.

[0065] At box B116, the merged message is sent to IoT hub 11, for example... Figure 8 The IoT hub H1 or H3. Method 100 is then completed.

[0066] As described above, this solution is designed for introducing smart devices as designated nodes, such as... Figure 1 The methods and node systems within a networked IoT ecosystem, such as the networked ecosystem 10. Based on parameters such as the energy budget of the initiating device, the designated node then selectively negotiates the optimal participation period for message exchange with the IoT hub 11. The trusted designated node's reports can be based on the requesting device's parameters or the current situation.

[0067] Trusted designated nodes can be categorized in this way and, in some instances, selected by the initiator node / device. In other methods, the IoT hub 11 can assist in selecting trusted designated nodes. Reporting is then performed at a cadence or periodically appropriate to the energy budget. This action ensures that high-power device types are available as part of the IoT ecosystem 10, such as the Matter ecosystem, even when in a power-depleted state. This is ensured by using trusted designated nodes as proxies to report device status to the IoT hub 11. In such embodiments, this, in turn, helps minimize battery power consumption. Given the foregoing disclosure, those skilled in the art will readily understand these and other accompanying benefits.

[0068] Figure 5 The diagram schematically illustrates a weighted cyclic control scheme 500, which can be implemented in a networked ecosystem 10 ( Figure 1 In the embodiment of the above, bandwidth is allocated to each node based on message criticality, urgency and task complexity to achieve multi-hop proximity ranging between multiple initiator nodes 510 and multiple receiver nodes 540.

[0069] Multiple initiator nodes 510 generate communication requests (request 1, request 2, request n), which are sent to a round-robin scheduler 520. The round-robin scheduler 520 executes a weighted round-robin algorithm that allocates ranging resources to the multiple initiator nodes 510, including time and bandwidth allocated for exchanging one or more packets to measure range and / or AoA, and computational resources for calculating range and / or AoA based on the received one or more packets. Based on the allocated ranging resources, ranging requests from the multiple initiator nodes 510 are dispatched to multiple receiver nodes 540 via a dispatch module 530.

[0070] The operation of the cyclic scheduler 520 includes the following: Each initiator has its own weight Wi, which is determined by a utility function U based on its application and whether it is actively used as a multi-hop node for ranging / localization, as follows:

[0071] Wi = U(Ci / (Di*Ti)),

[0072] in:

[0073] Ci represents the keyness of the table (Ci).

[0074] Di indicates the deadline or urgency, and

[0075] Ti represents the task completion complexity.

[0076] Each task has an average distance Li from the initiator node.

[0077] The following is a determination of the long-term bandwidth for each of the multiple initiator nodes 510:

[0078]

[0079] This arrangement is implemented to avoid starvation of weaker initiator nodes and to ensure a reasonable proportional allocation of communication bandwidth among initiator nodes.

[0080] Figure 6 The schematic diagram illustrates the components of a centralized proximity ranging system 600 and the associated method for implementing extended multi-hop ranging, which can be referenced... Figure 1The networked ecosystem 10 described in the embodiments is employed. The centralized proximity ranging system 600 includes relay nodes 630A of a first set of connections and relay nodes 630B of a second set of connections, wherein operation and connectivity are managed by a central controller 625, which, in one embodiment, is cloud-based. The relay nodes 630A of the first set of connections and 630B of the second set of connections may consist of intelligent nodes (master nodes) and / or relay nodes. The central controller 625 includes components capable of performing reference... Figure 2 The algorithm code for the extended multi-hop proximity ranging protocol 30 is described.

[0081] A mobile or stationary initiator node (“initiator client”) 610 initiates one or more communication efforts (“Instance 1”, “Instance 2”, “Instance n”) to achieve communication with a target node (“target client”) 640 within the centralized proximity ranging system 600. The target node (“target client”) 640 can also be mobile or stationary. In one embodiment, the target node 640 is outside the range of the initiator node 610, excluding direct communication with it. The central controller 625 employs an extended multi-hop proximity ranging protocol 30 to evaluate and manage communication and inter-node proximity estimation for relay nodes 630A across the first set of connections and relay nodes 630B across the second set of connections, taking into account conditions including energy budget, energy consumption, communication coverage, latency, node proximity, and node priority, such as relay, designation, criticality, etc.

[0082] The operation of the centralized proximity ranging system 600 includes the following: When multiple initiator nodes, such as a first initiator node and a second initiator node, seek to simultaneously perform multi-hop range estimation using one or more target nodes within the centralized proximity ranging system 600, a first control node, i.e., the central controller 625, determines multiple first parameters related to requesting range measurement between a first initiator node and at least one target node via a first subset of multiple relay nodes using the extended multi-hop proximity ranging protocol 30, and also determines multiple second parameters related to requesting range measurement between a second initiator node and at least one target node via a second subset of multiple relay nodes using the extended multi-hop proximity ranging protocol. The first and second parameters include parameters related to: node status, message characteristics such as urgency, criticality, task completion complexity / time, sleep mode, etc.; the location of the initiator node; the distance from the initiator node to one of the relay nodes; etc. Based on multiple first parameters and multiple second parameters, the control node prioritizes first communication requests from a first subset of relay nodes from a first initiator node and at least one target node, relative to second communication requests from a second subset of relay nodes from a second initiator node and at least one target node.

[0083] Figure 7 The schematic diagram illustrates the components of a distributed proximity ranging system 700 and the associated method for implementing extended multi-hop ranging, which can be referenced... Figure 1 The networked ecosystem 10 described in the embodiments is employed. The distributed proximity ranging system 700 includes a plurality of connected relay nodes 720, each of which includes a relay node 730 and one or more master nodes 725, wherein the master node(s) manages operation and connectivity based on information locally available to the master node(s). The master node(s) 725 includes functions capable of performing reference... Figure 2 The algorithm code for the extended multi-hop proximity ranging protocol 30 is described.

[0084] A mobile or stationary initiator node (“initiator client”) 710 initiates one or more ranging efforts (Instance 1, Instance 2, Instance n) to achieve ranging with a target node (“target client”) 740 within the networked ecosystem 10. The target node (“target client”) 740 can also be mobile or stationary. One or more master nodes 725 employ an extended multi-hop proximity ranging protocol 30 to evaluate and manage communication and proximity with neighboring relay nodes within their one-hop proximity range, considering conditions including: energy budget, energy consumption, communication coverage, latency, node proximity, and node priority, such as relay, designation, criticality, etc. In this embodiment, each set of relay nodes selects the next relay node on the path to the target node based on a metric.

[0085] Now for reference Figure 8 In some cases, devices in a connected IoT ecosystem 10 may include multiple IoT hubs 11, such as hubs H1, H2, and H3. Figure 1 In a representative embodiment, hubs H1, H2, and H3 can be embodied as multiple different control modules of vehicle 18. Devices with such connectivity options may require intelligent and unified optimization capabilities for assigning one of the nodes as a trusted designated node, and for determining an appropriate reporting rhythm or periodicity for energy. Various nodes 15 can act as initiator nodes 15I. A device acting as node N1 can request range measurements with hubs H1 and H3, while a device acting as node N2 can request range measurements with hub H2. Similarly, a device acting as node N3 can request range measurements with hub H3. Therefore, in this example, node N1 has two possible communication options, namely hub H1 or hub H3.

[0086] In scenarios where a device uses multiple hubs, sending periodic reports consumes significant processing power, requiring coordination of sleep / wake cycles and the like. To reduce power consumption, one aspect of this strategy involves merging reports and determining the optimal time to send a report / message to one of the IoT hubs 11. Representative conditions are illustrated in condition table 75 as, for example, energy budget, energy consumption, communication coverage, latency, node proximity, and node priority such as relay, designation, criticality, etc. Based on these conditions, a group of candidate nodes 15 is identified, from which a preferred node is selected. In some embodiments, the various conditions can be normalized and weighted such that condition table 75 collectively outputs a binary decision (0 or 1) regarding whether to assign a trusted designation node or continue using the initiator node 15I to transmit its status messages to the IoT hub 11.

[0087] For example, in box 76, node 15 is evaluated against condition table 75 to determine credible designated nodes, shown for simplicity as 15D-1 and 15D-2. Given the conditions, node 15N is ignored due to lack of the required capability. Node 15 * Nodes can be designated, but based on conditions and relative capabilities, designated nodes 15D-1 and 15D-2 are considered preferred choices. Of the two remaining designated nodes 15D-1 and 15D-2, node 15D-2 may currently be occupied, i.e., busy performing functions that exclude it from use as a trusted designated node. This would make node 15D-1 available for service and able to act as a trusted designated node 15D. In this example, node 15D-1 is then assigned as a trusted designated node and subsequently reported (Box 78).

[0088] In one embodiment, the centralized approach envisions using a central controller, such as a cloud-based server, backend device, or local server, operable to request range measurements with a target node for service activation as described above. For example, range-based applications are typically not feasible in multi-building scenarios where there is no cloud or field communication between different buildings. In instances where the nodes / devices requesting ranging do not co-reside on a communication network, according to this disclosure, the concept can use a central controller to seek one or more relay nodes and thereby coordinate extended multi-hop ranging.

[0089] Figure 9A distributed multi-ecosystem 900 or construction model according to an embodiment of the present disclosure is schematically illustrated, wherein a master node is used to perform multi-hop ranging between two other IoT nodes within the multi-ecosystem in order to perform actions when range is determined. The multi-ecosystem 900 includes a master node A 910, node 1911, node 2912, node 3913, and node 4914. In this embodiment, the master node A 910 listens for and records information included in periodic announcements from other IoT nodes within its communication range, such as nodes 1911, 2912, 3913, and 4914. Each message includes one or more of the following: the address (IP, MAC, and / or other device identifier) ​​of the reporting node, the node's identity and capabilities, an indication of whether the node is within the proximity range of the master node A 910, which helps the master node A 910 select relay nodes on the way to a given target node when it receives a multi-hop ranging request, and a list of other IoT nodes within the one-hop proximity range of the reporting IoT node. This further helps the master node A 910 create a local map of the layout of other IoT devices in its vicinity and can be used at least in part to select relay nodes on the way to a given target node when it receives a multi-hop ranging request from the initiating node. Other information associated with multiple nodes, such as nodes 1911, 2912, 3913, and 4914, includes: the capabilities of the respective IoT nodes, such as power supply (battery-powered or plugged in); the SOC indication of the respective nodes; the wake-up schedule of the respective nodes; their ability and willingness to participate in range measurements; their ability and willingness to participate in angle of arrival measurements; computing power; the mobility of the respective nodes, such as mobile or stationary nodes; the location information of the respective nodes (including timestamps if mobile); the available transmit power for sending periodic updates; and the maximum transmit power. The available transmit power for sending periodic updates can help master node 910 determine which nodes within its communication range are also in its proximity and thus can be used for multi-hop ranging. The maximum transmit power can be used to determine the radius of the reporting node for inter-node range measurements. While low-capacity IoT nodes may not be able to share some of this information, other master nodes within the communication range of master node A 910 can share more information and thus help it better plan the layout of surrounding nodes and thus make more efficient node selection when receiving multi-hop ranging requests. Master node A 910 can periodically rate nodes within its vicinity based on the recorded information and use that information when, for example, (a) determining whether to serve a multi-hop ranging request, (b) determining the best next-hop relay node for the requests it accepts, or (c) scheduling its resources when serving more than one multi-hop ranging request.

[0090] The concepts described in this paper provide a system and method for multi-hop ranging resource management, in which multiple initiating devices request multi-hop ranging from a manageable node, which prioritizes and schedules resources based on the state of a set of candidate relay nodes for multi-hop ranging. Prioritization can also be based on characteristics of each request, including one or more of criticality, urgency, and sleep mode scheduling.

[0091] Prioritization can also be based on characteristics, including the location of the requesting node, the distance of the node to one or more relay nodes, and one or more of the sleep mode scheduling of the requesting node.

[0092] Prioritization can also be based on the deterministic ranging capability between candidate relay node pairs that is above a predetermined threshold, and the processing resources available to the centralized node.

[0093] Prioritization may also include periodically establishing ratings for candidate relay nodes, wherein the rating of a candidate relay node is determined based on one or more of the following: power resources, ranging techniques, the rate at which it measures its proximity to its neighboring nodes, computational resources, knowledge of its own location, and / or whether it is a mobile or static node.

[0094] In one embodiment, the centralized node performs multi-hop ranging via more than one multi-hop route and combines the obtained ranging results to improve accuracy.

[0095] In one embodiment, a centralized node schedules resources for more than one multi-hop ranging request in order to optimize a utility function, wherein the utility function is based on one or more of criticality, urgency, request deadline, task completion time and / or total energy consumption.

[0096] In one embodiment, the first node prioritizes requests and schedules multi-hop ranging resources based on one or more of the following: the criticality of the request, the urgency of the request, and the energy required to complete the request at the first node.

[0097] In one embodiment, prioritization is further based on deterministic ranging capabilities above a predetermined threshold between the initiator node and candidate relay nodes, and / or between two candidate relay nodes, as well as the processing resources available for each relay node.

[0098] In one embodiment, a candidate relay node within the vicinity of an initiator node that has the capability to measure the angle of arrival is preferred over a candidate relay node that does not have this capability.

[0099] In one embodiment, a candidate relay node with plug-in power is preferred over a candidate relay node with battery operation.

[0100] In one embodiment, a static candidate relay node is preferred over a mobile candidate relay node.

[0101] In one embodiment, multi-hop ranging identifies more than one relay node, and the range between the initiator node and the target node is determined iteratively.

[0102] In one embodiment, the extended multi-hop proximity ranging protocol includes calculating the angle between a line connecting two nodes that are not within each other's immediate range and a reference direction, wherein the distance between the two nodes is measured via a relay node capable of calculating the range and angle of arrival between itself and each of the two nodes. This may include iteratively determining the range between the initiator and the target node by identifying a route that includes an initiator node, a target node, and a set of two or more relay nodes in a specific order, wherein the angle and range between the initiator and the target node relative to the reference direction are determined by iteratively calculating the angle and range between a first node and a second node on the route relative to the reference direction, the first and second nodes not being within each other's immediate range, the iteration further including subsequently selecting another node on the route to replace the second node according to the specific order.

[0103] The detailed description and accompanying drawings are supportive and descriptive of this teaching, but the scope of this teaching is defined only by the claims. While some preferred modes and other embodiments for carrying out this teaching have been described in detail, various alternative designs and embodiments exist to practice the teaching as defined in the appended claims. Furthermore, this disclosure explicitly includes combinations and sub-combinations of the elements and features presented above and below.

Claims

1. A resource management system for an Internet of Things (IoT) ecosystem, comprising: The IoT ecosystem has a first initiator node, a second initiator node, multiple relay nodes, a first target node, and a second target node, wherein the first target node is outside the scope of the first initiator node and the second target node is outside the scope of the second initiator node; One of the first initiator node, the second initiator node, the first target node, and the second target node is designated as the master node; The IoT ecosystem employs an extended multi-hop proximity ranging protocol to measure the first range between the first initiator node and the first target node via a first subset of multiple relay nodes; The IoT ecosystem employs an extended multi-hop proximity ranging protocol to measure a second range between a second initiator node and a second target node via a first subset of multiple relay nodes; The master node determines a number of first parameters related to the first range measurement request, wherein the first range measurement request includes determining a first range between a first initiator node and a first target node via a first subset of multiple relay nodes using an extended multi-hop proximity ranging protocol; The master node determines multiple second parameters related to the second range measurement request, wherein the second range measurement request includes determining a second range between the second initiator node and the second target node via a second subset of multiple relay nodes using an extended multi-hop proximity ranging protocol; and The master node prioritizes the first range measurement request relative to the second range measurement request based on multiple first parameters and multiple second parameters.

2. The resource management system of claim 1, wherein, The first parameters associated with the adoption of the extended multi-hop proximity ranging protocol include at least one parameter related to the urgency and criticality of the first range measurement request.

3. The resource management system according to claim 1, wherein, Several second parameters associated with the adoption of the extended multi-hop proximity ranging protocol include at least one parameter related to the urgency and criticality of the second range measurement request.

4. The resource management system according to claim 1, wherein, The first subset of multiple relay nodes is not mutually exclusive with the second subset of multiple relay nodes.

5. The resource management system according to claim 1, wherein, The first parameters associated with using an extended multi-hop proximity ranging protocol to measure the range between the first initiator node and the first target node include at least one of the location of the first initiator node and the distance between the first initiator node and one of a first subset of a plurality of relay nodes.

6. The resource management system according to claim 1, wherein, The second parameters associated with measuring the range between the second initiator node and the second target node using an extended multi-hop proximity ranging protocol include at least one of the locations of the second initiator node and the distances between the second initiator node and one of a second subset of multiple relay nodes.

7. The resource management system according to claim 1, wherein, One of the multiple relay nodes is anchored to a fixed object.

8. The resource management system according to claim 1, wherein, Based on multiple first parameters and multiple second parameters, the master node prioritizes the first range measurement requests, which are derived from the first initiator node and the first target node, from a second range measurement request via a second subset of multiple relay nodes, relative to the second range measurement requests derived from the second initiator node and the second target node via a second subset of multiple relay nodes.

9. The resource management system according to claim 1, wherein, The IoT ecosystem further includes a centralized system, which includes a centralized controller configured to execute an extended multi-hop proximity ranging protocol, wherein the centralized controller communicates with a first initiator node, a second initiator node, multiple relay nodes, a first target node, and a second target node.

10. The resource management system according to claim 1, wherein, The IoT ecosystem includes distributed systems.

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