Registered Edge Device Management

Abstract

The method may include obtaining device data for a set of edge devices. The method may further include obtaining a predicted movement path of the focal entity. The method may further include, for a first edge device of the set of edge devices, determining a first proximity of the first edge device to the predicted movement path based on the device data. The method may further include selecting the first edge device based at least in part on the first proximity. The method may further include transmitting a workload to the first edge device in response to selecting the first edge device. The method may further include receiving first captured data captured by the first edge device in response to transmitting the workload. The method may further include transmitting the first captured data to the electronic user device.

Description

[Technical field]

[0001] The present disclosure relates to management of network devices, and more particularly, to registered edge device management. [Background technology]

[0002] Edge devices can include various electronic devices connected to a network and configured to acquire data, process the data, and transmit the processed data to a centralized location, such as a web server or a cloud-based edge device management hub. For example, in some instances, a manufacturing facility can employ edge devices such as cameras, temperature sensors, and humidity sensors. In such instances, each edge device can acquire data about the manufacturing process, perform one or more calculations on the data, and transmit the processed data to a management hub configured to monitor and analyze the manufacturing process. In another example, a government agency can employ edge devices such as cameras, temperature sensors, barometers, and / or air quality sensors. In this example, such edge devices can acquire data about matters such as traffic conditions, weather conditions, and / or ambient conditions at a location. The edge devices can process such data and transmit the processed data to a centralized management hub.

[0003] The use of edge devices can present challenges. For example, in some cases, the usefulness of data generated by a set of edge devices may be limited by factors such as the number of available edge devices in proximity to a location of interest, the location (e.g., looking direction) of one or more edge devices, or the timing of data capture by one or more edge devices, or a combination thereof. In some cases, the control and / or management configuration of the edge devices may limit their ability to provide further usefulness, such as running additional workloads. Summary of the Invention

[0004] Aspects of the present disclosure are directed to a method that includes obtaining device data of a set of edge devices. The method may further include obtaining a predicted movement path of the focal entity. The method may further include determining a first proximity of the first edge device to the predicted movement path. The determining relates to a first edge device of the set of edge devices. The determining may be based on the device data. The method may further include selecting the first edge device. The selecting may be based, at least in part, on the first proximity. The method may further include transmitting a workload to the first edge device. The transmitting may be performed in response to selecting the first edge device. The method may further include receiving first captured data obtained by the first edge device. The receiving may be performed in response to transmitting the workload. The method may further include transmitting the first captured data to the electronic user device.

[0005] Advantageously, the aforementioned aspects of the present disclosure may enable data capture by one edge device of a set of edge devices based on the edge device having proximity to a predicted travel path of a focal entity. Thus, the aforementioned aspects may facilitate acquisition of accurate data corresponding to a focal entity having a travel path.

[0006] Further embodiments of the present disclosure including aspects discussed in the method above further include a case where the predicted travel path includes a first location and a second location. The method may further include a focal entity at the first location at a first time. The focal entity may be predicted to be at the second location at a second time. The second time may be subsequent to the first time. The method may further include selecting a second edge device of the set of edge devices. The selecting may be based, at least in part, on a second proximity of the second edge device to the second location. The method may further include transmitting the workload to the second edge device. The transmitting may be performed in response to selecting the second edge device. The method may further include receiving second capture data. The second capture data may be acquired by the second edge device. The receiving may be performed in response to transmitting the workload to the second device. The method may further include transmitting the second capture data to the electronic user device.

[0007] Advantageously, the foregoing aspects according to preferred embodiments of the present disclosure may enable multiple edge devices to accurately capture data about a focal entity over time as the focal entity moves.

[0008] Further embodiments of the present disclosure, including aspects discussed in the method above, further include a case in which the transmitting of the workload to the second edge device occurs at an intermediate time. The method may further include a second time subsequent to the intermediate time.

[0009] Advantageously, the aforementioned aspects of preferred embodiments of the present disclosure may improve the likelihood of capturing data about a focal entity before the focal entity leaves the location, since the edge device may acquire the workload before the focal entity arrives at the location.

[0010] Further aspects of the present disclosure are directed to systems and computer program products configured to perform the above-mentioned methods.

[0011] Aspects of the present disclosure are directed to a method that includes obtaining device data for a set of edge devices. The method may further include obtaining a predicted travel path of the focal entity. The method may further include determining that the subset is within a threshold distance of the predicted travel path. The determining relates to a subset of the set of edge devices. The determining may be based on the device data. The subset may consist of a mobile phone and a camera mounted on a building. The method may further include transmitting a workload to the subset. The transmitting may occur in response to the determining. The method may further include receiving a captured image. The captured image may be obtained by the subset. The receiving may occur in response to transmitting the workload. The method may further include transmitting the captured image to an electronic user device.

[0012] Advantageously, the foregoing aspects of the present disclosure may enable various types of edge devices to capture data regarding a focal entity having a path of travel.

[0013] Aspects of the present disclosure are directed to a method that includes obtaining device data for a set of edge devices. The method may further include obtaining a workload request from an electronic user device. The method may further include obtaining a predicted travel path of the focal entity. The method may further include determining that a subset of the set of edge devices is within a threshold distance of the predicted travel path. The determining may be performed in response to the workload request. The subset may consist of mobile phones. The subset may further consist of cameras mounted on a building. The method may further include transmitting a workload corresponding to the workload request to the subset. The transmitting may be performed in response to the determining. The method may further include receiving a captured image captured by the subset. The receiving may be performed in response to transmitting the workload. The method may further include transmitting the captured image to the electronic user device. The transmitting may be performed in response to the workload request.

[0014] Advantageously, the foregoing aspects of the present disclosure may enable various types of edge devices to capture images relating to a moving focal entity and transmit the captured images to an electronic user device upon request.

[0015] This Summary is not intended to describe each aspect, every implementation, or every embodiment of the present disclosure, or every combination thereof.

[0016] The drawings contained in this application are incorporated in and form a part of this specification. These drawings illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure. The drawings are merely illustrative of certain embodiments and are not intended to limit the disclosure. [Brief description of the drawings]

[0017] [Figure 1]FIG. 1 illustrates an example computing environment having registered edge device management, in accordance with an embodiment of the present disclosure. [Diagram 2] 1 is a flowchart diagram of an example method for implementing registered edge device management in accordance with an embodiment of the present disclosure. [Diagram 3] 1 is a diagram of an example geographic area in which registered edge device management is implemented, according to an embodiment of the present disclosure. [Figure 4] FIG. 1 is a diagram of representative major components of a computer system that may be used in accordance with embodiments of the present disclosure. [Diagram 5] FIG. 1 is a diagram of a cloud computing environment according to an embodiment of the present disclosure. [Figure 6] FIG. 1 is a diagram of an abstract model layer according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] While the invention is susceptible to various modifications and alternative forms, specific examples thereof have been shown by way of example in the drawings and will now be described in detail. It should be understood, however, that there is no intention to limit the invention to the particular embodiments described. On the contrary, it is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

[0019] FIELD OF THE DISCLOSURE

[0002] Embodiments of the present disclosure relate to management of network devices. More specifically, embodiments relate to registered edge device management. Although the present disclosure is not necessarily limited to such applications, various embodiments of the present disclosure can be understood through a discussion of various examples using this context.

[0020] Edge devices can include various electronic devices connected to a network and configured to acquire data, process the data, and transmit the processed data to a centralized location, such as a web server or a cloud-based edge device management hub. For example, in some instances, a manufacturing facility can employ edge devices such as cameras, temperature sensors, and humidity sensors. In such instances, each edge device can acquire data about the manufacturing process, perform one or more calculations on the data, and transmit the processed data to a management hub configured to monitor and analyze the manufacturing process. In another example, a government agency can employ edge devices such as cameras, temperature sensors, barometers, and / or air quality sensors. In this example, such edge devices can acquire data about matters such as traffic conditions, weather conditions, and / or ambient conditions at a location. The edge devices can process such data and transmit the processed data to a centralized management hub.

[0021] The use of edge devices can present challenges. For example, in some cases, the usefulness of data generated by a set of edge devices may be limited by factors such as the number of available edge devices in proximity to a location of interest, the location (e.g., looking direction) of one or more edge devices, or the timing of data capture by one or more edge devices, or a combination thereof. In some cases, the control and / or management configuration of the edge devices may limit their ability to provide further usefulness, such as running additional workloads.

[0022] To address these and other challenges, embodiments of the present disclosure include a Registered Edge Device Manager (REDM). In some embodiments, the REDM can retrieve device data for a set of edge devices registered with the REDM and send workloads to a subset of such registered edge devices based on one or more conditions being met. In some embodiments, the set of edge devices can have a respective set of different owners and / or custodians who can select to register the set of edge devices with the REDM. For example, in some embodiments, the set of edge devices can include: (1) a set of outdoor cameras mounted on a commercial building with a first custodian who manages the set of outdoor cameras, (2) a camera-integrated mobile phone with a first owner, and (3) a residential doorbell camera with a second owner. In this example, the first custodian, the first owner, and the second owner can each select to register the set of outdoor cameras, the mobile phone, and the residential doorbell camera with the REDM. Thus, advantageously, embodiments of the present disclosure can employ multiple different types of edge devices having various capabilities. In some embodiments, the REDM can obtain a predicted path of movement of a focal entity, such as a storm or tornado, and transmit workloads to a subset of edge devices that are within a threshold proximity to the focal entity. Thus, advantageously, embodiments of the present disclosure can employ registered edge devices to capture data at locations relative to the moving focal entity. In some embodiments, the REDM can transmit workloads to a subset of registered edge devices that are in proximity to a second location on the predicted path of movement. In these embodiments, the REDM can perform such transmissions prior to a time when the moving focal entity is predicted to be at the second location.For example, continuing with the tornado example above, a predicted tornado path of travel may include the tornado being in a first city at a first time and in a second city at a second time. In this example, the REDM may transmit a workload (e.g., instructions to capture image and audio data) to a registered edge device at the second location prior to the second time. Thus, embodiments of the present disclosure may advantageously employ registered edge devices in a manner that allows sufficient time to capture data of a moving focal entity.

[0023] Turning to the drawings, FIG. 1 illustrates a computing environment 100 including at least one each of a registered edge device manager (REDM) 105, a set of registered edge devices 150, a resource data server 135, a user device 140, and a computing device 145. In some embodiments, at least one REDM 105, the set of registered edge devices 150, the resource data server 135, the user device 140, and the computing device 145 can communicate data with one another over a network 165. The set of registered edge devices 150 can include one or more registered edge devices. For example, in some embodiments, the set of registered edge devices 150 can include n registered edge devices, where n is an integer greater than zero. For example, in an embodiment where the set of registered edge devices 150 includes only a first registered edge device 150-1 having a capture device 155-1 and a data processor 160-1, n=1, in an embodiment where the set of registered edge devices 150 includes two registered edge devices (the first registered edge device 150-1 described above and a second registered edge device 150-2 having a capture device 155-2 and a data processor 160-2), n=2, and so on. In some embodiments, one or more of the REDM 105, the set of registered edge devices 150, the resource data server 135, the user device 140, the computing device 145, and the network 165 may include a computer system, such as the computer system 401 described with respect to FIG.

[0024] 1, in some embodiments, the REDM 105 may be included in software installed on at least one computer system of the user device 140 and / or the computing device 145. The REDM 105 may include program instructions executed by a processor, such as the processor of the computing device 145, to perform one or more of the operations discussed with respect to FIG.

[0025] In some embodiments, the REDM 105 may include one or more modules, such as the device data manager 110, the workload manager 115, and the capture data manager 125. In some embodiments, the device data manager 110, the workload manager 115, and the capture data manager 125 may be integrated into a single module. In some embodiments, the device data manager 110, the workload manager 115, and the capture data manager 125, or a combination thereof, may acquire data, analyze data, store data, or transmit data, or a combination thereof. In some embodiments, the device data manager 110, the workload manager 115, and the capture data manager 125 may include program instructions executed by a processor, such as a processor of the computing device 145, to perform one or more operations discussed with respect to FIG. 2. For example, in some embodiments, the device data manager 110 may include program instructions for performing operation 205 of FIG. 2. In some embodiments, the workload manager 115 may include program instructions for performing operations 210-250 of FIG. 2. In some embodiments, the capture data manager 125 may include program instructions for performing operations 255 and 260 of Figure 2. The REDM 105 may autonomously perform the operations described with respect to Figure 2. Thus, the REDM 105 may efficiently and accurately perform the operations of the method 200 in embodiments in which the set of registered edge devices 150 includes multiple registered edge devices 150-n.

[0026] In some embodiments, the set of registered edge devices 150 may include one or more electronic devices, such as a camera, a mobile phone (e.g., a “smartphone”), a tablet, a wearable technology (e.g., a “smart watch”), a sensor (e.g., a temperature, humidity, air quality, or pressure sensor, or a combination thereof), and / or an audio recording device. In some embodiments, the set of registered edge devices 150 may include one or more cameras configured to capture images and / or sounds of public spaces. For example, such cameras may include cameras mounted on commercial buildings such that the cameras can capture images and / or sounds from areas proximate to the commercial buildings, such as parking lots, roads, building entrances and / or exits. In another example, such cameras may include cameras mounted on residential buildings, such as doorbell cameras, that can capture images and / or sounds from areas proximate to the residential buildings, such as roads or cul-de-sacs in front of the residential buildings. In another example, such cameras may include cameras mounted on physical structures, such as bridges, utility poles, and the like, that can capture images and / or sounds from areas, such as highways, road intersections, and the like. In some embodiments, such a camera may include a camera installed in a vehicle and capable of capturing images and / or sounds proximate to such a vehicle. Advantageously, the set of registered edge devices 150 may include multiple types of electronic devices capable of acquiring multiple types of data. Furthermore, in some embodiments, a registered edge device in the set of registered edge devices 150 may have separate, distinct, or unrelated, or combinations of owners and / or custodians that own and / or manage such registered edge devices. Advantageously, as further described below, the REDM 105 may send workloads to edge devices registered by multiple different owners and / or custodians.Thus, in some embodiments, the REDM 105 is not limited to obtaining data from one or more edge devices owned and / or managed by a single entity.

[0027] In some embodiments, the registered edge device 150-n may include at least one capture device 155-n or data processor 160-n or both. In some embodiments, the at least one capture device 155-n may include a sensor, an audio capture device, or an image capture device, or a combination thereof. For example, in an embodiment where the registered edge device 150-n is a smartphone, the smartphone may include multiple capture devices 155-n, such as an accelerometer, a camera, or a microphone, or a combination thereof. In another example, in an embodiment where the registered edge device 150-n is a camera, the camera may include multiple capture devices 155-n, such as temperature, humidity, or pressure sensors, or a combination thereof. In some embodiments, the at least one data processor 160-n may include a processor that is the same as or substantially similar to the processor 410 of FIG. 4. In some embodiments, the data processor 160-n may be configured to perform analysis, manipulation, or calculations on the data acquired by the registered edge device 150-n, or a combination thereof. For example, in some embodiments, the data processor 160-n may be configured to filter data acquired by the registered edge device 150-n. For example, in some embodiments, the registered edge device 150-n may include image analysis software that enables the data processor 160-n to perform functions such as obscuring, cropping, and / or re-editing images captured by the registered edge device 150-n.

[0028] In some embodiments, the at least one resource data server 135 may include one or more web servers. In some embodiments, the at least one resource data server 135 may correspond to a weather service, an emergency alert service, or a police service, or a combination thereof. In some embodiments, the at least one resource data server 135 may include software configured to analyze data to predict a path of travel of a moving focal entity, such as a storm distribution or a vehicle. For example, in some embodiments, a weather service may generate a predicted path of travel of a hurricane by one or more resource data servers 135 analyzing weather data. In these embodiments, the predicted path of travel may include a prediction that the hurricane will be in a first location at a first time and in a second location at a second time following the first time. In another example, in some embodiments, a highway patrol service may generate a predicted path of travel of a vehicle by one or more resource data servers 135 analyzing data such as traffic patterns, road maps, Global Positioning System (GPS) coordinates, or the speed and / or direction of the vehicle, or a combination thereof. In these embodiments, the predicted travel path may include a prediction that the vehicle will be at a first location at a first time, followed by a second location at a second time.

[0029] In some embodiments, at least one user device 140 may include devices such as a notebook computer, a tablet, a desktop computer, a mobile phone (e.g., a “smartphone”), a tablet, wearable technology (e.g., a “smart watch”), or the like. In some embodiments, a user, such as an owner and / or custodian of user device 140, may send a workload request from user device 140 to REDM 105, as described in further detail below. In some embodiments, such a user may be a separate, distinct, or unrelated owner and / or custodian of a registered edge device of set of registered edge devices 150, or a combination thereof. In some embodiments, an owner and / or custodian of an edge device may use user device 140 to register the edge device with REDM 105. Such registration may enable REDM 105 to send a workload to the registered edge device in accordance with embodiments of the present disclosure.

[0030] In some embodiments, at least one computing device 145 may include a device such as a computer, a web server, etc. In some embodiments, network 165 may be a wide area network (WAN), a local area network (LAN), the Internet, or an intranet. In some embodiments, network 165 may be substantially similar to or identical to cloud computing environment 50 discussed with respect to FIG.

[0031] 2 is a flow chart diagram of an example method 200 for performing registered edge device management in accordance with an embodiment of the present disclosure. The method 200 may be performed by a REDM, such as the REDM 105 discussed with respect to FIG.

[0032] Returning to FIG. 2, in operation 205, to register the edge device with the REDM, the REDM may obtain device data corresponding to one or more characteristics of the edge device. In some embodiments, the device data may include one or more technical specifications of the edge device. For example, in some embodiments, the device data may include an identification of the device type (e.g., whether the device is a mobile phone, a mounted camera, or an air quality sensor), a brand, or a model number, or a combination thereof. In some embodiments, the device data may specify one or more capture devices to be included with the edge device. For example, the device data for an edge device that is a smartphone may indicate that the smartphone includes a camera and a microphone as capture devices. In some embodiments, the device data may indicate the quality of the data capture of the edge device, such as the range of image resolutions that the camera can capture. In some embodiments, the device data may indicate the processing capabilities of the edge device's data processor, such as the processing speed, image analysis capabilities, or image filtering capabilities, or a combination thereof. In some embodiments, the device data may include information regarding the wireless data transmission rate of the edge device. In some embodiments, the device data may include information regarding the available power or storage capacity of an edge device. For example, in some embodiments, the device data for an edge device such as a smartwatch may include the remaining battery power of the smartwatch, or the size of the available data storage space in the memory of the smartwatch, or both. In some embodiments, the device data may include location and / or orientation information corresponding to the edge device. For example, in an example embodiment, such information may include GPS coordinates and / or a location description (e.g., the camera at 1st and Main, the camera in the North Campus parking lot, etc.).In some embodiments, such information may include a mounting height, or a direction in which an edge device, such as a camera, is facing and / or can be pointed, or a combination thereof. In some embodiments, the device data may include a workload status of the edge device, such as whether the edge device is executing a workload request.

[0033] In some embodiments, the device data may be selected by an owner and / or custodian of an edge device. For example, in some embodiments, such an owner and / or custodian may make a selection regarding the availability of the edge device to receive a workload from the REDM. For example, in some embodiments, such an owner and / or custodian may specify one or more dates, times, or time periods, or combinations thereof, during which the edge device is available to receive a workload from the REDM. For example, a custodian of a set of parking lot cameras may specify that the set of cameras is available to receive a workload from the REDM between 10:00 a.m. and 11:00 a.m. on Tuesdays. In another example, such a custodian may specify that the set of cameras is to receive a workload from the REDM if the workload has an execution time that does not exceed a threshold, such as a 30 second threshold. In some embodiments, an owner and / or custodian of an edge device may specify the urgency to be met that the edge device is to receive a workload from the REDM. For example, in some embodiments, an owner of a doorbell camera may specify that the doorbell camera is not to receive a workload from the REDM unless the workload has an emergency classification. In some embodiments, the owner and / or custodian of an edge device can limit the availability of the edge device according to the entities submitting workload requests to the REDM. For example, in some embodiments, the owner of a security camera can specify that the security camera should not receive workload from entities other than the law enforcement agency that submitted the corresponding workload request to the REDM. In some embodiments, the owner and / or custodian can submit device data indicating that the edge device will not receive workload from the REDM until the owner and / or custodian provides specific consent to such workload, such as by sending a consent notice to the REDM by the user device.

[0034] In some embodiments, operation 205 may include the REDM obtaining device data from one or more edge devices, or from one or more user devices (e.g., user device 140, FIG. 1), or a combination thereof. For example, in some embodiments, an owner and / or custodian of one or more edge devices may select to register such edge devices with the REDM by transmitting device data by such user devices to the REDM. In some embodiments, operation 205 may include the REDM storing the device data in a storage location of the REDM.

[0035] In operation 210, the REDM may obtain a workload request. A workload may refer to a set of tasks for a registered edge device, such as capturing image data, audio data, or sensor data, or a combination thereof (i.e., capture data), processing such captured data, and transmitting such captured data to the REDM. A workload request may refer to a request (e.g., a call, command, or program instructions) sent to the REDM to have one or more registered edge devices perform a workload. In some embodiments, an entity such as a private, individual user, or a government authority may send a workload request to the REDM by a user device (e.g., user device 140, FIG. 1). For example, in some embodiments, the workload request may include a request by a bicycle owner to have available cameras capture images of an area near a bicycle rack where the owner's bicycle is secured. In this example, such images may assist the user in preventing damage or theft of the bicycle. In another example, in some embodiments, the workload request may include a request by a government authority to have available cameras, temperature sensors, and air quality sensors collect images, temperature data, and air quality data of an area of ​​a city after an earthquake occurs. In this example, such captured data could assist government officials in determining where to give aid.

[0036] In some embodiments, the workload request may include various types of data regarding the workload request. For example, in some embodiments, the workload request may include location information of a focal entity, or identifying characteristics of the focal entity, or both. A focal entity may refer to an entity that may be the subject of a workload request, such as a person, an animal, a vehicle, or a weather entity (e.g., a storm, hurricane, typhoon, tornado, etc.). For example, in the example involving bike racks discussed above, the focal entity may include a bike rack or a bike, or both. As discussed in more detail below, in some embodiments, the focal entity may have a predicted travel path.

[0037] In some embodiments, the workload request may include a specification of a type of registered edge device that will perform the workload. For example, in some embodiments, the workload request may specify an audio capture device that will capture audio data, or a mobile phone that will capture images without accompanying audio data. In some embodiments, the workload request may include a brand and / or model number of the registered edge device that will perform the workload. In some embodiments, the workload request may include a specification regarding the quality of data capture of the registered edge device, such as a threshold image resolution for the captured images. In some embodiments, the workload request may include a specification regarding the processing capabilities corresponding to the registered edge device. For example, in some embodiments, the workload request may specify a type of image filter to be applied to the captured images. In some embodiments, the workload request may specify a threshold wireless data transmission rate for the registered edge device. In these embodiments, such a specification may facilitate efficient data transfer when time is critical with respect to obtaining the captured data. In some embodiments, the workload request may include a specification regarding the location and / or orientation of the registered edge device. For example, in some embodiments, the workload request may include a threshold distance from the focal entity at which the set of registered edge devices should be located. In some embodiments, the workload request may include a threshold distance from a predicted travel path at which the set of registered edge devices should be located. In some embodiments, the workload request may include a specification for a date, time, and / or duration at which the registered edge devices should perform the workload. In some embodiments, the workload request may include a specification regarding the urgency of the workload.For example, in some embodiments, an entity sending a workload request to the REDM may classify the workload as a "non-urgent," "urgent," or "urgent" workload, with the "non-urgent" classification being the least urgent and the "urgent" classification being the most urgent. In some embodiments, the workload request may specify the entity submitting the workload request to the REDM.

[0038] In some embodiments, operation 210 may include the REDM obtaining a workload request from a user device (e.g., user device 140, FIG. 1). For example, in some embodiments, an entity may submit a workload request to the REDM via such a user device. In some embodiments, operation 210 may include the REDM storing the workload request in a storage location of the REDM.

[0039] In some embodiments, in operation 215, the REDM may obtain a predicted travel path of the focal entity. In some embodiments, the predicted travel path may include a set of geographic locations where the moving focal entity is predicted to be present and a respective set of times where the moving focal entity is predicted to be present. For example, a predicted travel path of a vehicle traveling on a highway may include the vehicle arriving at a first city at a first time and arriving at a second city at a second time subsequent to the first time. In some embodiments, the REDM may obtain the predicted travel path from a resource data server (e.g., resource data server 135, FIG. 1) of an entity such as a government authority or a company providing data analytics services. In some embodiments, the REDM may obtain the predicted travel path from a user device (e.g., user device 140, FIG. 1). For example, in some embodiments, a user may determine a predicted travel path and transmit it to the REDM by such user device. In some embodiments, operation 215 may include the REDM storing the predicted travel path in a memory location of the REDM.

[0040] In operation 220, the REDM may determine whether one or more conditions corresponding to the workload request are satisfied. In some embodiments, such determination may include the REDM comparing and / or analyzing the data obtained in operations 205-215 to identify whether the corresponding data matches and / or exceeds one or more thresholds. For example, in some embodiments, the REDM may determine that the condition is satisfied in response to identifying that a type of registered edge device (e.g., smartphone) specified in the workload request matches a type of registered edge device (e.g., smartphone) included in the device data of the registered edge device. In some embodiments, the REDM may determine that the condition is satisfied in response to identifying that a location (e.g., GPS coordinates) of the registered edge device specified in the device data of the registered edge device does not exceed a threshold distance from the focal entity or from a predicted travel path of the focal entity.

[0041] In response to determining at operation 220 that one or more conditions corresponding to the workload requirements are met, REDM may proceed to operation 225. Alternatively, in response to determining that such conditions are not met, method 200 may end.

[0042] In operation 225, the REDM may select one or more registered edge devices to which the REDM may send workload for the corresponding workload request. In some embodiments, operation 225 may include the REDM selecting (e.g., identifying) a subset of the registered edge devices from among the set of registered edge devices for which the REDM obtained device data in operation 205.

[0043] In operation 230, the REDM may transmit a workload to one or more registered edge devices selected in operation 225. In some embodiments, operation 230 may include the REDM transmitting data obtained in operation 210 to one or more registered edge devices. In some embodiments, operation 230 may include the REDM issuing a command to one or more registered edge devices to execute a workload. In some embodiments, operation 230 may include the REDM sending a notification to one or more registered edge devices. For example, in an embodiment in which the registered edge device is a smartphone, the REDM may send a text notification to the smartphone requesting that the owner and / or custodian of the smartphone execute a workload by the smartphone, such as capturing a one-minute video recording of a road intersection near the smartphone.

[0044] In operation 235, in some embodiments, the REDM can determine whether there are one or more changes in the data corresponding to the workload request. For example, in embodiments that include a moving focal entity or a set of registered edge devices that capture data within a threshold distance of a predicted path of travel, the location of the focal entity or the predicted location of the focal entity may change over time. In such embodiments, the REDM can determine whether there are one or more changes in the data corresponding to the workload request.

[0045] In response to determining at operation 235 that there are one or more changes in the data corresponding to the workload requests, the REDM may proceed to operation 240. Alternatively, in response to determining that there are no such changes, the REDM may proceed to operation 255.

[0046] At operation 240, the REDM may determine whether one or more conditions corresponding to the workload demands are satisfied. Operation 240 may be the same as or substantially similar to operation 220. Continuing with the example discussed above with respect to capturing data within a threshold distance of a predicted travel path, at operation 240, the REDM may determine that the conditions are satisfied in response to identifying that the location of the registered edge device does not exceed a predicted second location of the traveling focal entity.

[0047] In response to determining at operation 240 that one or more conditions corresponding to the workload requirements are satisfied, the REDM may proceed to operation 245. Alternatively, in response to determining that such conditions are not satisfied, the REDM may proceed to operation 255.

[0048] In operation 245, the REDM may select one or more new registered edge devices to which the REDM may transmit workload for the corresponding workload request. In some embodiments, such new registered edge devices may be different from the one or more registered edge devices selected in operation 225. Operation 245 may be the same as or substantially similar to operation 225.

[0049] In operation 250, the REDM may transmit the workload to the one or more newly registered edge devices selected in operation 245. Operation 250 may be the same as or substantially similar to operation 230.

[0050] In operation 255, in response to transmitting the workload to the one or more registered edge devices, the REDM may receive captured data obtained by the one or more registered edge devices. In some embodiments, operation 255 may include one or more registered edge devices transmitting the captured data to the REDM. In some embodiments, such captured data may include filtered images, such as images having portions obscured to protect the privacy of individuals included in the images. In such embodiments, such images may be filtered by the registered edge devices according to a filter specification included in the workload. Such a workload may have been transmitted to the registered edge devices in operations 230 and / or 250.

[0051] The REDM may transmit the captured data in operation 260. For example, in some embodiments, the REDM may transmit the captured data to a user device of the entity that submitted the workload request in operation 210. In some embodiments, operation 260 may include the REDM deleting the captured data from a memory of the REDM, or from one or more registered edge devices, or both, to facilitate data privacy.

[0052] FIG. 3 is a diagram of an example geographic area 300 in which registered edge device management is implemented, according to an embodiment of the disclosure. In this example, a focal entity 325, such as the eye of a hurricane, may be at a first location 330 at a first time. Further in this example, a weather service may generate a predicted movement path 320 for the hurricane. The predicted movement path 320 may indicate that the hurricane will be at a second location 340 at a second time subsequent to the first time. The predicted movement path 320 may further indicate that the hurricane will be at a third location 350 at a third time subsequent to the second time. Further in this example, an emergency response team may send a workload request to the REDM by a user device. The workload request may specify a first region 335 with a radius of 10 km centered on the first location 330, a second region 345 with a radius of 15 km centered on the second location 340, and a third region 355 with a radius of 15 km centered on the third location 350. The workload request may further specify that cameras in the first region 335, the second region 345, and the third region 355 capture outdoor images. Further in this example, the REDM may identify a set of registered edge devices 305, 310, and 315 that meet the conditions of the workload request. Further in this example, the REDM may transmit a workload corresponding to the workload request to each set of registered edge devices 305, 310, and 315, the workload being transmitted to the set of registered edge devices 310 at a time prior to the second time, and the workload being transmitted to the set of registered edge devices 315 at a time prior to the third time. Further in this example, in response to the transmitted workload, the REDM may receive captured data from the set of registered edge devices 305, 310, and 315. Further in this example, the REDM may transmit the captured data to a user device of an emergency response team. In this manner, the emergency response team may have accurate and comprehensive information that facilitates an effective emergency response.

[0053] 4 is a diagram of representative major components of an exemplary computer system 401 that may be used in accordance with embodiments of the present disclosure. The particular components depicted are presented for illustrative purposes only and do not necessarily require such variations. The computer system 401 may include a processor 410, a memory 420, an input / output interface (also referred to herein as I / O or I / O interface) 430, and a main bus 440. The main bus 440 may provide a communication path for other components of the computer system 401. In some embodiments, the main bus 440 may be connected to other components, such as a specialized digital signal processor (not shown).

[0054] The processor 410 of the computer system 401 may be comprised of one or more CPUs 412. The processor 410 may further include one or more memory buffers or caches (not shown) that provide temporary storage of instructions and data for the CPU 412. The CPU 412 may execute instructions on input provided from the cache or from the memory 420 and output results to the cache or memory 420. The CPU 412 may include one or more circuits configured to perform one or more methods consistent with embodiments of the present disclosure. In some embodiments, the computer system 401 may include multiple processors 410, as is typical for larger systems. However, in other embodiments, the computer system 401 may be a single processor with one CPU 412.

[0055] The memory 420 of the computer system 401 can include a memory controller 422 and one or more memory modules (not shown) for storing data temporarily or permanently. In some embodiments, the memory 420 can include a random access semiconductor memory, a storage device, or a storage medium (either volatile or non-volatile) for storing data and programs. The memory controller 422 can communicate with the processor 410 to facilitate the storage and retrieval of information in the memory module. The memory controller 422 can communicate with the I / O interface 430 to facilitate the storage and retrieval of inputs or outputs in the memory module. In some embodiments, the memory module can be a dual in-line memory module.

[0056] The I / O interface 430 can include an I / O bus 450, a terminal interface 452, a storage interface 454, an I / O device interface 456, and a network interface 458. The I / O interface 430 can connect the main bus 440 to the I / O bus 450. The I / O interface 430 can direct instructions and data from the processor 410 and memory 420 to the various interfaces of the I / O bus 450. The I / O interface 430 can also direct instructions and data from the various interfaces of the I / O bus 450 to the processor 410 and memory 420. The various interfaces can include a terminal interface 452, a storage interface 454, an I / O device interface 456, and a network interface 458. In some embodiments, the various interfaces can include a subset of the aforementioned interfaces (e.g., an embedded computer system in an industrial application may not include a terminal interface 452 and a storage interface 454).

[0057] Logic modules throughout computer system 401 (including but not limited to memory 420, processor 410, and I / O interface 430) may communicate failures or changes to one or more components to a hypervisor or operating system (not shown). The hypervisor or operating system may allocate the various resources available to computer system 401 and track the location of data in memory 420 and processes allocated to the various CPUs 412. In embodiments that combine or reconfigure elements, aspects of the functionality of the logic modules may be combined or redistributed. Such variations will be apparent to those skilled in the art.

[0058] Although this disclosure includes a detailed description of cloud computing, it should be understood that implementation of the teachings presented herein is not limited to a cloud computing environment, rather, embodiments of the present invention may be implemented in conjunction with any other type of computing environment now known or later developed.

[0059] Cloud computing is a service delivery model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with the service provider. The cloud model can include at least five characteristics, at least three service models, and at least four deployment models.

[0060] Its features are as follows:

[0061] On-Demand Self-Service: Cloud consumers can unilaterally provision computing capacity, such as server time and network storage, automatically as needed without requiring human interaction with the provider of the service.

[0062] Broad network access: Functionality is available over the network and accessed through standard mechanisms that facilitate use by heterogeneous thin-client and thick-client platforms (e.g., cell phones, laptops, and PDAs).

[0063] Pooling of resources: Provider computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically allocated and reallocated according to demand. There is a sense of location independence in that consumers generally have no control or information about the exact location of the resources provided, although they may be able to identify the location at a higher level of abstraction (e.g., country, state, or data center).

[0064] Rapid Scalability: Capabilities can be provisioned quickly and scalably, in some cases automatically, quickly scaled out, and quickly released and quickly scaled in. To the consumer, the capabilities available for provisioning often appear unlimited, and any amount can be purchased at any time.

[0065] Metering of Services: Cloud systems automatically control and optimize resource usage by leveraging metering capabilities at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency to both providers and consumers of the services being used.

[0066] The service model is as follows:

[0067] Software as a Service (SaaS): The functionality offered to the consumer is the use of the provider's applications running on a cloud infrastructure. The applications are accessible from a variety of client devices through thin-client interfaces such as web browsers (e.g., web-based email). The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, storage, or even individual application functions, with the exception of limited user-specific application configuration settings.

[0068] Platform as a Service (PaaS): The capability offered to the consumer is to deploy consumer-created or off-the-shelf applications, written using programming languages ​​and tools supported by the provider, onto a cloud infrastructure. The consumer does not manage or control the underlying cloud infrastructure, including networks, servers, operating systems, or storage, but does have control over the deployed applications and, in some cases, the application hosting environment configuration.

[0069] Infrastructure as a Service (IaaS): The capability offered to the consumer is to provision processing, storage, network, and other basic computing resources on which the consumer can deploy and run any software, which may include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure, but does have control over the operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

[0070] The deployment model is as follows:

[0071] Private Cloud: The cloud infrastructure is operated exclusively for one organization. It can be managed by that organization or a third party and can exist on-premise or off-premise.

[0072] Community Cloud: Cloud infrastructure is shared by several organizations to support a particular community with a shared concern (e.g., mission, security requirements, policies, and compliance considerations), which may be managed by the organizations or a third party and can exist on-premise or off-premise.

[0073] Public Cloud: The cloud infrastructure is made available to the general public or large industry organizations and is owned by an organization that sells cloud services.

[0074] Hybrid Cloud: Cloud infrastructure is a combination of two or more clouds (private, community, or public) that remain a unique entity, but are joined by standardized or proprietary technologies that enable data and application portability (e.g., cloud bursting for load balancing between clouds).

[0075] Cloud computing environments are service-oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.

[0076] Referring now to FIG. 5, an exemplary cloud computing environment 50 is depicted. As shown, the cloud computing environment 50 comprises one or more cloud computing nodes 10 that can communicate with local computing devices used by cloud consumers, such as, for example, a personal digital assistant (PDA) or cell phone 54A, a desktop computer 54B, a laptop computer 54C, or an automobile computer system 54N, or combinations thereof. The nodes 10 can communicate with each other. They can be physically or virtually grouped in one or more networks, such as a private, community, public, or hybrid cloud, or combinations thereof, as described herein above (not shown). This allows the cloud computing environment 50 to provide infrastructure, platform, and / or software as a service, for which the cloud consumer does not need to maintain resources on a local computing device. It will be understood that the types of computing devices 54A-N shown in FIG. 5 are intended to be merely exemplary, and that the computing node 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network or over a network-addressable connection (e.g., using a web browser) or both.

[0077] Referring now to Figure 6, a set of functional abstraction layers provided by cloud computing environment 50 (Figure 5) is shown. It should be understood in advance that the components, layers, and functions shown in Figure 6 are intended to be merely exemplary, and embodiments of the present invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

[0078] The hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61, RISC (reduced instruction set computer) architecture based servers 62, servers 63, blade servers 64, storage devices 65, and networks and networking components 66. In some embodiments, software components include network application server software 67, and database software 68.

[0079] The virtualization layer 70 provides an abstraction layer over which the following examples of virtual entities may be provided: virtual servers 71 , virtual storage 72 , virtual networks including virtual private networks 73 , virtual applications and operating systems 74 , and virtual clients 75 .

[0080] In one example, the management layer 80 can provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources utilized to perform tasks within the cloud computing environment. Metering and billing 82 provides cost tracking as resources are utilized within the cloud computing environment and billing or invoicing for the consumption of these resources. In one example, these resources can include application software licenses. Security provides identity verification for cloud consumers and tasks, and protection for data and other resources. User portal 83 provides consumers and system administrators with access to the cloud computing environment. Service level management 84 provides cloud computing resource allocation and management so that required service levels are met. Service level agreement (SLA) planning and fulfillment 85 provides advance agreement and procurement of cloud computing resources in anticipation of future demand according to SLAs.

[0081] The Workload Layer 90 provides examples of functionality for which a cloud computing environment may be utilized. Examples of workloads and functions that may come from this layer include: mapping and navigation 91, software development and lifecycle management 92, virtual classroom instruction delivery 93, data analytics processing 94, transaction processing 95, and registered edge device management logic 96.

[0082] As discussed in more detail herein, it is contemplated that some or all of the operations of some of the method embodiments described herein may be performed in an alternative order, or not performed at all, and further, that multiple operations may occur simultaneously or as an internal part of a larger process.

[0083] The present invention may be a system, method, and / or computer program product at any possible level of technical detail of integration. The computer program product may include a computer-readable storage medium having computer-readable program instructions for causing a processor to perform aspects of the present invention.

[0084] A computer readable storage medium may be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of computer readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded devices such as punch cards or ridge structures in a groove on which instructions are recorded, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as a transitory signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., light pulses passing through a fiber optic cable), or electrical signals transmitted over electrical wires.

[0085] The computer readable program instructions described herein may be downloaded from a computer readable storage medium to an individual computing / processing device or to an external computer or storage device over a network, such as the Internet, a local area network, a wide area network, or a wireless network, or a combination thereof. The network may comprise copper transmission cables, optical transmission fiber, wireless transmission, routers, firewalls, switches, gateway computers, or edge servers, or a combination thereof. A network adapter card or network interface of each computing / processing device receives the computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the individual computing / processing device.

[0086] The computer readable program instructions for carrying out the operations of the present invention may be either source code or object code written in any combination of one or more programming languages, including assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state configuration data, configuration data for integrated circuits, or object oriented programming languages ​​such as Smalltalk®, C++, and procedural programming languages ​​such as the “C” programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer as a standalone software package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be made to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can execute computer readable program instructions to individualize the electronic circuitry by utilizing state information of the computer readable program instructions to perform aspects of the invention.

[0087] Aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer readable program instructions.

[0088] These computer readable program instructions may be provided to a processor of a computer or other programmable data processing apparatus to produce a machine, such that the instructions, which execute on the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams. These computer readable program instructions may also be stored on a computer readable storage medium having instructions stored thereon to instruct a computer, programmable data processing apparatus, or other device, or combination thereof, to function in a particular manner, such that the computer readable storage medium having the instructions stored thereon comprises an article of manufacture including instructions for implementing aspects of the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0089] The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operable steps to create a computer-implemented process such that the instructions, which execute on the computer, other programmable apparatus, or other device, implement the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0090] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block of the flowcharts or block diagrams may represent a module, segment, or portion of instructions, including one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions shown in the blocks may occur out of the order shown in the figures. For example, two blocks shown in succession may actually be performed as one step, or may be executed simultaneously, substantially simultaneously, partially or fully in a time-overlapping manner, or the blocks may sometimes be executed in reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, may be implemented by a special-purpose hardware-based system that performs the specified functions or actions or executes a combination of special-purpose hardware and computer instructions.

[0091] The description of various embodiments of the present disclosure has been presented for illustrative purposes, but is not intended to be exhaustive or limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the described embodiments. The terminology used in this specification is chosen to explain the principles of the embodiments, practical applications, or technical improvements over the art found in the market, or to allow others skilled in the art to understand the embodiments disclosed herein.

[0092] While the present disclosure has been described in particular embodiments, it is anticipated that alterations and modifications thereof will become apparent to those skilled in the art, and it is therefore intended that the following claims be interpreted to cover all such changes and modifications that fall within the true spirit and scope of the present disclosure.

[0093] Any advantages discussed in this disclosure are exemplary advantages, and there may be embodiments of the disclosure that achieve all, some, or none of the advantages discussed while still being within the spirit and scope of the disclosure.

[0094] Hereinafter, a list of non-limiting examples is given to illustrate some aspects of the present disclosure. Example 1 is a computer-implemented method. The method includes obtaining device data of a set of edge devices, obtaining a predicted movement path of a focal entity, and for a first edge device of the set of edge devices, determining a first proximity of the first edge device to the predicted movement path based on the device data, selecting the first edge device based at least in part on the first proximity, transmitting a workload to the first edge device in response to the selecting the first edge device, receiving first captured data captured by the first edge device in response to the transmitting the workload, and transmitting the first captured data to an electronic user device.

[0095] Example 2 includes the method of Example 1 with or without optional features. In this example, the predicted travel path includes a first location and a second location, the focal entity is at the first location at a first time, and the focal entity is predicted to be at the second location at a second time subsequent to the first time, and the computer-implemented method further includes: selecting a second edge device from the set of edge devices based at least in part on a second proximity of the second edge device to the second location, in response to selecting the second edge device, transmitting a workload to the second edge device, in response to transmitting the workload to the second device, receiving second captured data acquired by the second edge device, and transmitting the second captured data to the electronic user device.

[0096] Example 3 includes the method of example 1 or 2, with or without the optional feature, in which the step of transmitting the workload to the second edge device occurs at an intermediate time, and the second time is subsequent to the intermediate time.

[0097] Example 4 includes the method of any one of Examples 1-3, with or without optional features, in which the device data includes additional data selected from the group consisting of image capture resolution, image filtering capabilities, and data transmission rate, and selecting the first edge device is based at least in part on the additional data.

[0098] Example 5 includes the method of any one of Examples 1-4, with or without optional features. In this example, the method includes determining that the first proximity determines that a first distance between the first edge device and the predicted travel path does not exceed a threshold.

[0099] Example 6 includes the method of any one of Examples 1-5, with or without optional features, in which the workload includes instructions to capture one or more images associated with the focal entity, and the first captured data includes the one or more captured images.

[0100] Example 7 includes the method of any one of Examples 1-6, with or without the optional feature, in which the workload further includes an image filter specification, and the one or more captured images include one or more images filtered according to the filter specification.

[0101] Example 8 includes the method of any one of examples 1 to 7, with or without the optional feature. In this example, the focal entity includes a weather entity.

[0102] Example 9 is a computer-implemented method that includes obtaining device data for a set of edge devices, obtaining a predicted movement path of a focal entity, determining for a subset of the set of edge devices based on the device data, the subset being within a threshold distance of the predicted movement path, the subset including a mobile phone and a building mounted camera, in response to the determining step, transmitting a workload to the subset, in response to the transmitting the workload, receiving captured images captured by the subset, and transmitting the captured images to an electronic user device.

[0103] Example 10 is a computer-implemented method, the method including: obtaining device data for a set of edge devices, obtaining a workload request from an electronic user device, obtaining a predicted movement path of a focal entity, determining in response to the workload request that a subset of the set of edge devices is within a threshold distance of the predicted movement path, the subset including a mobile phone and a building mounted camera, in response to the determining step, transmitting a workload corresponding to the workload request to the subset, in response to the workload transmitting step, receiving captured images captured by the subset, and in response to the workload request, transmitting the captured images to the electronic user device.

[0104] Example 11 is a system comprising: one or more processors; and one or more computer-readable storage media storing program instructions that, when executed by the one or more processors, are configured to cause the one or more processors to perform a method as described in any one of Examples 1 to 10.

[0105] Example 12 is a computer program product including one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions being executable by the one or more processors to cause the one or more processors to perform a method as described in any one of Examples 1 to 10.

Claims

1. 1. A computer-implemented method comprising: obtaining device data for a set of edge devices; obtaining a predicted movement path of the focal entity; determining, for a first edge device of the set of edge devices, a first proximity of the first edge device to the predicted travel path based on the device data; selecting the first edge device based at least in part on the first proximity; in response to the selecting step of the first edge device, transmitting a workload to the first edge device; receiving first capture data acquired by the first edge device in response to the transmitting step of the workload; transmitting the first captured data to an electronic user device; 4. A computer-implemented method comprising:

2. the predicted travel path includes a first location and a second location, the focal entity is at the first location at a first time, and the focal entity is predicted to be at the second location at a second time subsequent to the first time; The computer-implemented method further comprises: selecting a second edge device from the set of edge devices based at least in part on a second proximity of the second edge device to the second location; in response to the selecting step of the second edge device, transmitting the workload to the second edge device; receiving second capture data acquired by the second edge device in response to the transmitting of the workload to the second device; and Transmitting the second captured data to the electronic user device. The computer-implemented method of claim 1 , further comprising:

3. 3. The computer-implemented method of claim 2, wherein the transmitting of the workload to the second edge device occurs at an intermediate time, the second time subsequent to the intermediate time.

4. The device data is Image capture resolution, Image filtering capabilities, and Data Transmission Speed and wherein said selecting said first edge device is based at least in part on said additional data.

5. 5. The computer-implemented method of claim 1, wherein the determining the first proximity comprises determining that a first distance between the first edge device and the predicted travel path does not exceed a threshold.

6. the workload includes instructions for capturing one or more images associated with the focal entity; The computer-implemented method of claim 1 , wherein the first captured data includes the one or more captured images.

7. the workload further comprises an image filter specification; The computer-implemented method of claim 6 , wherein the one or more captured images include one or more images that have been filtered according to the filter specification.

8. The computer-implemented method of claim 1 , wherein the focal entity comprises a weather entity.

9. the determining step determines for a subset of the set of edge devices, the subset being within a threshold distance of the predicted travel path, the subset including the first edge device, the subset including a mobile phone and a building mounted camera; the step of transmitting transmits a workload to the subset; the receiving step receives captured images acquired by the subset; The computer-implemented method of claim 1 , wherein the transmitting step transmits the first captured data to the electronic user device.

10. 10. The computer-implemented method of claim 9, further comprising obtaining a workload request from the electronic user device, the determining step being in response to the workload request, and the transmitting step being in response to the workload request.

11. one or more processors; one or more computer-readable storage media storing program instructions, The program instructions, when executed by the one or more processors, cause the one or more processors to: obtaining device data for a set of edge devices; obtaining a predicted movement path of the focal entity; determining, for a first edge device of the set of edge devices, a first proximity of the first edge device to the predicted travel path based on the device data; selecting the first edge device based at least in part on the first proximity; in response to the selecting step of the first edge device, transmitting a workload to the first edge device; receiving first capture data acquired by the first edge device in response to the transmitting step of the workload; transmitting the first captured data to an electronic user device; A system configured to perform a method including:

12. the predicted travel path includes a first location and a second location, the focal entity is at the first location at a first time, and the focal entity is predicted to be at the second location at a second time subsequent to the first time; The system further comprises: selecting a second edge device from the set of edge devices based at least in part on a second proximity of the second edge device to the second location; in response to the selecting step of the second edge device, transmitting the workload to the second edge device; receiving second capture data acquired by the second edge device in response to the transmitting of the workload to the second device; and Transmitting the second captured data to the electronic user device. The system of claim 11 further comprising:

13. 13. The system of claim 12, wherein the transmitting of the workload to the second edge device occurs at an intermediate time, the second time subsequent to the intermediate time.

14. The device data is Image capture resolution, Image filtering capabilities, and Data Transmission Speed and wherein said selecting said first edge device is based at least in part on said additional data.

15. 15. The system of claim 11, wherein the determining the first proximity comprises determining that a first distance between the first edge device and the predicted travel path does not exceed a threshold.

16. the workload includes instructions for capturing one or more images associated with the focal entity; The system of claim 11 , wherein the first captured data includes the one or more captured images.

17. the workload further comprises an image filter specification; The system of claim 16 , wherein the one or more captured images include one or more images filtered according to the filter specification.

18. 18. The system of claim 11, wherein the focal entity includes a weather entity.

19. A computer program product comprising one or more computer-readable storage media and program instructions collectively stored on the one or more computer-readable storage media, the program instructions causing one or more processors to: obtaining device data for a set of edge devices; obtaining a predicted movement path of the focal entity; determining, for a first edge device of the set of edge devices, a first proximity of the first edge device to the predicted travel path based on the device data; selecting the first edge device based at least in part on the first proximity; in response to the selecting step of the first edge device, transmitting a workload to the first edge device; receiving first capture data acquired by the first edge device in response to the transmitting step of the workload; transmitting the first captured data to an electronic user device; a computer program product executable by the one or more processors to cause the one or more processors to perform a method comprising:

20. the predicted travel path includes a first location and a second location, the focal entity is at the first location at a first time, and the focal entity is predicted to be at the second location at a second time subsequent to the first time; The method further comprising: selecting a second edge device from the set of edge devices based at least in part on a second proximity of the second edge device to the second location; in response to the selecting step of the second edge device, transmitting the workload to the second edge device; receiving second capture data acquired by the second edge device in response to the transmitting of the workload to the second device; and Transmitting the second captured data to the electronic user device.

20. The computer program product of claim 19, further comprising:

21. 21. The computer program product of claim 20, wherein the transmitting of the workload to the second edge device occurs at an intermediate time, the second time subsequent to the intermediate time.

22. The device data is Image capture resolution, Image filtering capabilities, and Data Transmission Speed and wherein said selecting said first edge device is based at least in part on said additional data.

23. 23. The computer program product of claim 19, wherein the determining the first proximity comprises determining that a first distance between the first edge device and the predicted travel path does not exceed a threshold.

24. the workload includes instructions for capturing one or more images associated with the focal entity; 24. The computer program product of claim 19, wherein the first captured data comprises the one or more captured images.

25. 25. A computer program product as claimed in any one of claims 19 to 24, wherein the focal entity comprises a weather entity.

26. 1. A computer-implemented method comprising: obtaining device data for a set of edge devices; obtaining a predicted movement path of the focal entity; determining, for a subset of the set of edge devices, based on the device data, that the subset is within a threshold distance of the predicted travel path, the subset including mobile phones and cameras mounted on buildings; in response to said determining step, transmitting a workload to the subset; receiving captured images acquired by the subset in response to the transmitting step of the workload; transmitting the captured image to an electronic user device; 4. A computer-implemented method comprising:

27. 1. A computer-implemented method comprising: obtaining device data for a set of edge devices; obtaining a workload request from an electronic user device; obtaining a predicted movement path of the focal entity; determining, in response to the workload request, that a subset of the set of edge devices is within a threshold distance of the predicted travel path, the subset including a mobile phone and a building mounted camera; in response to the determining step, transmitting a workload corresponding to the workload request to the subset; receiving captured images acquired by the subset in response to the transmitting step of the workload; transmitting the captured image to the electronic user device in response to the workload request; 4. A computer-implemented method comprising:

28. A computer program comprising program code means arranged to carry out the method of any of claims 1 to 10, or claim 26 or claim 27, when said program is run on a computer.

Images