Abstract: A method and device for execution of deep neural network (DNN) in an internet of things (IoT) edge network are provided. In an embodiment, at least one edge device within communication range of an IoT device are selected. Further, a network for connecting the IoT device with the at least one selected edge device is identified. A split ratio is determined based on an inference time of the DNN and a transmission time required for transmitting output of each layer of DNN from the IoT device to the selected at least one edge device. Finally, a plurality of layers of the DNN are split into a first part and a second part based on the split ratio, and the second part is transmitted to the selected at least one edge device through the identified network. The first part is executed on the IoT device, and the second part is executed on the selected at least one edge device.

Abstract: A method of distributing throughput intelligently amongst a plurality of applications residing at a User Equipment (UE) is provided. The method includes receiving, at the UE, recommended bit rate (RBR) information from a network node, the RBR information indicating a throughput value allocated to the UE, allocating a codec rate from the allocated throughput value to at least one voice over internet protocol (VoIP) application from the plurality of applications, and allocating, from remaining throughput value of the allocated throughput value, a bit rate to each of a plurality of non-VoIP applications from the plurality of applications, based on corresponding throughput requirement associated with the plurality of non-VoIP applications.

Abstract: Provided is a method for adaptively streaming an artificial intelligence (AI) model file, including determining a capability of a first electronic device and a capability of a second electronic device, network information associated with the first and second electronic devices, and AI model information associated with the AI model file; determining to adaptively stream the AI model file based on the determined capabilities and information; pre-processing the AI model file; and adaptively streaming the AI model.

Abstract: A method and system of task management in an internet of things (IoT)-edge network may be provided. The method may include assigning at least one deep neural network (DNN) task from an IoT device to a first edge device; determining whether the first edge device satisfies one of a first predetermined criteria and a second predetermined criteria during execution of the at least one DNN task; triggering an alarm to the IoT device based on the determination; identifying a second edge device subsequent to the triggering of the alarm; determining whether to transfer the at least one DNN task to the second edge device or to execute the at least one DNN task on the first edge device, based on determining that the first device satisfies the second predetermined criteria; and transferring the at least one DNN task to the second edge device, based on determining that the first device satisfies the first predetermined criteria.

Abstract: A method and device for execution of deep neural network (DNN) in an internet of things (IoT) edge network are provided. In an embodiment, at least one edge device within communication range of an IoT device are selected. Further, a network for connecting the IoT device with the at least one selected edge device is identified. A split ratio is determined based on an inference time of the DNN and a transmission time required for transmitting output of each layer of DNN from the IoT device to the selected at least one edge device. Finally, a plurality of layers of the DNN are split into a first part and a second part based on the split ratio, and the second part is transmitted to the selected at least one edge device through the identified network. The first part is executed on the IoT device, and the second part is executed on the selected at least one edge device.

Abstract: A method of distributing throughput intelligently amongst a plurality of applications residing at a User Equipment (UE) is provided. The method includes receiving, at the UE, recommended bit rate (RBR) information from a network node, the RBR information indicating a throughput value allocated to the UE, allocating a codec rate from the allocated throughput value to at least one voice over internet protocol (VoIP) application from the plurality of applications, and allocating, from remaining throughput value of the allocated throughput value, a bit rate to each of a plurality of non-VoIP applications from the plurality of applications, based on corresponding throughput requirement associated with the plurality of non-VoIP applications.

Abstract: In the field of Internet of Things understanding need of applications and translating them to network parameters and protocol parameters is a major challenge. This disclosure addresses problem of enabling network services by cognitive sense-analyze-decide-respond framework. A processor implemented method is provided for enabling network aware applications and applications aware networks by a sense analyze decide respond (SADR) framework.

Abstract: This disclosure relates generally to a method and system for multi-hop path selection for mobile robots based on cloud platform providing an optimal path for end-to-end communication in the multi-hop network. Multi-hop path selection for mobile robots, conventionally performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. Further, computation at mobile robot end is not an energy efficient solution. The disclosed cloud system communicates the optimal path to the source mobile robot to reach the destination mobile robot through the plurality of Access Points (APs). Multi-hop path selection for mobile robots, currently performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. The optimal path computed at cloud system increases the life-time of robotics network there by increasing the efficiency.

Abstract: This disclosure relates generally to a method and system for multi-hop path selection for mobile robots based on cloud platform providing an optimal path for end-to-end communication in the multi-hop network. Multi-hop path selection for mobile robots, conventionally performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. Further, computation at mobile robot end is not an energy efficient solution. The disclosed cloud system communicates the optimal path to the source mobile robot to reach the destination mobile robot through the plurality of Access Points (APs). Multi-hop path selection for mobile robots, currently performed at mobile robot end, may not provide an optimal path as mobile robots are unaware of the global scenario of the multi-hop network. The optimal path computed at cloud system increases the life-time of robotics network there by increasing the efficiency.

Abstract: In the field of Internet of Things understanding need of applications and translating them to network parameters and protocol parameters is a major challenge. This disclosure addresses problem of enabling network services by cognitive sense-analyze-decide-respond framework. A processor implemented method is provided for enabling network aware applications and applications aware networks by a sense analyze decide respond (SADR) framework.

Abstract: Robotic applications are important in both indoor and outdoor environments. Establishing reliable end-to-end communication among robots in such environments are inevitable. Many real-time challenges in robotic communications are mainly due to the dynamic movement of robots, battery constraints, absence of Global Position System (GPS), etc. Systems and methods of the present disclosure provide assisted link prediction (ALP) protocol for communication between robots that resolves real-time challenges link ambiguity, prediction accuracy, improving Packet Reception Ratio (PRR) and reducing energy consumption in-terms of lesser retransmissions by computing link matrix between robots and determining status of a Collaborative Robotic based Link Prediction (CRLP) link prediction based on a comparison of link matrix value with a predefined covariance link matrix threshold. Based on determined status, robots either transmit or receive packet, and the predefined covariance link matrix threshold is dynamically updated.

Abstract: Internet of Things (IoT) devices (101A) continuously capture raw data over a regular interval of time. The captured raw data is transmitted to gateway devices (101B) deployed in an environment, for example, a warehouse. Continuous transmission of such data leads to data redundancy, continuous channel utilization and bandwidth usage, etc. To overcome this problem, present disclosure implements a Compressive Sensing based Data Prediction (CS-DP) model that predicts data at the gateway devices by learning the data pattern received from IoT devices, estimates and computes, using a Compressive Sensing based Data Estimation (CS-DE) model, optimal data instead of considering the overall data captured at the gateway devices and reconstructs, using a Compressive Sensing based Data Reconstruction (CS-DR) model, missing data and/or corrupted data using the partial information received at the gateway devices.

Abstract: This disclosure relates generally to distributed robotic networks, and more particularly to communication link-prediction in the distributed robotic networks. In one embodiment, robots in a robotic network, which are mobile, can establish communication with a cloud network through a fog node, wherein the fog node is a static node. A robot can directly communicate with a fog node (R2F) if the fog node is in the communication range of the robot. If there is no fog node in the communication range of the robot, then the robot can establish communication with another robot (R2R) and indirectly communicate with the fog node through the connected robot. Communication link prediction is used to identify one or more communication links that can be used by a robot for establishing communication with the cloud network. A link that satisfies requirements in terms of link quality and any other parameter is used for communication purpose.

Abstract: Internet of Things (IoT) devices (101A) continuously capture raw data over a regular interval of time. The captured raw data is transmitted to gateway devices (101B) deployed in an environment, for example, a warehouse. Continuous transmission of such data leads to data redundancy, continuous channel utilization and bandwidth usage, etc. To overcome this problem, present disclosure implements a Compressive Sensing based Data Prediction (CS-DP) model that predicts data at the gateway devices by learning the data pattern received from IoT devices, estimates and computes, using a Compressive Sensing based Data Estimation (CS-DE) model, optimal data instead of considering the overall data captured at the gateway devices and reconstructs, using a Compressive Sensing based Data Reconstruction (CS-DR) model, missing data and/or corrupted data using the partial information received at the gateway devices.

Abstract: Robotic applications are important in both indoor and outdoor environments. Establishing reliable end-to-end communication among robots in such environments are inevitable. Many real-time challenges in robotic communications are mainly due to the dynamic movement of robots, battery constraints, absence of Global Position System (GPS), etc. Systems and methods of the present disclosure provide assisted link prediction (ALP) protocol for communication between robots that resolves real-time challenges link ambiguity, prediction accuracy, improving Packet Reception Ratio (PRR) and reducing energy consumption in-terms of lesser retransmissions by computing link matrix between robots and determining status of a Collaborative Robotic based Link Prediction (CRLP) link prediction based on a comparison of link matrix value with a predefined covariance link matrix threshold. Based on determined status, robots either transmit or receive packet, and the predefined covariance link matrix threshold is dynamically updated.

Abstract: This disclosure relates generally to distributed robotic networks, and more particularly to communication link-prediction in the distributed robotic networks. In one embodiment, robots in a robotic network, which are mobile, can establish communication with a cloud network through a fog node, wherein the fog node is a static node. A robot can directly communicate with a fog node (R2F) if the fog node is in the communication range of the robot. If there is no fog node in the communication range of the robot, then the robot can establish communication with another robot (R2R) and indirectly communicate with the fog node through the connected robot. Communication link prediction is used to identify one or more communication links that can be used by a robot for establishing communication with the cloud network. A link that satisfies requirements in terms of link quality and any other parameter is used for communication purpose.