Abstract: A method and a system is disclosed herein for co-operative on-path and off-path caching policy for information centric networks (ICN). In an embodiment, a computer implemented method and system is provided for cooperative on-path and off-path caching policy for information centric networks in which the edge routers or on-path routers optimally store the requested ICN contents and are supported by a strategically placed central off-path cache router for additional level of caching. A heuristic mechanism has also been provided to offload and to optimally store the contents from the on-path routers to off-path central cache router. The present scheme optimally stores the requested ICN contents either in the on-path edge routers or in strategically located off-path central cache router. The present scheme also ensures optimal formulation resulting in reduced cache duplication, delay and network usage.

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: In LTE Random Access Channel (RACH) mechanism, devices use slotted ALOHA based protocol for RACH message exchange. During these messages exchange, if a device does not get a response from a base-station (BS), the device assumes that it is not able to reach base station due to insufficient transmission power and increases transmit power to reach to the base station. However, at higher density most of requests are lost due to collision. In existing RACH procedure, device unnecessarily ramps power in next RACH process which leads to power wastage in already resource constrained device. When there is failure of reception of RACH process, the present disclosure computes time delays (TD) based on a RSSI value obtained from a message transmitted by the BS, and initiates RACH process accordingly. The embodiments further enable requests transmission from device to BS by ramping power of the devices based on the computed TD.

Abstract: This disclosure relates generally to telecommunication networks, and more particularly to a method and system for delay aware uplink scheduling in a communication network. The telecommunication network performs data uplink and data downlink associated with data communication between multiple transmitter-receiver pairs connected to the network, by using specific scheduling schema as part of the data processing. Disclosed are method and system for performing a delay-aware uplink scheduling in the communication network. For an uplink request received from a transmitter, the system estimates downlink delay at the corresponding receiver side. The system further estimates a processing delay for the received uplink request. Based on the estimated downlink delay and the processing delay, the system determines an uplink delay budget for the transmitter. Further, based on the uplink delay budget and an achievable data rate computed for the transmitter, the system schedules uplink for the transmitter.

Abstract: This disclosure relates to managing Fog computations between a coordinating node and Fog nodes. In one embodiment, a method for managing Fog computations includes receiving a task data and a request for allocation of at least a subset of a computational task. The task data includes data subset and task constraints associated with at least the subset of the computational task. The Fog nodes capable of performing the computational task are characterized with node characteristics to obtain resource data associated with the Fog nodes. Based on the task data and the resource data, an optimization model is derived to perform the computational task by the Fog nodes. The optimization model includes node constraints including battery degradation constraint, communication path loss constraint, and heterogeneous computational capacities of Fog nodes. Based on the optimization model, at least the subset of the computational task is offloaded to a set of Fog nodes.

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: The present application provides a method and system for sharing of unlicensed spectrum. The disclosed method and system when implement improves the spectral efficiency of LTE users and also improves the overall performance of LAA and Wi-Fi users. A BS senses the channel for any ongoing transmissions for a CCA period which is equal to DIFS time period of Wi-Fi. If the channel is busy (CCA-busy), it enters into back-off stage by selecting a uniform random number from [0, C0?1] as the back-off counter, where C0 is the fixed congestion window size. If the channel is free for a CCA period (CCA-idle), the back-off counter gets decremented by unity until it reaches zero. Once the back-off counter reaches zero, LAA again senses and schedules its down-link transmissions for a maximum channel occupancy period, provided the channel is free.

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: Conventional protocols for live media streaming are not lightweight and hence not suitable for constrained video transmitting devices. The protocols are poor in terms of delay performance under lossy conditions and need to maintain a lot of states at the constrained transmitting end leading to load on the memory and draining energy of the devices. The conventionally used protocols do not perform well for intermittent connectivity. Usually the existing streaming solutions act either in completely reliable manner, using reliable transport protocol like TCP, or in completely unreliable manner using best effort unreliable transport protocol like UDP. The present disclosure provides a single streaming solution which can change the protocol semantics and maintains a balance between reliability and delay-performance, thereby optimizing the overall system goodput. The protocol does this intelligently by inferring the criticality of the segment in flight and enable live video streaming for Internet of Things (IoT).

Abstract: This disclosure relates generally to provisioning network services in a cloud computing environment, and more particularly to framework for provisioning network services in a heterogeneous cloud computing environment. In one embodiment, the disclosure includes a network as a service (NaaS) layer under a cloud provisioning platform. The NaaS layer can be interfaced with any cloud provisioning platform. The NaaS layer serves the networking needs of the heterogeneous cloud environment. It provides network services like monitoring, notifications, QoS policies, network topology and other services. For example, the cloud provisioning platform defines a virtual network and attaches a plurality of virtual machines to it. All the communications related to creation/deletion/update of virtual networks, virtual subnets, virtual ports, virtual router, virtual interfaces etc., are sent to the NaaS layer.

Abstract: A method and system is provided for distributed optimal caching for information centric networking. The system and method disclosed herein enables each router/node in the network to make an independent decision to solve the optimization problem based upon a cost feedback from its neighbors. Content is received by a first router which determines if it should store the content in its cache based on a Characterizing Metrics (CM) value or send it to a neighbor router j, where the neighbor router j is selected based on a transaction cost determination. The node j on receiving the content shared with itself again performs similar computation to determine if the content should be stored in its cache. The method is performed iteratively for optimal distributed caching.

Abstract: This disclosure relates generally to autonomous devices, and more particularly to method and system to optimally allocate warehouse procurement tasks to distributed autonomous devices. The method includes obtaining, at a coordinating agent, a global task associated with the warehouse and information associated with the robotic agents. The information includes a count and status of the robotic agents. The global task is profiled to obtain a set of sub-tasks and constraints associated with the set of sub-tasks are identified. The constraints include utilization constraint and/or pricing constraints. A distributed, decentralized optimal task allocation is performed amongst the robotic agents based on constraints to obtain optimal performance of robotic agents.

Abstract: This disclosure relates generally to autonomous devices, and more particularly to method and system to optimally allocate warehouse procurement tasks to distributed autonomous devices. The method includes obtaining, at a coordinating agent, a global task associated with the warehouse and information associated with the robotic agents. The information includes a count and status of the robotic agents. The global task is profiled to obtain a set of sub-tasks and constraints associated with the set of sub-tasks are identified. The constraints include utilization constraint and/or pricing constraints. A distributed, decentralized optimal task allocation is performed amongst the robotic agents based on constraints to obtain optimal performance of robotic agents.

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: The present disclosure is related to a system and method for providing aerial communication services to users trapped in disaster conditions and need immediate attention. It includes an UAV mounted with a central base station to establish communication services with each communication equipment by modeling of an emergency communication network. It analyzes utility function of criticalities to assure an efficient resource allocation mechanism where critical users get preference over non-critical users. The users can be in a critical state either due to low remaining energy of at least one communication equipment of the one or more users or because of the criticality due to their physical surroundings and data rate component to ensure throughput for the communication services. An assisted global positioning system (A-GPS) is being used for obtaining information of physical criticality of the users distributed over a geographical area.

Abstract: System(s) and method(s) for designing a network of one or more entities in an enterprise is disclosed. A design type along with configurable design parameters is selected from a list of designs. Requirements associated with design of entities are collected from the users. The requirements and configurable design parameters are analyzed to obtain analysis results. Based on at least one of a layer-wise requirement and distribution or a zone-wise requirement and distribution, network devices and modules associated with the entities are determined. One or more designs are generated for the network of entities based on the layer-wise requirement and distribution or the zone-wise requirement and distribution of network devices and modules associated with the entities in the enterprise.

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: In order to make use of computational resources available at runtime through fog networked robotics paradigm, it is critical to estimate average performance capacities of deployment hardware that is generally heterogeneous. It is also not feasible to replicate runtime deployment framework, collected sensor data and realistic offloading conditions for robotic environments. In accordance with an embodiment of the present disclosure, computational algorithms are dynamically profiled on a development testbed, combined with benchmarking techniques to estimate compute times over the deployment hardware. Estimation in accordance with the present disclosure is based both on Gustafson's law as well as embedded processor benchmarks. Systems and methods of the present disclosure realistically capture parallel processing, cache capacities and differing processing times across hardware.

Abstract: In LTE Random Access Channel (RACH) mechanism, devices use slotted ALOHA based protocol for RACH message exchange. During these messages exchange, if a device does not get a response from a base-station (BS), the device assumes that it is not able to reach base station due to insufficient transmission power and increases transmit power to reach to the base station. However, at higher density most of requests are lost due to collision. In existing RACH procedure, device unnecessarily ramps power in next RACH process which leads to power wastage in already resource constrained device. When there is failure of reception of RACH process, the present disclosure computes time delays (TD) based on a RSSI value obtained from a message transmitted by the BS, and initiates RACH process accordingly. The embodiments further enable requests transmission from device to BS by ramping power of the devices based on the computed TD.

Abstract: A method and system is provided for device-to-device (D2D) offloading in long term evolution (LTE) networks. The present application provides a method and system for device-to-device (D2D) offloading in long term evolution (LTE) networks, comprising processor implemented steps of selecting an offloader by a eNodeB (eNB) for a user device out of a plurality of user devices based on location of the user device and other closed proximity user devices, corresponding load and channel conditions upon receiving the offloading request from the user device; exchanging a control messages between the user device and the eNB; and between the eNB and the offloader; and scheduling of resource blocks (RBs) by the eNB for the user device and the offloader in D2D offloading.