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:
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:
Optimization of control plane in a software defined network includes obtaining peer information of at least one neighbouring network controller by a network controller and determining a traffic profile variation. The method further includes computing of a self payoff value indicative of one of optimum utilization, underutilization and overutilization of the network controller. The method further includes initiating a non-zero sum game based network control plane optimization operation based on the self payoff value and the traffic profile of the neighbouring network controllers, and may include one of activating additional network controller(s), transferring control of one or more network devices managed by the network controller(s) to a neighbouring greedy network controller, deactivating the network controller, and transferring control of one or more additional network devices managed by the neighbouring network controller(s) to the greedy network controller.
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. The processor implemented method includes sensing, at least one application parameter and at least one network parameter to obtain a plurality of sensed information; analyzing, the plurality of sensed information is filtered and synchronized to generate a plurality of derived parameters; determining, a plurality of rules based on the plurality of derived parameters; validating, the plurality of rules for a plurality of scenarios to obtain plurality of decisions; and enabling, at least one of (i) network, (ii) application and (iii) protocol control based on the plurality of decisions.
Abstract:
The present application provides a method and system for optimal caching of content in the Information Centric Networks (ICN) and a cache replacement based on a content metric value. The method and system comprises requesting for a plurality of content by a user to a nearest local or edge ICN cache router; delivering by the local or edge ICN cache router the requested plurality of content to the user if it is available in its cache; else forwarding the request for the plurality of content to any of intermediate ICN cache router for finding source of the requested plurality of content; downloading the plurality of content in its downstream path; and storing the downloaded plurality of content based on a content metric value derived by a content metric system (CMS) based on a plurality of network parameters for the requested plurality of content.
Abstract:
Methods and systems for providing requested content to a user device in an Overlay Information Centric Network (O-ICN) architecture are disclosed herein. The method may include receiving a request for accessing content, from the user device. The request is routed to the ICN manager from an ICN router. The method may also include parsing the request to determine whether the request is an ICN based request. The ICN-based request is associated with a flag. Further, the method includes based on the determination, identifying at least one network entity hosting the requested content. The identification is based on a name of the content. The method also includes sending a notification to the at least one identified network entity for providing the requested content to the user device.
Abstract:
A method for uplink scheduling over a communication channel in a communication network including at least one UE and an eNodeB, is described. The method comprises determining whether the UE is associated with at least one of Guaranteed Bit Rate (GBR) bearers and non-Guaranteed Bit Rate (non-GBR) bearers. Based on the determining, for each of the GBR-bearers and the non-GBR-bearers, computing a demand for resources for establishing an uplink communication, wherein the demand is computed based physical layer characteristics and transport layer characteristics associated with the communication channel. The demand computed is communicated as a request message to the eNodeB. In response to the request message, receiving an allocation of the resources for uplink scheduling from the eNodeB.
Abstract:
This disclosure relates to method and system for improving Wi-Fi performance in co-existing communication networks using learning methodologies. In recent times, most of telecom operators have expressed interest in deploying LTE (Long-Term Evolution) over the unlicensed spectrum. However, simultaneous use of unlicensed band (by operators using LTE and other Wi-Fi) presents coexistence challenges in terms of network performance especially for the Wi-Fi. The disclosed techniques enable improving the Wi-Fi performance in the co-existing communication networks based on learning methodologies. The disclosed techniques improve Wi-Fi performance based on several steps that includes detecting an interfering channel, and further identifying an optimal channel to mitigate the interference caused by the detected interfering channel. The optimal channel is identified based on an optimization technique, wherein the optimization technique is a reinforcement learning technique based on a Q-learning.
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).