Repeated game theory-based reducer selection strategy for energy management in SDWSN

Abstract

The sensor-generated data by Internet of Things are considered to be the most common source of big data. A wide range of applications are relying on these data for analytics. While a considerable amount of data is sufficient for the application users to get valuable insights, sending vast amount of data to the cloud seems inappropriate and it only increases the communication cost in the network. It is well-known that an increase in communication cost increases energy depletion in the network. Since sensor nodes have a restricted power supply, it is necessary to harness the energy of nodes to prolong the network lifetime. In this paper, a solution for energy management of sensor nodes is proposed by integrating the software defined framework with the sensor network, software defined wireless sensor networks (SDWSN), that aids in processing the data inside the network before transferring it to the sink node. To this context, a game model has been formulated for selecting the appropriate nodes as reducers which will execute the reducer function. The software defined network (SDN) controller, geographically placed outside of the wireless sensor network, is responsible for selecting the reducers and dynamically load reducing function on them. Based on the selection, a routing protocol, routing via respective reducer (RVRR), that forwards data packets via in-network processing path and control packets via common path has been proposed. This remarkably reduces the communication cost, thereby prolonging the lifetime of the deployed network. The RVRR algorithm is implemented in NS-3 simulator to evaluate the performance of proposed work in SDWSN environment.