Deterministic clustering based compressive sensing scheme for fog-supported heterogeneous wireless sensor networks

Abstract

Data acquisition problem in large-scale distributed Wireless Sensor Networks (WSNs) is one of the main issues that hinder the evolution of Internet of Things (IoT) technology. Recently, combination of Compressive Sensing (CS) and routing protocols has attracted much attention. An open question in this approach is how to integrate these techniques effectively for specific tasks. In this paper, we introduce an effective deterministic clustering based CS scheme (DCCS) for fog-supported heterogeneous WSNs to handle the data acquisition problem. DCCS employs the concept of fog computing, reduces total overhead and computational cost needed to self-organize sensor network by using a simple approach, and then uses CS at each sensor node to minimize the overall energy expenditure and prolong the IoT network lifetime. Additionally, the proposed scheme includes an effective algorithm for CS reconstruction called Random Selection Matching Pursuit (RSMP) to enhance the recovery process at the base station (BS) side with a complete scenario using CS. RSMP adds random selection process during the forward step to give opportunity for more columns to be selected as an estimated solution in each iteration. The results of simulation prove that the proposed technique succeeds to minimize the overall network power expenditure, prolong the network lifetime and provide better performance in CS data reconstruction.