EDCCS: effective deterministic clustering scheme based compressive sensing to enhance IoT based WSNs

The problem of Data acquisition in large distributed Wireless Sensor Networks (WSNs) scale is a hindrance in the growth of the Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted great interest from researchers. An open question of this approach is how to effectively integrate these technologies for specific tasks. The objective of this paper is two parts. First, we propose an effective deterministic clustering scheme based CS technique (EDCCS) for data collection in IoT based homogeneous and heterogeneous WSN to deal with the data acquisition problem, reduce the consumption of energy and increase the lifetime of network. Second, we propose random matching pursuit (RMP) as an effective CS reconstruction algorithm to improve the recovery process by reducing the error average at the base station (BS). The simulation results show that our proposed novel EDCCS scheme reduces at least 60% of the average power consumption and increases the network lifetime at least 1.3 times of the other schemes in homogeneous network while, it increases the network lifetime and residual energy by 1.9 times and 1.3 times respectively, compared to the other schemes in heterogeneous network. Also, our proposed RMP algorithm reduces the error average of reconstruction at least 35% compared to other reconstruction algorithms.