FACS: Fairness aware clustering scheme for monitoring applications of internet of things based wireless sensor networks

Energy conservation is one of the main challenges hindering the advancement of Wireless Sensor Networks (WSNs) in the Internet of Things (IoT). Although various approaches including probabilistic, deterministic, and mixed model based solutions have been proposed to solve the issue of energy conservation in cluster based WSNs, most of them do not consider the node’s heterogeneity in terms of energy as well as transmission rate during the creation or selection of clusters. In this paper, a new Fairness Aware Clustering Scheme (FACS) for heterogeneous WSN is proposed, where we consider heterogeneity of the WS N in terms of transmission rate and energy. In contrast to existing schemes, the proposed FACS provides a set of polices that take care of round operations and improve the network performance and lifetime. In FACS, fuzzy logic is employed to benefit from the heterogeneity of the nodes and to determine the utility of each node. Through extensive simulation, we show that FACS performs efficiently and it outperforms the existing state-of-the-art approaches in terms of network lifespan, energy, and fairness.