Energy-Efficient Topology Control Mechanism for IoT-Oriented Software-Defined WSNs

Abstract

In time-varying software-defined wireless sensor networks (SDWSNs) for Internet-of-Things (IoT) applications, the topology may change due to the interference or abnormal events, thus leading to network performance degradation. In this paper, an energy-efficient topology control (TC) mechanism applied for IoT-oriented SDWSNs is proposed to maximize the network energy efficiency (EE) during the dynamic topology maintenance. First, a hierarchical SDWSN architecture consisting of the cluster-based sensing network and the programmable relay network is presented. Second, two TC algorithms based on the link EE are proposed to apply in the cluster and relay sub-networks of SDWSN, respectively. In the cluster sub-network, the proposed distributed TC algorithm enables the link interference mitigation by employing power control and rate allocation in each cluster. In the relay sub-network, the proposed centralized TC algorithm first utilizes a specified model to construct the original topology. During the dynamic topology maintenance, the proposed centralized TC algorithm is realized by the value-iteration learning method based on a Markov decision process (MDP) model, upon which the state-transition probability (STP) of the relay sub-network is obtained, where the relay-network state is composed of the link, the queue, and the residual energy ratio states for all nodes in the relay sub-network. Finally, simulation results show that both two TC algorithms can improve the corresponding sub-network EE of time-varying SDWSN.