Minimizing the maximum receiver interference in wireless sensor networks using probabilistic interference model

Abstract

Now in the era of the Internet of Energy (IoE), researchers are more focused on optimal energy utilization. In the wireless sensor network, maximization of battery lifetime or network lifetime is one of the primary research objectives. There are many techniques available to enhance the network lifetime, and interference minimization is one of them. Interference minimization leads to less transmission power consumption in wireless sensor networks and thus enhances the network lifetime. The interference minimization is proven to be NP-Hard problem. In this paper, we have proposed a method to minimize receiver interference using different encoding schemes and genetic algorithm. We have used more realistic probabilistic interference model to calculate receiver interference instead of the graph-based model used in most of the literature. The Genetic Algorithm used to minimize receiver interference uses three different chromosome representation schemes, namely Prüfer code, Edge-set, and Edge-window-decoder. We have used benchmark data sets and special cases like exponential node chain, two exponential node chain, spiral model, one cluster, and two-cluster for experimental simulations. Our proposed algorithm outperforms other algorithms available in the literature like MI-S (Minimizing Interference in Sensor networks), MinMax-RIP (Minimizing Maximum Receiver Interference Problem), and MST (Minimum Spanning Tree: Prim's algorithm) in terms of minimizing maximum receiver interference in the network.