Energy-aware routing in wireless sensor networks using MLP and simulated annealing algorithms

The main purpose of wireless sensor networks (WSNs) is to monitor, record and announce specific conditions from different locations and different applications to the well node or end user. Wireless sensor networks have many applications such as patient status monitoring, target tracking, forest and rangeland monitoring, battlefield, and so on. In these networks, energy is one of the inherent limitations. Because the energy consumed is supplied by a battery, which has a limited lifespan. Clustering is one of the best ways to save energy due to data aggregation, and selecting the right clusters increases the lifespan of wireless sensor networks. Since clustering is one of the NP-hard problems, metaheuristic algorithms are suitable for this problem. In this paper, an energy-aware and cluster-based routing method for WSNs with a combination of multilayer perceptron (MLP)neural network algorithm and simulated annealing (SA) is presented. In the proposed method, the simulated annealing metaheuristic algorithm is simulated to determine the cluster head (CH) and multilayer perceptron neural networks are used to determine the members of each cluster. After the clustering process, data is sent from the source node to the well by creating appropriate routing tables among the headers. The simulation results of the proposed method show that this method improves the parameters of energy consumption, package delivery rate and throughput.