An Energy Efficient Cluster Head Selection Approach For Performance Improvement In Network-Coding-Based Wireless Sensor Networks With Multiple Sinks

The limited power source of sensors is the main constraint of Wireless Sensor Networks (WSNs); therefore, some energy management techniques are required. Network coding and topology control techniques have received extensive attention to decrease energy consumption and improve network performance. Although, combining these two techniques in WSNs can reduce energy consumption efficiently, their main drawback is the computation time, which grows exponentially by increasing nodes; this is not a convenient case for large-scale networks. In this paper, we utilize clustering method as a well-known topology control technique to overcome the mentioned drawback. The initial probability of cluster head selection is critical in distributed clustering algorithms. The results show that finding an appropriate probability provides a robust and fast alternative for the optimization approaches that are sensitive to the initial guess. Hence, we determine a near-optimal probability for cluster head selection to reach the maximum efficiency in the energy consumption. This probability is specified in terms of the number of nodes in the network and the distance of each node from its nearest neighbor provided that the neighbor lies within an angle of the source-destination axis. Our clustering method is based on learning automata and a sleep-awake mechanism to improve results. Accordingly, a routing algorithm along with an optimization problem is developed, which is called inter cluster subgraph selection. Simulation results show that the proposed approach is suitable for large-scale WSNs. Moreover, we demonstrate that the performance of the proposed approach, in terms of energy consumption and network lifetime, is more beneficial as compared to some existing algorithms.