An Energy-Aware Hybrid Approach for Wireless Sensor Networks Using Re-clustering-Based Multi-hop Routing

Wireless sensor networks (WSNs) consist of a large number of sensor nodes, which are primarily employed for collecting data from an environment of interest. Energy resources of WSN nodes are generally restricted, irreplaceable and non-rechargeable. Hence, lowering the level of energy consumption in such networks to save more energy is the key issue in the literature. Clustering, selecting the best Cluster Head (CH) among candidates, and performing the routing only among cluster heads would be an effective approach to reduce the WSN nodes energy consumption. Therefore, cluster-based routing leads to extending the network's lifetime through aggregating data in CHs, uniformly distributing the energy among nodes, and, consequently, reducing the number of contributing nodes in the routing procedure. In this paper, an energy-aware cluster-based multi-hop routing algorithm is presented, in which the clusters would, if required, re-formed during the routing procedure. Furthermore, like other multi-hop routing algorithms, it guarantees minimizing the energy consumption through balancing energy within the network. In this paper, we have presented a cluster-based multi-hop routing algorithm. In our proposed approach, a combination of two algorithms, namely K-means and Open Source Development Model Algorithm (ODMA), are employed for clustering, and Genetic Algorithm, is applied for multi-hop routing. The simulation results confirm superiority of our proposed method in comparison with MH-FCM, EEWC, and GAFOR algorithms in terms of several metrics such as average residual energy, residual energy variance, number of packets received, number of dead nodes, and network lifetime.