| Popis: |
Wireless sensor network has several constraints. The major constraint is the limited energy resource of the sensor nodes, limiting the overall efficiency and lifetime of the network system. Clustering is a widely used strategy that arranges the network into smaller groups called clusters for improving the performance and longevity of the network. The majority of swarm-based clustering algorithms focus on a single objective for optimization to find optimal cluster centers. However, in many practical situations, there is a need for simultaneous optimization of many objectives to get the promising cluster centers. Here, a multi-objective binary Grey wolf optimizer is used for finding Pareto optimal clustering centers to achieve five objectives, namely maximize overall CH energy, minimize compactness, minimize CH count, minimize energy from nonCH to CH transmission, and maximize separation. The stability period of the network with proposed approach is increased by 56%. Moreover simulation outcomes show the improved performance of the proposed method in terms of total residual energy, number of elected cluster heads, and network lifetime as compared to other state-of-the-art evolutionary clustering protocols such as SEP, IHCR, ERP, and BEECP. |
