Abstract
Abstract Wireless Sensor Networks (WSN) aggregate data from multiple sensors and transfer it to a central node. Sensor nodes should use as little energy as possible to aggregate data. This work has focused on optimal clustering and cluster head node selection to save energy. HyperGraphs (HGC) and cluster head selection based on distance and energy consumption are unique approaches to spectral clustering. GRA computes a relational matrix to select the cluster head. The network’s Moving Agent (MA) may use Hypergraphed Particle Swarm Optimization (HGPSO) to collect data from cluster heads. Compared to the clustering algorithm without agent movement, the HGC-GRA-HGPSO approach has increased residual energy by 5.59% and packets by 2.44%. It also has improved residual energy by 2.45% compared to Grey Wolf Optimizer-based Clustering (GWO-C).
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