Swarm Intelligence-Based Energy-Centric Clustering and Routing in WSN

The use of Wireless Sensor Networks (WSNs) is growing as a flexible and affordable solution for many uses, but one of the biggest problems in WSNs is energy efficiency. An energy-efficient cluster-based routing algorithm reduces the transmission distance between sensor nodes and the base station (BS) by grouping nodes into clusters and avoiding nodes with lower energy. This work implemented an energy-efficient Ultra-Scalable Ensemble Clustering technique for large data handling. Because of its low computing complexity and great stability, the Flamingo Search Algorithm is utilized for the selection of cluster heads. In complex network conditions, the Q-learning method is utilized to find the shortest path among BS and CHs. This method generates reward points based on an objective function that requires along the distance among the BS and CHs, the energy usage and area coverage. The implemented method outperformed better performance than existing methods in terms of attained less delay of 2s, high packet delivery ratio of 95%, less energy consumption of 0.09J, and high throughput of 97% for 100 nodes.