A hybrid swarm intelligence and evolutionary approach for adaptive fuzzy segmentation in energy-efficient WSNs

Abstract Wireless Sensor Network have become popular in a variety of applications including environmental monitoring and healthcare systems. But the biggest problem in WSNs is still how to optimize the energy consumption to improve the network lifetime. In dynamic node conditions, the wireless sensor networks need energy-aware clustering to prolong the lifetime. The proposed research work is Adaptive Fuzzy Guided Segmentation which constructs a round-wise eligibility pool for cluster-head using residual energy and communication context, and constructs a stable cluster by refining the cluster stability using GA. The method is tested with various node density and random deployments, with typical radio models and named WSN baselines. Results show increased lifetime, lowered energy consumption, increased delivery reliability, and controlled latency. This hybrid algorithm makes use of fuzzy logic to adaptively cluster sensor nodes based on the dynamic parameters like energy, communication range etc., which is different from the conventional clustering algorithms. The framework uses Swarm Intelligence to optimize cluster formation, making sure that data is transmitted effectively and with a low energy cost. The Evolutionary Algorithm also optimizes iterative processes, making the trade-offs. To prove the proposed algorithm is better than the existing algorithms (PSO and GA), detailed simulations are done. It enhances its network lifetime, packet delivery ratio, and overall energy consumption. With fuzzy segmentation, the clusters are dynamically generated and the hybrid algorithm has a very good energy load distribution among nodes, as the network changes conditions. The proposed solution is innovative and presents a scalable solution that can be realized in the various network sizes and topologies and is suitable for usage in smart environments with limited energy resources.