Energy-Efficient Clustering in Wireless Sensor Networks through Firefly–Gradient Descent Hybrid Optimization

Abstract

The Wireless Sensor Networks (WSNs) are utilized by many monitoring applications, and it is widely accessible. Restricted node energy and dynamic network backgrounds limits the effectiveness of WSN. Thus, premature node failures and shorter network lifetime (NL) may result from these limitations. For Cluster Head (CH) selection, conventional clustering methods are ineffective, because these conventional methods mostly utilized static metrics. So, these conventional methods fail to adapt to dynamic topologies and energy patterns. An AI-enhanced Firefly–Gradient Descent Hybrid Optimization (AI-FGDHO) model is suggested in this study for the purpose of resolving those issues. In the network structure, intelligent decision-making is integrated by AI-FGDHO model. The node-level local CH candidacy scoring with lightweight machine learning (ML) algorithms and CH level collaborative model updates without raw data sharing using federated learning (FL) are utilized by this suggested model. The CH rotation schedules and routing strategies are dynamically refined by the Reinforcement learning (RL). For enhancing CH placement, and exploiting network topology, graph neural networks (GNNs) are used. In the exploration ability of Firefly optimization and the exploitation strength of gradient descent, these AI components are integrated, and it facilitate in adaptive and energy-aware clustering. Then, simulation was conducted with the suggested AI-FGDHO and conventional methods. With higher residual energy (0.70 J), delivery ratio (80), NL (950 rounds), throughput (900 packets), lower overhead (120 packets), latency (200 ms), reduced CH rotations (22), and improved coverage (75), the suggested AI-FGDHO model executes better than conventional methods, and it was demonstrated by the simulation outcomes.