ENHANCED LEACH WITH MULTI-LAYER CLUSTERING, FEDERATED & GRAPH NEURAL LEARNING AND MULTI-AGENT REINFORCEMENT FOR ENERGY-EFFICIENT WSNS

Abstract

This document presents an enhanced LEACH protocol that fuses multi-layer clustering with distributed learning and network intelligence. The design introduces (i) energy-efficient primary–secondary–tertiary clustering for hierarchical aggregation and transmission, (ii) federated learning for privacy-preserving optimization of cluster-head (CH) selection, (iii) a graph neural network (GNN) that models the wireless sensor network (WSN) topology to predict cluster configurations, (iv) multi-agent reinforcement learning (MARL) for collaborative CH decision-making, (v) energy harvesting-aware scheduling to prioritize renewable-powered nodes, and (vi) adaptive fault tolerance guided by GNN-based failure prediction. Integrated algorithms, objective functions, and a simulation-ready specification are provided, alongside a template results table to compare against LEACH and DQL-based baselines.