Multi Agent Energy Management Prediction and Load Balanced Clustering Framework for WSNs using Quantum Bio-Inspired PSO Optimization

Objectives: To introduce an adaptive architecture for energy state prediction and a load balancing clustering framework for WSNs to address the prevailing challenges of high energy consumption, rapid node depletion, and inadequate cluster formation in large-scale sensor deployments. This aims maximize network lifetime and communication by integrating a multi-agent decision model with swarm optimization. Methods: The Quantum Particle Swarm Optimization (QPSO) reinforced clustering method, combined with the Multi-Agent Predictive Control (MAPC) model is utilized to enhance energy and load balance operations across all network rounds. Each cluster head functions as an autonomous agent to forecast the residual energy and estimated traffic load using lightweight predictive analytics. This helps proactive workload redistribution and prevents premature node depletion. QPSO tunes clustering weights, routing preferences and load balancing parameters through quantum-based particle movement and probabilistic exploration. Stable cluster heads, cluster sizes, and energy-efficient multi-hop routes are selected using the optimized decision vector. NS-3, MATLAB and Python are used as an evaluation tool for the proposed MPAC-QPSO. Simulation generated datasets are used for performance evaluation and a comprehensive comparative assessment is done with the existing models such as E-FLZSEPFCH, DEEC- IWQPSO, EAC-ILAP and MC-CRITIC-KM. Findings: According to simulation results, the proposed MAPC-QPSO model reveals superior performance with 98.8% network lifetime, achieves a packet delivery rate of 98.4% with 8.7s execution time, has a load-balancing accuracy of 97.8%, and reduces average latency to 41ms while extending the first-node-death lifetime to 2100 rounds and reducing overall energy consumption by approximately 15–20%. Novelty: The novel MAPC integrated with QPSO forms an intelligent, adaptive, and robust framework that effectively minimizes energy depletion and imbalance, reduces end-to-end communication delays, improves load balancing, and significantly extends the operational lifetime of Wireless Sensor Networks, which is highly suitable for resource-constrained environments. Keywords: Wireless Sensor Networks, Energy Management, Clustering, Quantum-PSO, Multi-Agent Prediction, Load Balancing, WSN Routing