GOVERNANCE-DRIVEN SMART CITY ENERGY MANAGEMENT EMPLOYING BIO-INSPIRED QUANTUM FIREFLY–PARTICLE SWARM OPTIMIZATION FOR WIRELESS SENSOR NETWORKS AND RELIABLE PUBLIC SERVICES

Abstract

Rapidly expanding smart-city ecosystems require energy-efficient, transparent, and citizen-centric governance frameworks, particularly for surveillance-intensive infrastructures such as intelligent traffic monitoring and urban safety systems. This study proposes a Governance-Oriented Smart City Energy Management Framework powered by a Bio-Inspired Quantum Firefly Particle Swarm Optimization (QF-PSO) algorithm, engineered for large-scale urban environments with complex, heterogeneous sensing architectures. By integrating Firefly Algorithm exploration dynamics with quantum-enhanced PSO convergence, the framework jointly optimizes sensor duty cycling, adaptive routing, and workload allocation across distributed edge–cloud layers. The system is evaluated using real-time surveillance streams and IoT telemetry sourced from publicly available smart-city open-data repositories widely used for mobility analytics, traffic modeling, and public-safety applications. Experimental results show that QF-PSO delivers 29–42% energy savings, 23–36% latency reduction, and a 33% extension in wireless sensor-network lifetime, outperforming conventional PSO, FA, GA, ACO, and QPSO with 25–28% faster convergence and improved robustness. Additionally, packet delivery ratio increases by 11%, and anomaly detection accuracy improves by 8–10%, enhancing reliability under dense urban deployment conditions. From a governance perspective, the framework ensures 5% higher service uptime, 17–20% faster fault recovery, and improved transparency through policy-aware real-time monitoring dashboards. These outcomes demonstrate that embedding quantum-enhanced bio-inspired optimization within governance-first architecture can substantially advance the sustainability, resilience, and accountability of modern smart-city digital infrastructures.