A DOA-Driven Adaptive Framework for Smart Traffic and Street Lighting in WSN

Abstract Traffic management and street lighting optimization are increasingly dependent on intelligent systems for smart cities. The main objective of this paper is to design the Intelligent Traffic and Lighting Systems (ITLS) using the dream optimization algorithm (DOA) for Wireless Sensor Networks (WSNs). The system assimilates single-lane roads, moving vehicles, sensor-equipped streetlights, and a centralized control station. The fitness function of DOA optimizes network performance by balancing energy consumption, reducing congestion, and stabilizing vehicle speed variations. The network adjusts to changing traffic conditions, optimizing routes and lighting efficiency. The MATLAB simulation shows that DOA surpasses traditional rule-based systems by refining traffic flow while reducing energy usage. The performance of our proposed approach, DOA-ITLS, is compared with existing techniques like IB-SEC and KFFOA-PDES in terms of network lifetime, packet delivery, throughput, and energy efficiency. The protocol enhances packet delivery by 50% and extends network lifetime by effectively delaying node failures. DOA-ITLS is found to be a scalable, robust, and energy efficient solution for urban traffic and lighting control. By enhancing data delivery and system responsiveness, this framework makes urban mobility more sustainable and efficient.