Bio-inspired Energy-efficient Routing Protocol for Dynamic Clustering in AIbased Wireless Sensor Networks in Smart Cities

Abstract

Efficiency in Wireless Sensor Networks (WSNs) is a key challenge, particularly in applications related to smart cities where sensor nodes are usually deployed in hard-to-reach areas with low battery life. Conventional static routing and clustering schemes are not adequate for dealing with dynamic urban environments, leading to wastage of energy and shorter network lifetime. Bio-inspired algorithms and light-weight Artificial Intelligence (AI) are promising techniques to overcome these constraints. This research seeks to develop a hybrid routing protocol-BioAIEDRNet-that blends bio-inspired optimization with AI-enabled decision-making to improve energy efficiency, adaptability, and scalability in WSNs. The protocol will be designed to enable dynamic clustering, reduce energy utilization, and enhance routing responsiveness in real-time smart city environments. The model includes Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) algorithms for choosing cluster heads and optimizing paths. A light AI Decision Engine based on decision trees evaluates nodes for eligibility against current parameters such as residual energy, buffer capacity, and transmission delay. The WSN Multi-Hop Dataset was employed for testing, which underwent preprocessing with normalization, removing outliers, and correlation-based feature selection. Simulation outcomes illustrate that BioAI-EDRNet decreases energy consumption to 8%, far better than EERPMS (64.50%), CRPFCM (58.60%), and FIGWO (56.15%). The system also showed enhancements in packet delivery ratio, minimized transmission delay, and increased network lifetime, justifying its efficiency and strong performance. BioAIEDRNet is an efficient solution for sustainable WSN communication in smart cities. The hybrid scheme guarantees energy-aware, self-organizing, and adaptive routing, opening the door for future real-time deployments and smart infrastructure development.