Wireless Sensor Networks (WSNs) play a vital role in smart city Internet of Things (IoT) applications, including environmental monitoring, intelligent transportation, and infrastructure management. However, limited battery capacity, uneven energy consumption, and inefficient clustering and routing mechanisms significantly reduce network lifetime, reliability, and scalability, especially in large-scale IoT deployments. Traditional routing protocols often rely on single-objective optimization or static clustering strategies, which fail to maintain long-term energy balance and stable communication performance. To address these challenges, this paper proposes iPAFAR, a Pareto-based multi-objective clustering and routing framework designed for IoT-enabled WSNs. The proposed model formulates cluster-head selection as a multi-objective optimization problem that considers residual energy, node centrality, load variance, and fairness. A Non-Dominated Sorting Artificial Algae Algorithm (NS-AAA) is used to obtain Pareto-optimal cluster-head configurations, followed by a fuzzy inference system for refined decision-making. To ensure long-term energy stability, a Lyapunov-based routing model is incorporated, and an adaptive re-clustering mechanism is introduced to reduce unnecessary control overhead under dynamic network conditions. The performance of the proposed framework is evaluated through MATLAB-based simulations and compared with existing protocols, including LEACH-M, ME-LEACH, FQA, MKNDPC, RANP-PSO, and BKA-TOA. Experimental results show that iPAFAR achieves approximately 40% lower end-to-end delay, 15%–20% higher packet delivery ratio, and 45%–50% improvement in residual energy while maintaining nearly twice the number of active nodes after 1000 simulation rounds. These results confirm that the proposed framework provides improved energy efficiency, load balancing, and routing stability, making it suitable for long-term smart city IoT deployments.
Talwar et al. (Thu,) studied this question.