Whale and ant colony optimization for energy-efficient IoT-WSN clustering and routing in smart cities

Abstract

Energy-constrained wireless sensor networks (WSNs) underpin most Internet of Things (IoT) deployments in smart cities, yet node clustering and inter-cluster routing are frequently optimized as separate, sequential stages, which limits the energy efficiency and lifetime achievable by the network as a whole. This paper addresses that gap by jointly optimizing node-activation-based topology control, energy-aware clustering, and multi-hop routing within a single metaheuristic search, and evaluates this design principle across two structurally different network assumptions and optimization paradigms. First, for homogeneous IoT-WSN deployments, we propose WOTC-EE (Whale Optimization-based Topology Control and Energy-Efficient Routing), a binary-adapted Whale Optimization Algorithm driven by a multi-objective fitness function that balances residual energy, coverage, energy variance, and cluster-head proximity. Simulation results show that WOTC-EE reduces early-stage energy consumption by approximately 28%, improves network lifetime by nearly 50%, and decreases dead-node ratio by 81% relative to classical protocols such as LEACH, PSO, and GWO. Second, for heterogeneous IoT-WSN deployments with mixed-capability nodes, we propose ACO-HCR (Ant-Colony-Optimization-based Heterogeneous Clustering and Routing), which jointly constructs cluster-head assignments and multi-hop routes through pheromone-guided probabilistic construction rather than continuous position-update search. Evaluated against the heterogeneity-aware baselines LEACH, SEP, and DEEC across networks of 100–300 nodes, representative of smart-city sub-district sensing clusters (e.g., a neighborhood environmental-monitoring grid or a single utility-metering zone) rather than full metropolitan-scale deployments, with 15–20 independent runs per configuration, ACO-HCR significantly delays first- and half-node death relative to all three baselines (p < 0.001 in most comparisons), while exhibiting an earlier last-node-death round, a genuine energy-balance-versus-full-depletion trade-off that we analyze and report transparently rather than omit. Read together, the WOTC-EE and ACO-HCR case studies show that jointly optimizing topology control, clustering, and routing within a single metaheuristic search improves network sustainability regardless of whether the underlying network is homogeneous or heterogeneous, and regardless of whether the search itself is continuous position-update-based (WOA) or discrete construction-based (ACO), indicating that the principle generalizes beyond either algorithm individually.