Abstract

Emergency rescue operations in urban combat zones face critical challenges in maintaining reliable communication networks under conditions of high mobility, limited energy, and dynamic topologies. This study proposes an integrated Queen Honey Bee Migration (QHBM) and Internet of Things (IoT) framework for multi-objective optimization in tactical rescue communication. The framework utilizes a bio-inspired algorithm without clustering, employing polar-spatial sector analysis to enable adaptive routing in mission-critical environments. Simulation results demonstrate that the proposed QHBM-IoT framework outperforms conventional heuristic algorithms, namely Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO). Specifically, QHBM achieves up to 25% longer network lifetime than PSO, and 30% higher throughput while maintaining a packet delivery ratio above 92% in networks with up to 300 nodes. It also achieves the lowest energy consumption (0.87–1.21 Joules), minimal end-to-end delay (105–179 ms), and reduced control overhead (5.2–11.2 packets/s). In addition, QHBM exhibits the fastest and most stable convergence, achieving optimal fitness within fewer than 100 iterations. A prototype wearable device integrating RSSI, distance, and GPS further validates the framework's real-time implementation feasibility. These findings demonstrate the practical potential of QHBM-based swarm intelligence for energy-efficient and reliable communication in emergency rescue operations under highly dynamic urban conditions.