Horse Herd Leadership Optimization: A Trust-Aware Metaheuristic for Resource Allocation and Secure Wireless Sensor Networks

Wireless sensor networks (WSNs) are foundational to modern smart environments, supporting applications ranging from healthcare and precision agriculture to industrial control and disaster response. Despite their potential, WSNs remain constrained by a limited battery life, packet loss, variable throughput, latency, and security vulnerabilities. This paper extends Horse Herd Leadership Optimization (HHLO), a bio-inspired metaheuristic modeling herd leadership, synchronization, and exploration to drive energy-aware clustering and trust-aware routing. HHLO rotates cluster-head leadership in order to balance load, injects chaotic exploration in order to avoid premature convergence, and incorporates a continuously updated node trust score directly into the routing cost in order to exclude unreliable or malicious nodes. Extensive MATLAB simulations with 1000 nodes deployed over a 1000 m × 1000 m2 field for 400 rounds, under both static and mobile settings, demonstrate HHLO’s effectiveness. Compared to baseline approaches, HHLO achieves residual energy improvement of 12–21%, throughput gains of 14–23%, Packet Delivery Ratio (PDR) increase of 6–12%, and network lifetime extension of 18–32%; it also achieves an energy balance factor (EBF) of 0.91 and a trust balance factor (TBF) of 0.88, reduces end-to-end latency by 8–10%, and reduces control overhead ratio (COR) by 10–12%. These improvements result from HHLO’s joint optimization of energy, congestion, mobility, and trust, yielding longer-lived and more reliable networks. By unifying security and optimization within a single framework, HHLO advances the development of sustainable, resilient, and environmentally conscious WSNs for next-generation IoT deployments.