A Hybrid Dynamic Sand Cat Swarm Optimisation (HD-SCSO) Approach for Resource Allocation in Wireless Sensor Networks

Abstract

Wireless sensor networks (WSNs) require robust solutions to optimise energy consumption, ensure reliable data transmission and maintain fair resource distribution across large-scale deployments. This paper proposes a hybrid dynamic Sand Cat Swarm Optimisation (HD-SCSO) approach, a bioinspired optimisation framework derived from sand cat adaptive localisation and hunting behaviours. HD-SCSO enhances resource allocation through dynamic sensor node operation adjustments, balanced network workload distribution and optimised data routing to minimise packet loss and extend network lifetime. It operates through three key mechanisms: adaptive cluster organisation, intelligent cluster head selection and real-time adjustments in response to changing network conditions. A security-mathematical model is proposed, which considers the aspect of trust, feedback, probability of cluster heads and conditions of intrusion detection system (IDS) alerts to strengthen resistance to attacks. Simulation results demonstrate that HD-SCSO outperforms existing algorithms, in terms of energy efficiency, packet delivery, network fairness and overall throughput across varying network sizes. Unlike existing algorithms, HD-SCSO integrates trust-based IDS, hybrid sensitivity-driven position updating and unified routing, security and resource allocation, enabling enhanced adaptability and robustness. Its self-optimisation features make it highly suitable for diverse IoT applications, including environmental monitoring, industrial automation and healthcare management, ensuring efficient operation and long-term network sustainability.