Swarm Intelligence-based Energy Optimization Protocol for Hybrid Routing in Wireless Sensor Networks
Received: 12 February 2025 | Revised: 17 March 2025 and 27 March 2025 | Accepted: 28 March 2025 | Online: 4 June 2025
Corresponding author: Rati D. Joshi
Abstract
Wireless Sensor Networks (WSNs) face critical challenges in energy efficiency, scalability, and fault tolerance due to the limited energy resources of sensor nodes. This study proposes a novel hybrid energy optimization protocol that leverages swarm intelligence algorithms, specifically Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), to address these challenges. This protocol integrates traditional clustering techniques with swarm-based optimization to design an energy-efficient and adaptive routing mechanism. Sensor nodes are self-organized into clusters using PSO, ensuring optimal coverage and connectivity. Cluster Head (CH) selection within each cluster is performed using ACO, considering residual energy, node density, and distance to the base station, ensuring balanced energy consumption. The routing mechanism combines intra-cluster communication with PSO-based multi-hop inter-cluster routing, dynamically optimized using ACO to minimize transmission costs. Reinforcement strategies adapt to environmental changes, such as node failures and energy depletion, while promoting load balance and reliability. This protocol mimics the collaborative behavior of biological swarms, allowing dynamic and adaptive energy-aware routing while addressing the challenges of network scalability and reliability. The proposed Swarm Intelligence-based Ant Colony Optimization and Particle Swarm Optimization (SIACOPSO) protocol offers significant advantages, including enhanced energy efficiency, adaptability to dynamic network conditions, fault tolerance, and scalability for large-scale deployments. Comparative analysis with traditional bioinspired protocols demonstrates the superiority of this hybrid approach in prolonging network lifetime and improving overall performance.
Keywords:
Wireless Sensor Networks (WSNs), Ad-hoc On-Demand Distance Vector (AODV), Ant Colony Optimization (ACO), Swarm Intelligence-based Ant Colony Optimization Particle Swarm Optimization (SIACOPSO)Downloads
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