This routing method improves energy efficiency and node reliability in wireless sensor networks, indicating better performance over existing protocols.
WSNs consist of numerous energy-constrained Sensor Nodes (SNs), making energy efficiency a critical challenge. This paper presents a novel multipath routing model designed to enhance network lifetime by simultaneously optimizing energy consumption, node connectivity, and transmission distance. The model employs an Improved Particle Swarm Optimization (IPSO) algorithm to dynamically determine the optimal weight coefficients of a cost function that integrates three parameters: residual energy, link reliability, and buffer capacity. A compressed Bloom filter is incorporated to improve packet transmission efficiency and reduce error rates. Simulation experiments conducted in the NS2 environment show that the proposed approach significantly outperforms existing protocols, including Reinforcement Learning Q-Routing Protocol (RL-QRP), Low Energy Adaptive Clustering Hierarchical (LEACH), On-Demand Distance Vector (AODV), Secure and Energy-Efficient Multipath (SEEM), and Energy Density On-demand Cluster Routing (EDOCR), achieving a 7.45% reduction in energy consumption and maintaining a higher number of active nodes over time. Notably, the model sustains 19 live nodes at round 800, whereas LEACH and APTEEN experience complete node depletion by that point. This adaptive, energy-aware routing strategy improves reliability, prolongs operational lifespan, and enhances load balancing, making it a promising solution for real-world WSN applications.
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Mohammadian et al. (2025) studied this question.
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