A Routing Algorithm Based on Ant Colony, Local Search and Fuzzy Inference to Improve Energy Consumption in Wireless Sensor Networks

Abstract

Energy efficiency is one of the foremost challenges in the deployment and operation of Wireless Sensor Networks (WSNs), primarily due to the limited battery capacity of sensor nodes and the high energy demands of data transmission and network management. Inefficient routing mechanisms can accelerate node energy depletion, leading to reduced network lifetime, uneven load distribution, and potential communication failures. To address these critical issues, this research proposes a novel hybrid routing algorithm that integrates Ant Colony Optimization (ACO), local search heuristics, and fuzzy inference systems to optimize energy consumption while maintaining network reliability. The algorithm leverages the decentralized path-finding capabilities of ACO to identify potential routing paths, applies local search strategies to refine path selection for improved energy efficiency, and employs a fuzzy inference system to adaptively evaluate routes under uncertain network conditions. Simulation-based performance evaluations demonstrate that the proposed algorithm achieves significant reductions in energy consumption, balances routing loads effectively, prolongs network lifetime, and maintains high packet delivery ratios when compared to conventional routing protocols. Analytical results reveal that the integration of these three techniques provides a synergistic effect, where ACO offers global exploration, local search prevents suboptimal convergence, and fuzzy logic introduces context-aware decision-making, collectively enhancing network sustainability. The findings of this study contribute a systematic framework for energy-aware routing in WSNs, highlighting both theoretical insights and practical implications for real-world deployments. Furthermore, limitations, potential trade-offs, and directions for future research, including scalability to large-scale and heterogeneous networks, are discussed to provide a comprehensive understanding of the algorithm’s applicability. Overall, this work demonstrates that hybrid optimization and intelligent decision-making approaches can effectively address the energy constraints inherent in WSNs, offering a robust methodology for enhancing operational longevity and reliability in diverse application scenarios.