Wireless Sensor Networks (WSNs) are essential components of contemporary communication systems because they facilitate the easy gathering and transfer of data for various uses. Because sensor nodes have limited energy resources and network topologies are dynamic, effective and flexible routing strategies are required. To improve energy efficiency and extend network lifetime in WSNs, this study proposes the Fuzzy Logic Optimized with Swarm Intelligence Algorithm Algorithm (FLOWSIA). Fuzzy logic and swarm intelligence are combined in the FLOWSIA technique to dynamically modify routing choices according to several variables, such as network density, link quality, and node energy levels. By imitating the collective behavior of biological systems like ants and bees, swarm intelligence guarantees scalable and distributed optimization, while fuzzy logic offers a reliable method for managing the inherent uncertainty in WSN contexts. The suggested method optimizes the trade-offs between several parameters using adaptive learning techniques. According to simulation studies, FLOWSIA performs noticeably better than conventional routing protocols regarding packet delivery ratio, network lifetime, energy efficiency, throughput, delay, and network stabilization time. Fuzzy-based decision-making reduces premature node failures and maintains network connectivity over long periods by ensuring balanced energy utilization across nodes.
Singh et al. (2026) studied this question.