OPTIMIZED QOS-BASED MOBILE SINK PATH PLANNING STRATEGY USING FUZZY C-MEANS AND FUZZY INFERENCE SYSTEM IN WSN

Abstract

      Traditional Wireless Sensor Networks (WSNs) suffer from non-uniform energy consumption, where nodes near high-traffic zones deplete their batteries faster than others, forming energy holes that significantly decrease the total network lifespan. Introducing a Mobile Sink (MS) can mitigate this issue by balancing the network load and redistributing data collection tasks. However, excessive or inefficient sink movement may increase communication overhead and cause frequent topology changes, leading to unstable performance. Thus, designing an adaptive and energy-aware trajectory for the mobile sink is crucial for efficient data gathering. This research deals with an adaptive fuzzy-based mobile sink path planning mechanism, termed RP-FCM-FIS, aimed at enhancing energy extending and usage network lifespan in WSNs. The suggested model partitions the sensing field into the optimal rendezvous points (RPs), and grid-based clusters are dynamically identified by utilizing the Fuzzy C-Means (FCM) algorithm. Thereafter, a Fuzzy Inference System (FIS) determines the sink’s next motion according to four decision parameters, residual energy, traffic load, sensor density, and the source node's angle. Simulation results validate that the RP-FCM-FIS protocol obtains superior energy stability, substantially extends network lifespan, and minimizes the standard variation of energy consumption compared to benchmark algorithms such as MSC-BES-HNN, MSPO-ABC, and FA*-Static.