Adaptive Type-2 Fuzzy Unequal Clustering With Energy-Aware Routing For Efficient WSNs

Conventional clustering algorithms in wireless sensor networks (WSNs) often face several challenges, including energy imbalance, routing, and the hot-spot problem, especially in networks with varying node densities and energy levels. To address these challenges, we propose the adaptive type-2 fuzzy unequal clustering algorithm with energy-aware routing (AT2UCAR). It employs a fuzzy inference mechanism to dynamically establish cluster sizes and select optimal cluster heads based on residual energy, node density, and distance from the base station. By creating a set of fuzzy rules, this algorithm enables the development of unequal clusters, allowing nodes situated farther from the BS to form larger clusters, while those nearer to the BS maintain smaller clusters to minimize overall energy consumption. AT2UCAR is proposed to optimize data transmission paths based on residual energy and further optimizes multi-hop routing to improve energy efficiency and load balancing. Simulation results indicate that AT2UCAR significantly improves load balancing and enhances energy efficiency in comparison to other clustering algorithms.