Dynamic Palm Tree Architecture for Energy-Efficient Data Aggregation Using Sugeno Fuzzy Inference System

Ephraim Aminchi, Emmanuel Adewale Adedokun, Basira Yahaya, Joel Haruna

Abstract


Traditional data aggregation methods often result in excessive energy consumption, increased aggregation time, and inefficient node selection, limiting their applicability in large-scale UWSNs. The existing approach suffers from redundancy in data aggregation, suboptimal selection of master and local centres, and unbalanced energy utilization, leading to reduced network lifetime. To address these challenges, this work introduced an improved data aggregation scheme based on a palm tree-inspired hierarchical architecture, incorporating fuzzy logic for dynamic node selection. The OPT-FIS utilized a multi-criteria fuzzy logic-based decision-making system to optimize the selection of local centres, considering parameters such as leaflet angle, residual energy, distance to the master node, and energy-to-distance ratio. Fuzzy inference rules, which evaluate the inputs, were created to determine node suitability for local centre selection. The performance of the developed OPT-FIS was evaluated against the existing method using performance metrics of aggregation energy, aggregation time, aggregation ratio, network lifetime, and selection efficiency for master and local centres. The results of the implementation across various communication ranges of 400m, 500m, and 600m showed that the OPT-FIS improved energy efficiency, achieving a reduction in aggregation energy by 8.70%, 10.81%, and 7.98%, as well as a reduction in aggregation time by 13.71%, 18.60%, and 9.84%, respectively. The results showed that the OPT-FIS provides a scalable, energy-efficient, and adaptive approach to data aggregation in underwater wireless sensor networks.


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