Optimized Tree Construction and Clustering-Based Data Aggregation for Heterogeneous Wireless Sensor Networks Using Ford-Fulkerson Algorithm.

The article focuses on optimizing data aggregation in heterogeneous wireless sensor networks (HWSNs) using a tree construction and clustering-based approach enhanced by the Ford-Fulkerson Algorithm (FFA). This method constructs an energy-efficient spanning tree and forms clusters to aggregate data, aiming to reduce energy consumption and improve network performance metrics such as energy efficiency, quality of service (QoS), transmission rate, packet delivery ratio (PDR), delay, network lifetime, throughput, scalability, and fault tolerance. Simulation results demonstrate that the proposed FFA outperforms several existing algorithms across these metrics, achieving, for example, 95.86% energy efficiency and 96.58% fault tolerance. The approach also incorporates security measures like Advanced Encryption Standard (AES) and adapts dynamically to network changes, though it involves computational complexity and potential single points of failure at cluster heads. Future work includes integrating reinforcement learning, genetic algorithms, and heuristic methods to further enhance data aggregation efficiency and adaptability in dynamic network environments.