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

Abstract

The use of wireless sensor networks (WSNs) as a key technology for controlling and monitoring a range of applications has been accepted. Heterogeneous WSNs involve nodes with different functions, for instance, nodes with sensing, fusion, and routing duties. To maximize the performance of a heterogeneous WSN, an optimized tree construction and clustering-based data aggregation approach is proposed in this paper. The Ford-Fulkerson algorithm is used to construct an optimized spanning tree with minimum energy consumption. Clusters are then formed in a distributed manner, and data aggregation is performed among the clusters. The suggested strategy is effective, as shown by the simulation results, and adding a reliable optimization method greatly lowers energy usage and enhances network performance. The research provides a clustering-based data aggregation strategy meant to maximize data delivery from heterogeneous WSNs with low-power nodes. The employed technique combines two clustering algorithms, k-means, and fuzzy c-means, to ensure fast and reliable data forwarding among WSNs. The proposed FFA obtained 95.86% energy efficiency, 87.21% QoS, 95.25% transmission rate, 90.13% PDR, 8.86% delay, 96.21% network lifetime, 91.22% throughput, 89.42% scalability and 96.58% Fault Tolerance. The proposed method can significantly reduce energy consumption and communication overhead, improving network efficiency and utilization. This work has the potential to significantly impact the design and operation of wireless sensor networks in various applications.