Energy-Efficient WSN Communication Using Tabu Search-Based Minimum Spanning Tree Routing for Data Aggregation

The study foregrounds WSNs and reviews the development of wireless sensor networks on energy aspects including; apparel battery energy, large bandwidth and few energy sources that affect sensor nodes. Possible data gathering mechanisms like tree-based approaches and hierarchical clustering are discussed to improve the energy efficiency of WSNs. This paper presents our proposed method known as TSEO-MSTR, an improvement on the minimum spanning tree algorithm for constructing energy-efficient routes with Data Aggregation for WSNs and Tabu Search (TS) for optimizing energy consumption. The method reduces energy utilization and enhances the durability of the network by targeting the best nodes with more energy for message forwarding. The necessary information regarding the principles of the clustering phases such as node selection and data transmission is presented. The above stated methodology is then compared with other routing algorithms, proving itself superior in energy efficiency, routing overhead, and network lifetime. The outcomes of the numerical researches discovered that the suggested TSEO-MSTR solution increases energy saving and enhances data accuracy, thereby decreasing latency to make WSN communication more efficient.