Abstract

In a Wireless Sensor Network (WSN), dozens or hundreds of battery-driven sensors communicate with one another. Batteries have to be replaced frequently when nodes are deployed in unattended environments. Internet of Things (IoT) applications are becoming increasingly scalable and energy-efficient, making energy-efficient data aggregation a critical research focus. As part of this study, two hybrid data aggregation frameworks are presented and evaluated in order to optimize energy consumption and network performance. In the first framework, hierarchical clustering is performed using BIRCH (Balanced Iterative Reduction and Clustering Using Hierarchies), while mobile base station shunting is performed using Ant Colony Optimization (ACO). Using Particle Swarm Optimization (PSO), optimal cluster heads and base stations can be placed, and routing paths can be optimized using the Minimum Spanning Tree (MST) algorithm. Software-defined WSNs reduce computational overhead and improve adaptability by utilizing a software-defined architecture. According to a comparison of energy efficiency, network lifetime, control overhead, and data latency metrics, both approaches outperform traditional static clustering methods significantly; however, the BIRCH and ACO model excels in adaptive clustering and load distribution, while the PSO and MST model provides the best path optimization and the least amount of delay in data transmission.

Keywords

Wireless Sensor Networks, Sensor Nodes, Routing, Clustering, Data Aggregation,

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References

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