IoT-Based Sensor Networks: Evaluating Various Clustering Methods for Energy Efficiency in Wireless Sensor Networks (WSNs)

Wireless Sensor Networks (WSNs) are critical components of the Internet of Things (IoT) due to their ability to collect and transmit data from various environments. However, the energy efficiency of these networks remains a significant challenge, as sensor nodes often operate on limited power sources. This study investigates IoT-based sensor networks with a focus on various clustering methods aimed at improving energy efficiency in WSNs. The study evaluates the effectiveness of these clustering methods in prolonging the lifespan of WSNs by optimizing energy consumption across the network. Factors such as node density, communication overhead, and the scalability of the clustering algorithms are analysed to determine their impact on energy efficiency. This study proposed the Efficient Data aggregation a dissemination using Optimized Tree based clustering (EDOT) for IoT enabled WSNs model for efficient process of data aggregation and dissemination using the hybrid approach.