Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)

Abstract

Wireless sensor networks WSNs have expanded in popularity in recent years and are now being utilized for many different operational tasks including tracking, monitoring, transportation, military operations, and healthcare. Therefore, it's essential for WSNs to prolong the sensor node's lifespan. The most crucial component in the sensor nodes is the energy from the battery, determining how long the WSN will last. Energy saving is essential since there is a limited battery powering the sensor nodes. Energy is needed at sensor nodes for a variety of operations, including data receipt and transmission, data processing, sensing, and other operations. However, data processing uses substantially less energy than data transmission, which has the highest energy consumption of all of them. As a result, reducing the spacing between the base station (BS) and the sensor node will result in reducing the distance that the data travels on its way to the BS, which will help conserve energy and increase the lifespan of WSNs. In this research, two methods that operate at the sensor node level are proposed: clustering and data aggregation. K-means clustering and Extrema Point (EP) data aggregation. The proposed approaches operate in three steps periodically: data collection, data aggregation, and data transmission. By aggregating duplicated data before transmitting, it to the base station (BS), these methods aim to shorten the distance between sensor nodes and the base station as well as the amount of transmitted data, while maintaining a reasonable level of accuracy for the data received at the BS or Cluster Head (CH). The efficiency of the proposed strategies is evaluated by extensive simulated experiments. The simulation outcomes demonstrate that the proposed methodology outperforms the current strategies and yields energy savings of over 90% when compared to the PIP-DA and ATP strategies.