Energy-Efficient Cluster Head Election and Data Aggregation Ensemble Machine Learning Algorithm

Data transmission and communication in mobile wireless sensor networks are hindered due to the limited energy of the sensor node. This causes various challenges in the communication between sensor nodes having network loss, latency, and in complete transactions. To concern, a clustering-based network model has been developed where the cluster head election is the major issue. Therefore, we proposed an intelligent cluster-based network model with the objective to provide intelligent energy-efficient cluster head election and data aggregation mechanisms using Artificial Intelligence techniques in the mobile sensor network. Also, to overcome the network overhead, a mechanism has been presented to validate data similarity among the nearby sensor nodes. The performance evaluation of the proposed scheme has been conducted using Python with machine learning and the results obtained reflect better performance in terms of cluster head selection and data aggregation.