Efficient and Sustainable Utilizations of Sensor Networks with Minimal Traffic Using K-Medoid based Hybrid Clustering

The network traffic is one of the significant research issues in current communication industries due to people are passing bulk and unwanted data to transmit from one network segment to another network segments. Efficient and sustainable utilisation of sensor networks necessitates minimal traffic within the networked sensors (WSNs). These networks comprise multiple sensor nodes that are interconnected, and their optimal performance is achieved through the avoidance of congestion, low energy consumption, elimination of duplicate information transmission, and minimal data transfers to the sink. Efficient attainment of these objectives necessitates the use of data aggregation. The principal objective of data aggregation is to effectively collect and merge data, while simultaneously eliminating superfluous data in order to improve the longevity of the network. This study's review centered on various information aggregation methods, such as flat networks, hierarchical systems, and structure-free systems, and their respective variations. The current research centres on the initiation of work pertaining to energy-efficient data collection and the administration of large databases through the utilisation of distinct models such as K-medoid, k-means, and fuzzy-based clustering mechanisms for validation. The objective is to enhance data collection in wireless sensor networks, thereby demonstrating an improved cluster head selection rate and a better minimum distance rate between two nearby nodes through the proposed scheme. Overall, the implementation of the proposed scheme yields a performance improvement of more than 13 to 17% when compared to the results obtained from the current system.