Published Paper Details:

Wireless communication is made up of numerous constrained sensor node with constrained resources such as Energy capacity, Data transmission capacity, Battery longevity, Storage capability, Computational capacity. Sensor nodes generate massive volumes of data, which are subsequently gathered and transmitted to the sink through single or multi-bounds paths. Due to inadequate data transfer across nodes and excessive energy consumption caused by rising data transmission delay, the network becomes unstable due to the limited skills of the nodes. Network performance can be significantly impacted by sink locations and the paths that sensors take to get there. Although several solutions have been put out to deal with this problem, they frequently lead to networks that have limited lifespans, consume a lot of energy, and transport data slowly. Clustering is a helpful method for overcoming these constraints that can help wireless sensor networks operate better by consuming less energy. With the goal of increasing the lifespan of wireless sensor networks, this study applies a clustering technique to predict the optimization of two or more sink deployment and sensor-to-sink communication. We aggregate nodes using an improved mean shift approach initially, and then choose the cluster head using a QOS-aware cluster head selection. The suggested approach uses sleep scheduling and cluster-based routing for data transmission, which aids in quick routing and energy conservation.