Secure Information Collection and Energy Efficiency in Heterogeneous Sensor Networks Using Machine Learning with the Internet of Things

Abstract

Regarding the preservation of human life, this is one of the secondary research goals that the WSN strives to achieve. There have been the development and testing of a number of cutting-edge algorithms and protocols, and many more are now in the process of being created. Among the aforementioned methods, clustering is one that is particularly well known for its capacity to lengthen the lifespan of the web. It is also one of the solutions with the lowest possible price. Two strategies have been proposed as a means to extend the life of the WSN, improve its functionality, and maximize its usefulness. The Energy-Aware Ant Colony Optimization (EAACOP) protocol, which is derived from the Time Domain Protocol, was at first devised with the goal of reducing overall power consumption. The mission of EAACOP is to build an energy-efficient group with the highest possible number of nodes, choose the most qualified individual to serve as the group’s head, restrict the amount of space available for intragroup communication, and extend the lifetime of the web as much as feasible. By using the pillar $K$ -means grouping strategy, we are able to generate groups and guarantee that every node is covered to the greatest extent that is practically possible. During TDMA-based MAC protocol data transmission, it is used to determine who would make ideal group leaders and to evaluate the amount of energy that is consumed. The viability of the protocol is shown by simulating it and comparing it with contemporary protocols; the conclusions reached are predicated on QoS performance criteria. Specifically, as compared to older methods, the packet distribution ratio, throughput, and residual energy are raised, while the packet loss ratio, network latency, edge-to-edge delay, and battery power consumption are lowered. This results in an overall improvement.