Enhancing Wireless Sensor Network Routing Strategies with Machine Learning Protocols

Woth the realm of Internet supportive Things (IoT), where numerous wireless components constantly communicate, a unique challenge arises. This challenge includes issues such as handling diverse devices, ensuring data broadcasting and managing scalability. To handle these issues, a new routing protocol has been proposed in this paper. This protocol is intelligent, energy-conscious, and employs a multi-objective approach, based on the distribution type Reinforcment based Learn -Driven (RL) mechanism integrated Meta-Learning termed as Data synchronized -Machine learning (DS-ML). The core goal of proposed data-route mechanism, called DS-ML, will optimized power consumption on IoT connected networks, a critical issue given the defined power resources of wireless connected IoT Components. Additionally, mechanism focuses on adapting to network changes seamlessly by ensuring smooth adjustments to alterations and minimizing disruptions to enhances entire network Efficacy. The algorithm incorporates correlates issues in rewards designed in acceleration of the learn cycle. Through various simulation parameters, the DS-ML routing protocol demonstrated significant improvements in power efficacy and rapid adapt in unexpected network happenings. This was evidenced by enhancements in delivered ratio of data sets and reductions in data-sets delivery latency when compares in conventional data-routing Techniques. In general, proposed DS-ML system represents a solution for enhancing the performances and sustainability of IoT network infrastructure.