Enhancing the Efficiency of Routing Strategies in WSNs Using Live Streaming Algorithms

The application of machine learning in wireless sensor networks (WSN) has attracted much attention. Since references in WSNs are pre-defined, determining how to optimize the utilization of resources and achieve efficient load balancing has become a critical problem in WSNs. The goal of conventional green routing algorithms is to reduce energy consumption and increase network life cycles by improving routing schemes in wireless networks. However, sometimes problems arise, such as poor flexibility, focusing on a single operative, and relying on precise algebraic models. Machine learning techniques can adapt to environmental changes and employ multiple agents to make informed decisions, providing new ideas for energy-saving and intelligent routing algorithms in wireless networks. In this piece, we examine the suggestion of fictitious artificial intelligence. Developing a mathematical framework is an effective approach to formulating an ideal green routing strategy that addresses the shortcomings of conventional green networking techniques. This research summarizes past, present, and future advancements in environmentally friendly routing algorithms within wireless communication networks. The information in this article will be interesting for individuals interested in applications of machine learning in WSNs.