Prediction of Better Path Selection in Network for Coverage Based Communication Among Sensor Node in WSN

Currently, congested data networks have become a major issue that affects the quality of communication, especially in urban areas and in high-traffic locations. Even with the introduction of technologies that can help alleviate congestion, with the increasing demand for data, it is still hard to guarantee all data transmissions go through, and this affects the performance of communication. This paper focuses on a solution that seeks to improve the overall performance of data transmissions, particularly when users are located in urban environments and in high-traffic areas. The proposed solution is based on the prediction of network paths that lead to better coverage and faster data transmission. To accomplish this, we propose to use machine learning models and analyze the current network state from a probabilistic point of view. Subsequently, we explore the use of predictive analysis to identify possible congestion and the most effective routes for faster communications. We also investigate the scalability characteristics of our proposed solution. The results of our experiments demonstrate that the proposed approach provides improved coverage for data transmission and that it is capable of handling tolerable delay even under high traffic load.