Optimal Energy Efficiency Techniques and Security Enhancement in Wireless Sensor Network Using Machine Learning

Speech recognition, natural language processing, and image processing are just a few of the many applications that machine learning (ML) has been employed in. The use of ML in wireless sensor networks (WSNs) has recently captured the attention of many researchers, broadening the field of study. Load balance and resource utilization are the problem with WSNs. Numerous routing algorithms can resolve these problems by using less energy and extending the network lifetime in WSNs through optimum routing techniques. Additionally, there are frequently issues like rigidity, taking into account various factors, and relying on models. Machine learning has demonstrated its effectiveness in creating effective procedures to deal with difficult issues in a variety of network features. In a wireless sensor network, routing is the process of determining the best path for data transmission between various sensor nodes in accordance with the situation. A lot of machine learning has been applied to creating energy-efficient routing methods. The key contribution of this study is to give a concise overview of clustering in wireless sensor networks based on three major categories, including classical, optimization, and machine learning techniques.