Abstract
Wireless sensor networks (WSNs) are advantageous when there is no existing infrastructure (such as in military applications, emergency relief efforts, etc.) and it is necessary to develop a network at a low cost. A predetermined routing protocol or intrusion detection system is not available to Wireless Sensor Networks because they are dynamic by nature and need separating the network's nodes to do this. Because nodes in the majority of WSN applications are mobile and rely on battery capacity and the availability of restricted resources, energy consumption is an important research area for carrying out a variety of activities in WSNs. Self-learning algorithms that function without scripting or human involvement can be effectively used to report this problem depending on the applications need. This study investigated different ML-based WSN systems and exploring the ML techniques for energy efficiency along with some open issues.
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