A Comprehensive Review of an Artificial Intelligence Assisted Resource Allocation Strategy in Wireless Sensor Networks

In today’s world, the Wireless Sensor Network (WSN) is quite important. The implementation of sensor networks through wireless media has also risen, reflecting the growing importance of tracking and monitoring operations. Sensors are ubiquitous, but they come with a host of problems related to power consumption, security, coverage, latency, and design; WSNs are the subject of active investigation. Extending the useful life of sensors has recently emerged as a priority in the scientific community. With a microelectronic device, a sensor has a limited amount of power. The energy required for processing is supplied by the power source. Charging and upgrading nodes might be challenging task if the nodes are spread apart and the energy usage is just as important as an issue with hardware. This research study enhances the energy consumption of its nodes in WSN and evaluates several methods for localization and resource allocation. This research provides a solution to the challenging problem of node management and scheduling by predicting the energy consumption of WSNs to carry out all communications. By modifying AI logic to increase fault tolerance and including the notion of digital twins to make informed judgments and optimize resource utilization via WSN, this study resolves the prior complications. The findings will be useful in enhancing the knowledge of current procedures and creating new, cutting-edge approaches.