A Wireless Detection System that Utilizes an Artificial Neural Network to Enhance Energy Efficiency and Prolong the Lifespan of the Network

Nano-electro-mechanical designs (NEMD), wireless technology, additionally, digital electronics can make it possible to create small, inexpensive, and low-powered detection units that can communicate over short and multi-hop distances. The main issue for Cellular Detector Systems (CDS) is to maximize battery power abstraction to increase network lifespan while reducing power consumption from regular input signals and operational expenses between access points and detector networks. Packet expenses, retransmissions, and control signal overhead fees add energy challenges. We suggest using random sampling and correct routing to conserve power and transport data quickly in the network significantly reducing the overhead of the control signal. Using neural network-based backpropagation, energy-efficient and nearby modules are predicted along with the information-promoting path to the endpoint for increasing the efficiency of packet transmission. This study illustrates the ways to optimize battery energy consumption without sacrificing data connection speed at low cost using simulations. This strategy improves detection system efficiency, network longevity, performance, and energy consumption to 76%, 68%, 79%, 82% respectively.