Optimal Path Selection Algorithm for Energy and Lifetime Maximization in Mobile Ad Hoc Networks Using Deep Learning

The energy-efficient path selection algorithm proposed in this paper balances the conflicting goals of maximizing network lifetime and minimizing energy usage routing in mobile ad hoc networks (MANETs). The proposed strategy maximizes lifetime energy efficiency, MANET, and deep learning. Produce the data after building the network by carrying out assaults and validating paths. Then sketch a neural network with capabilities for prediction and performance evaluation. Then nodes in a network that are negative by definition must be followed by choosing the optimum route. Employed in the current study to increase the energy efficiency as well as the kind of data handling on the network with the metrics of stolen time, total time, total energy, and packet delivery rate, predict the energy and lifetime maximization utilizing deep neural networks for deep learning, management, and lifetime energy efficiency maximization. Five hundred packets of data from a neural network were used to get the maximum value. The total energy used is 7570, packets are delivered at 74.60, time taken is 371.81, and the minimum theft rate for 500 packets is 6.8.