Energy-Efficient Path Selection Based on Deep Neural Networks and Security Management in IoT
Abstract
In this study, the internet of things (IoT) security and energy improvement aims can be reconciled by employing the energy-efficient path selection technique. The suggested approach combines machine learning, security, deep learning, and IOT energy improvement. To produce the data, first establish the network, then carry out attacks and confirm the paths. Build a neural network with performance evaluation and predicting skills. Nodes in a network are therefore by definition negative. The optimum line of action must then be chosen. To anticipate energy by utilising the stolen time, total time, total energy, and packet delivery rate parameters, deep learning with deep neural networks, IOT, and security management are required. By employing deep neural networks to forecast energy and life maximization, deep learning, management, and longevity energy efficiency can be improved. All of the following are measured: the speed of packet delivery, total energy used, total time used, and stolen time. Here the highest number is 500 that is used to process 500 packets using a neural network. With an improvement of 100, 200, 300, and 400 packets, the lowest theft rate of theft acquired by 500 packets is 2.5. The entire amount of time spent was 364.10 seconds, with a packet delivery rate of 81.24.
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