Smart Energy Management Based Task Allocation With Security Analysis Using Machine Learning Algorithms

Abstract

An emerging component of smart cities is vehicle-to-grid (V2G) technology, which provides a novel approach to scheduling and energy storage. Security threats currently impede V2G's normal operations. V2G security faces two challenges. Current V2G security schemes only consider the static security approach, which is insufficient to handle the problem of advanced persistent attacks and high dynamics in V2G. However, the lack of a unified information modeling technique in present V2G causes problems with security and communication. The aim is to propose a novel technique in task allocation and security analysis based on smart energy management using a machine learning model in V2G architecture. Here, the smart energy management and task allocation are carried out using a hybrid fuel cell model with a deep vector Q-gradient model. Then, the security analysis of the V2G network is carried out using a multilayer blockchain smart contract-based federated LSTM model. Experimental analysis is carried out in terms of QoS, energy efficiency, network efficiency, data integrity, and training accuracy. Simulation results are conducted to prove the effectiveness of this proposed method.