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Title	A Neuro Evolutionary Scheme For Improved Iot Energy Efficiency In Smart Cities
ID_Doc	2956
Authors	Choudhury S.; Luhach A.K.; Alnumay W.; Pradhan B.; Roy D.S.
Year	2022
Published	Computers and Electrical Engineering, 104
DOI	http://dx.doi.org/10.1016/j.compeleceng.2022.108443
Abstract	With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city's Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme. © 2022 Elsevier Ltd
Author Keywords	Clusterhead; Feed Forward Neural Network (FFNN); Genetic Algorithm (GA); Internet of Things (IoT); Particle Swarm Optimization (PSO); Smart city

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