Internet of Things-Based Energy Efficiency Optimization Model in Fog Smart Cities

Wasswa Shafik - Computer Engineering Department, Yazd University, Intelligent Connectivity Research Laboratory, Yazd, Iran
S. Mojtaba Matinkhah - Computer Engineering Department, Yazd University, Yazd, Iran
Mamman Sanda - Department of Physics, Solid-state Physics, Yazd University, Yazd, Iran
Fawad Shokoor - Computer Engineering Department, Yazd University, Yazd, Iran

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In recent years, the IoT) Internet of Things (IoT) allows devices to connect to the Internet that has become a promising research area mainly due to the constant emerging of the dynamic improvement of technologies and their associated challenges. In an approach to solve these challenges, fog computing came to play since it closely manages IoT connectivity. Fog-Enabled Smart Cities (IoT-ESC) portrays equitable energy consumption of a 7% reduction from 18.2% renewable energy contribution, which extends resource computation as a great advantage. The initialization of IoT-Enabled Smart Grids including (FESC) like fog nodes in fog computing, reduced workload in Terminal Nodes services (TNs) that are the sensors and actuators of the Internet of Things (IoT) set up. This paper proposes an integrated energy-efficiency model computation about the response time and delays service minimization delay in FESC. The FESC gives an impression of an auspicious computing model for location, time, and delay-sensitive applications supporting vertically -isolated, service delay, sensitive solicitations by providing abundant, ascendable, and scattered figuring stowage and system associativity. We first reviewed the persisting challenges in the proposed state-of-the models and based on them. We introduce a new model to address mainly energy efficiency about response time and the service delays in IoT-ESC. The iFogsim simulated results demonstrated that the proposed model minimized service delay and reduced energy consumption during computation. We employed IoT-ESC to decide autonomously or semi-autonomously whether the computation is to be made on Fog nodes or its transfer to the cloud.


Computation time; energy efficiency optimization; fog-enabled smart grid; Internet of Things (IoT); response time; service delay minimization.

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