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


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.5.2.373

Abstract


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.

Keywords


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

Full Text:

PDF

References


W. Shafik, M. Matinkhah, P. Etemadinejad, M. N. Sanda, “Reinforcement learning rebirth, techniques, challenges, and resolutions,†JOIV: International Journal on Informatics Visualization,†vol. 4, no. 3, pp. 127-135, 2020.

W. Shafik, S. M. Matinkhah, M. N. Sanda and S. S. Afolabi, “A 3-dimensional fast machine learning algorithm for mobile unmanned aerial vehicle base stations,†International Journal of Advances in Applied Sciences, vol. 10, no. 1, pp. 28–38, 2020.

W. Shafik, S. M. Matinkhah and M. N. Sanda, “Network resource management drives machine learning: a survey and future research direction,†Journal of Communications Technology, Electronics and Computer Science, vol. 30, pp. 1–15, 2020.

W. Shafik, S. M. Matinkhah and M. Ghasemazade, “Fog-mobile edge performance evaluation and analysis on internet of things,†Journal of Advance Research in Mobile Computing, vol. 1, no. 3, pp. 1–17, 2019.

W. Shafik, S. M. Matinkhah and M. Ghasemzadeh, “A fast machine learning for 5g beam selection for unmanned aerial vehicle applications,†Information Systems & Telecommunication, vol. 7, no. 28, pp. 262-278, 2019.

H. Meng, W. Shafik, S. M. Matinkhah and Z. Ahmad, “A 5g beam selection machine learning algorithm for unmanned aerial vehicle applications,†Wireless Communications and Mobile Computing, 2020.

W. Shafik and S. A. Mostafavi, “Knowledge engineering on internet of things through reinforcement learning,†International Journal of Computer Applications, vol.177, no. 44, pp. 0975–8887, 2019.

W. Shafik, S. M. Matinkhah, M. Asadi, Z. Ahmadi and Z. Hadiyan, “A study on internet of things performance evaluation,†Journal of Communications Technology, Electronics and Computer Science, vol. 28, pp. 1–19, 2020.

W. Shafik, S. M. Matinkhah and M. Ghasemzadeh, “Theoretical understanding of deep learning in uav biomedical engineering technologies analysis,†SN Computer Science, vol. 1, no. 6, pp. 1–13, 2020.

S. Mostafavi and W. Shafik, “Fog computing architectures, privacy and security solutions,†Journal of Communications Technology, Electronics and Computer Science, vol. 24, pp. 1–14, 2019.

S. Sanakkayala, S.C. Joseph, A. Venkatesha, R. Polimera, R. S. Pawar et al, “Heart-beat monitoring of virtual machines for initiating failover operations in a data storage management system, using ping monitoring of target virtual machines,†Google Patents 15/716,386, 2018.

O. Akrivopoulos, I. Chatzigiannakis, C. Tselios, A. Antoniou, “On the deployment of healthcare applications over fog computing infrastructure,†IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), vol. 2, pp. 288-293, 2017.

M. Chiang and T. Zhang, “Fog and IoT: An overview of research opportunities,†IEEE Internet of things journal, vol. 3, no. 6, pp. 854-64, 2016.

F. E. Samann, S. R. Zeebaree and S. Askar, “IoT provisioning QoS based on cloud and fog computing,†Journal of Applied Science and Technology Trends. vol. 2, no. 01, pp. 29-40, 2021.

Y. Wu, H. N. Dai, H. Wang and K. K. Choo, “Blockchain-based privacy preservation for 5g-enabled drone communications,†IEEE Network. vol. 35, no.1, pp. 50-66, 2021.

Y. S. Patel, M. K. Mishra, B. S. Mishra, R. Misra, “Cloud of things assimilation with cyber physical system: a review,†Internet of Things: Enabling Technologies, Security and Social Implications, pp. 93-110, 2021.

E. E. Abel and A. L. Muhammad, “Management of WSN-enabled cloud internet of things: a review,†International Journal of Computing and Digital Systems. vol. 10, pp. 353-372, 2021.

E. B. Hansen and S. Bøgh, “Artificial intelligence and internet of things in small and medium-sized enterprises: A survey,†Journal of Manufacturing Systems, vol. 58, pp. 362-372, 2021.

A. Hajebrahimi, I. Kamwa, E. Delage, and M. Abdelaziz, “Adaptive distributionally robust optimization for electricity and electrified transportation planning,†IEEE Trans. Smart Grid, 2020.

A. J. Wilson, D. R. Reising, R. W. Hay, R. C. Johnson, A. A. Karrar et al., “Automated identification of electrical disturbance waveforms within an operational smart power grid,†IEEE Trans. Smart Grid, 2020.

A. B. Rjab and S. Mellouli, “Smart cities in the era of artificial intelligence and internet of things: promises and challenges,†Smart Cities and Smart Governance: Towards the 22nd Century Sustainable City, pp. 259-88, 2021.

T. Qayyum, Z. Trabelsi, A. W. Malik, K. Hayawi, “Multi-level resource sharing framework using collaborative fog environment for smart cities,†IEEE Access, vol. 9, pp. 21859-21869, 2021.

M. Kaur, R. Aron, “A systematic study of load balancing approaches in the fog computing environment,†The Journal of Supercomputing, vol. 4, pp. 1-46, 2021.

A. Suyyagh, J. G. Tong, and Z. Zilic, “Performance evaluation of meta-heuristics in energy-aware real-time scheduling problems,†Jordanian Journal of Computers and Information Technology (JJCIT), vol. 2, no. 1, pp. 168-185, 2016.

H. G. Abreha, C. J. Bernardos, A. D. Oliva, L. Cominardi and A. Azcorra, “Monitoring in fog computing: state-of-the-art and research challenges,†International Journal of Ad Hoc and Ubiquitous Computing, vol. 36, no. 2, pp. 114-130, 2021.

M. Keshavarznejad, M. H. Rezvani, S. Adabi, “Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms,†Cluster Computing, pp. 1-29, 2021.

T. Nguyen Gia et al., "Energy-efficient fog-assisted IoT system for monitoring diabetic patients with cardiovascular disease," Future Generation Computer Systems (FGCS), vol. 93, pp. 198–211, Apr. 2019.

X. Chen, Y. Zhou, B. He and L. Lv, “Energy-efficiency fog computing resource allocation in cyber physical internet of things systems,†IET Commun., vol. 13, no. 13, pp. 2003–2011, May 2019.

M. Abbasi, E. Mohammadi-Pasand, M. R. Khosravi, “Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing,†Computer Communications, vo.169, pp. 71-80, 2021.

W. Shafik and S. M. Matinkhah, “Admitting New Requests in Fog Networks According to Erlang B Distribution,†in 2019 27th Iranian Conference on Electrical Engineering (ICEE), Yazd, Iran, pp. 2016–2021, 2019.

W. Shafik, “A fast machine learning for beam selection in 5g unmanned aerial vehicle communications†M.Sc. dissertation, Yazd University, Iran, 2020.