Smart City Gnosys

Smart city article details

Title	A Fog Computing Service Placement For Smart Cities Based On Genetic Algorithms
ID_Doc	1711
Authors	Canali C.; Lancellotti R.
Year	2019
Published	CLOSER 2019 - Proceedings of the 9th International Conference on Cloud Computing and Services Science
DOI	http://dx.doi.org/10.5220/0007699400810089
Abstract	The growing popularity of the Fog Computing paradigm is driven by the increasing availability of large amount of sensors and smart devices on a geographically distributed area. The scenario of a smart city is a clear example of this trend. As we face an increasing presence of sensors producing a huge volume of data, the classical cloud paradigm, with few powerful data centers that are far away from the data sources, becomes inadequate. There is the need to deploy a highly distributed layer of data processors that filter, aggregate and pre-process the incoming data according to a fog computing paradigm. However, a fog computing architecture must distribute the incoming workload over the fog nodes to minimize communication latency while avoiding overload. In the present paper we tackle this problem in a twofold way. First, we propose a formal model for the problem of mapping the data sources over the fog nodes. The proposed optimization problem considers both the communication latency and the processing time on the fog nodes (that depends on the node load). Furthermore, we propose a heuristic, based on genetic algorithms to solve the problem in a scalable way. We evaluate our proposal on a geographic testbed that represents a smart-city scenario. Our experiments demonstrate that the proposed heuristic can be used for the optimization in the considered scenario. Furthermore, we perform a sensitivity analysis on the main heuristic parameters. Copyright © 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
Author Keywords	Fog computing; Genetic algorithms; Optimization model; Smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
2379	0.925	De Queiroz T.A.; Canali C.; Iori M.; Lancellotti R.	A Location-Allocation Model For Fog Computing Infrastructures	CLOSER 2020 - Proceedings of the 10th International Conference on Cloud Computing and Services Science (2020)
26780	0.915	Apat H.K.; Goswami V.; Sahoo B.; Barik R.K.; Saikia M.J.	Fog Service Placement Optimization: A Survey Of State-Of-The-Art Strategies And Techniques	Computers, 14, 3 (2025)
18375	0.915	Maiti P.; Apat H.K.; Kumar A.; Sahoo B.; Turuk A.K.	Deployment Of Multi-Tier Fog Computing System For Iot Services In Smart City	International Symposium on Advanced Networks and Telecommunication Systems, ANTS, 2019-December (2019)
26772	0.904	Singh S.; Vidyarthi D.P.	Fog Node Placement Using Multi-Objective Genetic Algorithm	International Journal of Information Technology (Singapore), 16, 2 (2024)
20717	0.894	Shaik S.; Baskiyar S.	Distributed Service Placement In Hierarchical Fog Environments	Sustainable Computing: Informatics and Systems, 34 (2022)
40898	0.892	Negi V.; Joshi D.; Sharma A.	Optimizing Task Allocation In Fog-Based Iot For Smart City Solutions	Citizen-Centric Artificial Intelligence for Smart Cities (2025)
35080	0.891	Gargees R.S.	Leveraging Fog Computing For Geographically Distributed Smart Cities	2022 IEEE 2nd Conference on Information Technology and Data Science, CITDS 2022 - Proceedings (2022)
20661	0.89	Beraldi R.; Canali C.; Lancellotti R.; Mattia G.P.	Distributed Load Balancing For Heterogeneous Fog Computing Infrastructures In Smart Cities	Pervasive and Mobile Computing, 67 (2020)
647	0.888	Butt A.A.; Khan S.; Ashfaq T.; Javaid S.; Sattar N.A.; Javaid N.	A Cloud And Fog Based Architecture For Energy Management Of Smart City By Using Meta-Heuristic Techniques	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019 (2019)
8870	0.888	de Queiroz T.A.; Canali C.; Iori M.; Lancellotti R.	An Optimization View To The Design Of Edge Computing Infrastructures For Iot Applications	Internet of Things (2022)