Smart City Gnosys

Smart city article details

Title	Smart City Transportation: A Vanet Edge Computing Model To Minimize Latency And Delay Utilizing 5G Network
ID_Doc	50571
Authors	Wang M.; Mao J.; Zhao W.; Han X.; Li M.; Liao C.; Sun H.; Wang K.
Year	2024
Published	Journal of Grid Computing, 22, 1
DOI	http://dx.doi.org/10.1007/s10723-024-09747-5
Abstract	Smart cities cannot function without autonomous devices that connect wirelessly and enable cellular connectivity and processing. Edge computing bridges mobile devices and the cloud, giving mobile devices access to computing, memory, and communication capabilities via vehicular ad hoc networks (VANET). VANET is a time-constrained technology that can handle requests from vehicles in a shorter amount of time. The most well-known problems with edge computing and VANET are latency and delay. Any congestion or ineffectiveness in this network can result in latency, which affects its overall efficiency. The data processing in smart city affected by latency can produce irregular decision making. Some data, like traffics, congestions needs to be addressed in time. Delay decision making can make application failure and results in wrong information processing. In this study, we created a probability-based hybrid Whale -Dragonfly Optimization (p-H-WDFOA) edge computing model for smart urban vehicle transportation that lowers the delay and latency of edge computing to address such issues. The 5G localized Multi-Access Edge Computing (MEC) servers were additionally employed, significantly reducing the wait and the latency to enhance the edge technology resources and meet the latency and Quality of Service (QoS) criteria. Compared to an experiment employing a pure cloud computing architecture, we reduced data latency by 20%. We also reduced processing time by 35% compared to cloud computing architecture. The proposed method, WDFO-VANET, improves energy consumption and minimizes the communication costs of VANET.
Author Keywords	Edge computing; Internet of things; Internet of vehicles; Smart city development; Vehicular ad hoc network

Similar Articles

Id	Similarity	Authors	Title	Published
1710	0.947	Farooqi A.M.; Alam M.A.; Hassan S.I.; Idrees S.M.	A Fog Computing Model For Vanet To Reduce Latency And Delay Using 5G Network In Smart City Transportation	Applied Sciences (Switzerland), 12, 4 (2022)
5818	0.91	Maruthamuthu R.; Patel N.; Yawanikha T.; Jayasree S.; Alsalami Z.; Subbarao S.P.V.	A Way To Design Fog Computing Model For 5G Network Using Vanet	2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024 (2024)
25414	0.904	El-Sayed, H; Chaqfeh, M	Exploiting Mobile Edge Computing For Enhancing Vehicular Applications In Smart Cities	SENSORS, 19, 5 (2019)
26799	0.894	Rehman M.A.U.; Salah Ud Din M.; Mastorakis S.; Kim B.-S.	Foggyedge: An Information-Centric Computation Offloading And Management Framework For Edge-Based Vehicular Fog Computing	IEEE Intelligent Transportation Systems Magazine, 15, 5 (2023)
8566	0.893	Medeiros T.C.; Soares E.; Vieira Campos C.A.	An Intelligent Transportation System Application Using Mobile Edge Computing	Proceedings - IEEE Symposium on Computers and Communications, 2021-September (2021)
35879	0.889	Chaymae T.; Mhamed R.; Soumia Z.	Machine Learning And 5G Edge Computing For Intelligent Traffic Management	International Journal of Advanced Computer Science and Applications, 16, 6 (2025)
21801	0.888	Laha M.; Kamble S.; Datta R.	Edge Nodes Placement In 5G Enabled Urban Vehicular Networks: A Centrality-Based Approach	26th National Conference on Communications, NCC 2020 (2020)
21773	0.885	Parveen Banu S.; Patil Y.M.; Somasundaram R.; Santhosh C.; Singh D.P.; Manikandan G.	Edge Computing-Based Short-Term Traffic Flow Forecast For The Smart City Employing 5G Internet Vehicles	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
32658	0.885	Rathore R.; Kaushik P.; Sikarwar S.S.; Joshi H.; Mishra A.K.; Hudda Y.	Intelligent Transportation Systems Make Use Of Fog And Edge Computing For Navigation	2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024 (2024)
40685	0.884	Peyman M.; Fluechter T.; Panadero J.; Serrat C.; Xhafa F.; Juan A.A.	Optimization Of Vehicular Networks In Smart Cities: From Agile Optimization To Learnheuristics And Simheuristics	Sensors, 23, 1 (2023)