Knowledge Agora



Similar Articles

Title Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network
ID_Doc 37595
Authors Wang, MQ; Mao, JY; Zhao, W; Han, XY; Li, MY; Liao, CJ; Sun, HM; Wang, KX
Title Smart City Transportation: A VANET Edge Computing Model to Minimize Latency and Delay Utilizing 5G Network
Year 2024
Published Journal Of Grid Computing, 22.0, 1
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.
PDF

Similar Articles

ID Score Article
39294 Farooqi, AM; Alam, MA; Hassan, SI; Idrees, SM A Fog Computing Model for VANET to Reduce Latency and Delay Using 5G Network in Smart City Transportation(2022)Applied Sciences-Basel, 12, 4
36820 Li, M; Si, PB; Zhang, YH Delay-Tolerant Data Traffic to Software-Defined Vehicular Networks With Mobile Edge Computing in Smart City(2018)Ieee Transactions On Vehicular Technology, 67, 10
35960 Ren, XD; Vashisht, S; Aujla, GS; Zhang, PY Drone-Edge Coalesce for Energy-Aware and Sustainable Service Delivery for Smart City Applications(2022)
40607 Tufail, A; Namoun, A; Alrehaili, A; Ali, A A Survey on 5G Enabled Multi-Access Edge Computing for Smart Cities: Issues and Future Prospects(2021)International Journal Of Computer Science And Network Security, 21, 6
41971 Wang, SG; Zhao, YL; Xu, JL; Yuan, J; Hsu, CH Edge server placement in mobile edge computing(2019)
40402 Liu, ZR A Multi-joint Optimisation Method for Distributed Edge Computing Resources in IoT-Based Smart Cities(2023)Journal Of Grid Computing, 21, 4
43402 Madamori, O; Max-Onakpoya, E; Erhardt, GD; Baker, CE Enabling Opportunistic Low-cost Smart Cities By Using Tactical Edge Node Placement(2021)
Scroll