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Social Prediction-Based Handover in Collaborative-Edge-Computing-Enabled Vehicular Networks
- Source :
- IEEE Transactions on Computational Social Systems. 9:207-217
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Collaborative edge computing (CEC) can realize the cooperation and integration of heterogeneous resources distributed in adjacent areas, increasing the overall resource utilization efficiency. In a CEC-supported heterogeneous vehicular network composed of different access solutions, including cellular vehicle-to-everything (C-V2X) and dedicated short-range communications (DSRC), good network connections can guarantee timely access to edge resources. How to maintain stable and high-quality network connections for vehicles is a crucial issue. With traditional received signal strength (RSS)-based handover schemes, vehicles may encounter severe ping-pong effects and even direct handover failures leading to data packet loss. In this article, to overcome the frequent handover problem caused by vehicles' high-speed motion and the ever-changing network environment, we propose a trajectory prediction-based handover scheme. In this scheme, the sojourn time of a vehicle staying in each candidate network's coverage can be obtained through a social long short-term memory (social-LSTM)-based prediction model. Together with the signal strength, available bandwidth, and cost, the sojourn time is also taken as a handover decision attribute parameter. Simulation results show that our proposed scheme can reduce the number of handovers effectively.
- Subjects :
- Vehicular ad hoc network
business.industry
Computer science
Network packet
RSS
computer.file_format
Dedicated short-range communications
Human-Computer Interaction
Handover
Modeling and Simulation
Bandwidth (computing)
Enhanced Data Rates for GSM Evolution
business
computer
Social Sciences (miscellaneous)
Edge computing
Computer network
Subjects
Details
- ISSN :
- 23737476
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Computational Social Systems
- Accession number :
- edsair.doi...........58d77d84be1b06aa1e8443900efbcf1f