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Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC
- Source :
- Jisuanji kexue, Vol 49, Iss 2, Pp 304-311 (2022)
- Publication Year :
- 2022
- Publisher :
- Editorial office of Computer Science, 2022.
-
Abstract
- In the internet of vehicles systems that combining mobile edge computing (MEC) with non-orthogonal multiple access (NOMA) technology,to solve the high latency problem when user processes computationally intensive and latency-sensitive task,a strategy of task offloading,migration and cache optimization based on game theory and Q learning is proposed.Firstly,the mo-del of offloading delay,migration delay and cache delay of the internet of vehicles task based on NOMA-MEC is established.Se-condly,we use the cooperative game method to obtain the optimal user group to optimize the offloading delay.Finally,in order to avoid local optima,the Q learning algorithm is utilized to optimize the joint delay of the migration cache in the user group.The simulation results show that compared with other solutions,the proposed algorithm can effectively improve the offloading efficiency and reduce the task delay by about 22% to 43%.
Details
- Language :
- Chinese
- ISSN :
- 1002137X
- Volume :
- 49
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Jisuanji kexue
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.516204ba04844a76ad340b74e53ea29c
- Document Type :
- article
- Full Text :
- https://doi.org/10.11896/jsjkx.210100157