Chameleon: Latency and Resolution Aware Task Offloading for Visual-Based Assisted Driving

Chao Zhu, Yi-Han Chiang, Abbas Mehrabi, Yu Xiao*, Antti Yla-Jaaski, Yusheng Ji

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
35 Downloads (Pure)

Abstract

Emerging visual-based driving assistance systems involve time-critical and data-intensive computational tasks, such as real-time object recognition and scene understanding. Due to the constraints on space and power capacity, it is not feasible to install extra computing devices on all the vehicles. To solve this problem, different scenarios of vehicular fog computing have been proposed, where computational tasks generated by vehicles can be sent to and processed at fog nodes located for example at 5G cell towers or moving buses. In this paper, we propose Chameleon, a novel solution for task offloading for visual-based assisted driving. Chameleon takes into account the spatiotemporal variation in service demand and supply, and provides latency and resolution aware task offloading strategies based on partially observable Markov decision process (POMDP). To evaluate the effectiveness of Chameleon, we simulate the availability of vehicular fog nodes at different times of day based on the bus trajectories collected in Helsinki, and use the real-world performance measurements of visual data transmission and processing. Compared with adaptive and random task offloading strategies, the POMDP-based offloading strategies provided by Chameleon shortens the average service latency of task offloading by up to 65% while increasing the average resolution level of processed images by up to 83%.

Original languageEnglish
Article number8768075
Pages (from-to)9038-9048
Number of pages11
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number9
DOIs
Publication statusPublished - Sep 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • Vehicular fog computing
  • task offloading
  • assisted driving
  • POMDP
  • RESOURCE-ALLOCATION
  • INTERNET

Projects

DataFog: A Data-Driven Platform for Capacity and Resource Management in Vehicular Fog Computing

Xiao, Y., Noreikis, M., Zhu, C., Mao, W., Akgul, Ö., Zhanabatyrova, A. & Li, X.

01/01/201931/12/2022

Project: Academy of Finland: Other research funding

5G-MOBIX: 5G for cooperative & connected automated MOBIility on X-border corridors

Xiao, Y., Zhanabatyrova, A., Pastor Figueroa, G., Li, X., Lundström, P. & Akgul, Ö.

01/11/201831/12/2022

Project: EU: Framework programmes funding

PriMO-5G: Virtual Presence in Moving Objects through 5G

Mutafungwa, E., Jäntti, R., Menta, E., Lassila, P. & Sheikh, M.

01/07/201831/12/2021

Project: EU: Framework programmes funding

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