4.6 Article

Mobile Vehicular Offloading With Individual Mobility

期刊

IEEE ACCESS
卷 8, 期 -, 页码 30706-30719

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2956229

关键词

Wireless fidelity; Delays; Mobile handsets; Predictive models; Data transfer; Vehicles; Urban areas; V2I; mobile device; offloading; prediction; LSTM

资金

  1. NSFC [61572113]
  2. China Postdoctoral Science Foundation [2014M562308]
  3. Science and Technology Achievements Transformation Demonstration Project of Sichuan Province of China [2018CC0094]
  4. Fundamental Research Funds for the Central Universities [ZYGX2015J155, ZYGX2016J084, ZYGX2016J195, ZYGX2019J075]

向作者/读者索取更多资源

In vehicular communication, roadside infrastructure, such as WiFi APs, often requires a large amount of investment. In this paper, we propose the idea of Mobile Vehicular Offloading (MoVeOff), which doesn't require extra investment, but allows data transfer from on-board devices to mobile devices of drivers and passengers, for uploading to the Internet in the future. When they arrive at their homes, offices, or other places where WiFi connection is available, vehicular data will be offloaded in a delay-tolerant manner, by the ferrying of mobile devices. We build a realistic system to investigate the regularities in people's daily travelling and WiFi usage, analyze individual mobility, and establish a Long Short Term Memory (LSTM) model to predict one's future WiFi connectivity. Moreover, a mobility-aware routing scheme is developed for inter-vehicle communication. Each vehicle broadcasts its expected offloading probability and delay, so that messages are dynamically delivered to the nodes, whose offloading can guarantee delay and delivery ratio bounds required by applications. Thus, our scheme overcomes traditional opportunistic forwarding, and introduces predictable ferrying guaranteed by individual mobility. Through system running and simulations, we demonstrate that our scheme provides extra and stable offloading service for delay-tolerant data in the areas with sparse roadside infrastructures.

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