期刊
IEEE INTERNET OF THINGS JOURNAL
卷 8, 期 11, 页码 9344-9358出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2021.3056396
关键词
Servers; Task analysis; Edge computing; Cloud computing; Internet of Things; Urban areas; Delays; Mobile-edge computing (MEC); parked vehicle; time-related trajectory prediction; virtual edge server
资金
- Natural Science Foundation of China [61902282, 62002263]
- Natural Science Foundation of Tianjin [18JCYBJC85900, 18JCQNJC70200]
- Tianjin Municipal Education Commission Project for Scientific Research Plan [2018KJ155]
- Science and Technology Development Fund of Tianjin [JW1702]
- Science and Technology Project of Guangdong Province [2017KQNCX194]
The research introduces the concept of parking edge computing, utilizing parked vehicle resources in urban areas to assist edge servers in task offloading. A task scheduling algorithm and local task scheduling policy are designed, along with a time-related trajectory prediction model to enhance task offloading performance.
Vehicular edge computing has been a promising paradigm to offer low-latency and high reliability vehicular services for users. Nevertheless, for compute-intensive vehicle applications, most previous researches cannot perform them efficiently due to both the inadequate of infrastructure construction and the computing resource bottleneck of the edge server. Motivated by the fact that there is a large number of outside parked vehicles with rich and underutilized resources in the urban area, we propose the idea of parking edge computing, which makes use of the parked vehicles to assist edge servers in offloaded task handling. Specifically, on-street and off-street parked vehicles are first organized into parking clusters to act as virtual edge servers, participating in offloaded tasks execution in our framework. Second, a novel task scheduling algorithm is designed to jointly decide edge server selection and resource assignment. Furthermore, a local task scheduling policy is proposed as well, which reasonably allocates parked vehicles to perform the tasks with the aim of further improving task offloading performance. Finally, a time-related trajectory prediction model based on the random forest model is built, which helps to send back output result accurately. Our framework not only requires no additional infrastructure investment but also provides adequate computing resources. Simulation results based on a real city map and realistic traffic situations demonstrate that our framework provides more efficient and stable offloading services, especially in a large number of task requests condition.
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