Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 7, Issue 7, Pages 5783-5791Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2949633
Keywords
Object detection; Servers; Adaptation models; Feature extraction; Context modeling; Cloud computing; Computational modeling; Context-aware; edge-cloud cooperation; object detection; vehicular networks
Categories
Funding
- National Natural Science Foundation of China [61772387, 61802296]
- Fundamental Research Funds for the Central Universities [JB180101]
- China Post-Doctoral Science Foundation [2017M620438]
- Fundamental Research Funds of Ministry of Education and China Mobile [MCM20170202]
- National Natural Science Foundation of Shaanxi Province [2019ZDLGY03-03, 2019JQ-375]
- ISN State Key Laboratory
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Due to high mobility and high dynamic environments, object detection for vehicular networks is one of the most challenging tasks. However, the development of integration techniques, such as software-defined networking (SDN) and network function visualization (NFV), in networking, caching, and computing provides us with new approaches. In this article, we propose a novel context-aware object detection method based on edge-cloud cooperation. Specifically, an object detection model based on deep learning is established in the cloud server. Different from other methods, to further explore the underlying inner spatial features of collected images, the visual objects of images are regarded as nodes and the spatial relations between objects as edges, then a type of message-passing method is employed to update the nodes' features. In the mobile edge computing (MEC) servers, the context information and captured images of the vehicular environments are extracted and then are used to adjust the object detection model from the cloud server. In this way, the cloud server cooperates with the MEC servers to realize context-aware object detection, which improves the adaptation and performance of the detection model under different scenarios. The simulation results also demonstrate that the proposed method is more accurate and faster than the previous methods.
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