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Sensors (Basel). 2017 Dec 29;18(1). pii: E82. doi: 10.3390/s18010082.

Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks.

Author information

1
School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China. laiyx@xmu.edu.cn.
2
Department of Automation, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China. yang@xmu.edu.cn.
3
School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China. jssu@xmu.edu.cn.
4
Department of Automation, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China. zhouqf@xmu.edu.cn.
5
College of Computer Science and Technology, Huaqiao University, Xiamen 360000, China. wangtian@hqu.edu.cn.
6
School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China. xmuzhanglu@stu.xmu.edu.cn.
7
School of Software, Xiamen University, 422 Siming South Road, Siming District, Xiamen 360000, China. xuyifan@stu.xmu.edu.cn.

Abstract

Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data.

KEYWORDS:

VANETs; data-gathering; event detection; mobile sensing

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