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Sensors 2017, 17(4), 815; doi:10.3390/s17040815

Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm

1
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Luoyu Road 129, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 16 January 2017 / Revised: 22 March 2017 / Accepted: 8 April 2017 / Published: 10 April 2017
(This article belongs to the Section Sensor Networks)
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Abstract

The efficient data access of streaming vehicle data is the foundation of analyzing, using and mining vehicle data in smart cities, which is an approach to understand traffic environments. However, the number of vehicles in urban cities has grown rapidly, reaching hundreds of thousands in number. Accessing the mass streaming data of vehicles is hard and takes a long time due to limited computation capability and backward modes. We propose an efficient streaming spatio-temporal data access based on Apache Storm (ESDAS) to achieve real-time streaming data access and data cleaning. As a popular streaming data processing tool, Apache Storm can be applied to streaming mass data access and real time data cleaning. By designing the Spout/bolt workflow of topology in ESDAS and by developing the speeding bolt and other bolts, Apache Storm can achieve the prospective aim. In our experiments, Taiyuan BeiDou bus location data is selected as the mass spatio-temporal data source. In the experiments, the data access results with different bolts are shown in map form, and the filtered buses’ aggregation forms are different. In terms of performance evaluation, the consumption time in ESDAS for ten thousand records per second for a speeding bolt is approximately 300 milliseconds, and that for MongoDB is approximately 1300 milliseconds. The efficiency of ESDAS is approximately three times higher than that of MongoDB. View Full-Text
Keywords: BeiDou bus network; streaming spatio-temporal mass data access; Apache Storm; Sensor Observation Service; cloud computing BeiDou bus network; streaming spatio-temporal mass data access; Apache Storm; Sensor Observation Service; cloud computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, L.; Chen, N.; Chen, Z. Efficient Streaming Mass Spatio-Temporal Vehicle Data Access in Urban Sensor Networks Based on Apache Storm. Sensors 2017, 17, 815.

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