Using on-the-move mining for mobile crowdsensing

Sherchan, W, Jayaraman, P, Krishnaswamy, S, Zaslavsky, A, Loke, S and Sinha, A 2012, 'Using on-the-move mining for mobile crowdsensing', in Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (MDM 2012), Bengaluru, Karnataka, India, 23 - 26 July 2012, pp. 115-124.


Document type: Conference Paper
Collection: Conference Papers

Title Using on-the-move mining for mobile crowdsensing
Author(s) Sherchan, W
Jayaraman, P
Krishnaswamy, S
Zaslavsky, A
Loke, S
Sinha, A
Year 2012
Conference name MDM 2012
Conference location Bengaluru, Karnataka, India
Conference dates 23 - 26 July 2012
Proceedings title Proceedings of the 2012 IEEE 13th International Conference on Mobile Data Management (MDM 2012)
Publisher IEEE
Place of publication United States
Start page 115
End page 124
Total pages 10
Abstract In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing.
Subjects Ubiquitous Computing
Mobile Technologies
Keyword(s) Context-Aware
Data collection
Efficient strategy
Energy usage
Facebook
Mobile data
Mobile data mining
Mobile users
Real-time data
Real-time information
Scalable approach
Sensory information
Social media
Specific location
DOI - identifier 10.1109/MDM.2012.58
Copyright notice © 2012 IEEE
ISBN 9781467317962
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