Self-Adapting Acquisition of Sensor Data in Mobile Environment

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Abstract:

A mobile application meets the highly real-time requirement for data in sensor networks. However mobile applications have to handle many difficulties that do not exist in non-mobile environment. For example, the wireless network provided for mobile is slow, expensive and bandwidth-limited. So mobile application cannot acquire sensor data like a common application does, which will consume a large share of bandwidth, CPU and memory. To address this issue, we propose our design that will limit the concurrently acquisition of the sensor data with a self-adapting mechanism. It can lower the overhead of the system significantly and whats more, it hardly reduces the user experience even in the situation that the total acquisition frequency is limited.

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Periodical:

Advanced Materials Research (Volumes 756-759)

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2339-2344

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Cosm, Internet of Things Platform Connecting Devices and Apps for Real-Time Control and Data Storage, 2012; cosm. com.

Google Scholar

[2] R. T. Fielding, Architectural styles and the design of network-based software architectures, doctoral dissertation, Dept. Information and Computer Science, Univ. California, (2000).

Google Scholar

[3] Guohong Cao, A Scalable Low-Latency Cache Invalidation Strategy for Mobile Environments, Knowledge and Data Engineering, vol. 15, pp.1251-1265, Sept. -Oct. (2003).

DOI: 10.1109/tkde.2003.1232276

Google Scholar

[4] D. Guinard and T. Vlad, Towards the web of things: web mashups for embedded devices, Proceedings of the International World Wide Web Conferences, Apr. (2009).

Google Scholar

[5] R. Fielding, J. Gettys, J. Mogul, H. Frystyk, L. Masinter, P. Leach and T. Berners-Lee, Hypertext Transfer Protocol - HTTP/1. 1, IETF RFC 2616, June 1999; www. ietf. org/rfc/rfc2616. txt.

DOI: 10.17487/rfc2616

Google Scholar

[6] V. Loia, G. Fenza, D. Furno and C. De Maio, Swarm-based Approach to Evaluate Fuzzy Classification of Semantic Sensor Data, Pervasive Computing and Communications Workshops, IEEE CS, 2012, pp.308-313.

DOI: 10.1109/percomw.2012.6197501

Google Scholar

[7] M. Botts, G. Percivall, C. Reed and J. Davidson, OGC® Sensor Web Enablement: Overview And High Level Architecture, Proceedings of the 5th International ISCRAM Conference, 2008, pp.713-723.

DOI: 10.1007/978-3-540-79996-2_10

Google Scholar

[8] D. Crockford, The application/json Media Type for JavaScript Object Notation (JSON), IETF RFC 4627, July 2006; www. ietf. org/rfc/rfc4627. txt.

DOI: 10.17487/rfc4627

Google Scholar

[9] T. Bray, J. Paoli, C. M. Sperberg-McQueen, E. Maler, and F. Yergeau, Extensible Markup Language (XML) 1. 0, World Wide Web Consortium (W3C) note, Novemember 2008; www. w3. org/TR/REC-xml.

DOI: 10.1007/978-3-642-59944-6_8

Google Scholar