Abstract
A participatory mobile sensing system is designed to enable clients to voluntarily collect environmental data using embedded sensors and a mobile device while going about their daily activities. Due to the spatio-temporal nature of the data, and the significant benefits of the data to the general public, it is necessary to employ an efficient and effective query processing model for the mobile clients to access the data that can be visualized via an interactive multimedia interface. This article introduces a unified on-demand and data broadcast model to serve queries in the context of a mobile sensing system. The contributions of this article include the following: (i) it presents a novel data structure and indexing method to support the system; (ii) it provides flexibility for the client to issue query using on-demand or broadcast channel according to the server load and broadcast schedule; (iii) it enables new data access and processing for the mobile client; and (iv) it is designed for a multiple channels/receivers environment in a 4G wireless network. The proposed model uses a holistic query processing approach for the mobile sensing system that offers substantial efficiency and autonomy for mobile clients when retrieving data. The results of the experiments undertaken affirm the effectiveness of its performance.
- AlphaSense: a sensor technology company (Online). http://www.alphasense.com (last accessed: Feb. 2011).Google Scholar
- Cambridge mobile urban sensing (Online). http://www.escience.cam.ac.uk/mobiledata (last accessed: Feb., 2011).Google Scholar
- Dept. of Environment, Climate Change, and Water: Air Quality Index (Online). 2011. http://www.environment.nsw.gov.au/aqms/aqi.htm (last accessed: Feb. 2011).Google Scholar
- Eisman, S. B., Miluzzo, E., Lane, N. D., Peterson, R. A., Ahn, G.-S., and Campbell, A. T. 2007. The BikeNet mobile sensing system for cyclist experience mapping. In Preceedings of the International Conference on Embedded Networked Sensor System (Sensys). 87--101. Google Scholar
Digital Library
- Environment Protection Authority of Victoria (Online). 2011. http://www.epa.vic.gov.au/air/monitoring/air-monitoring-report2009.asp (last accessed: Feb., 2011).Google Scholar
- Greaves, S., Issarayangyun, T., and Liu, Q. 2008. Exploring variability in pedestrian exposure to fine particulates (PM2.5) along a busy road. Atmos. Environ. 42, 8, 1665--1676.Google Scholar
Cross Ref
- Hsu, H.-H. and Chen, C.-C. 2010. RFID-based human behavior modeling and anomaly detection for elderly care. Mobile Inf. Syst. 6, 4, 341--354.Google Scholar
Digital Library
- Hu, Q., Lee, W. C., and Lee, D. L. 1999. Indexing techniques for wireless data broadcast under data clustering and scheduling. In Proceedings of the 8th International Conference on Information and Knowledge Management (CIKM). 351--358. Google Scholar
Digital Library
- Huang, J. L. and Chen, M.-S. 2002. Dependent data broadcasting for unordered queries in a multiple channel mobile environment. In Proceedings of the IEEE GLOBECOM, 972--976.Google Scholar
- Imielinski, T., Viswanathan S., and Badrinath, B. R. 1994. Energy Efficient Indexing on Air. In Proceedings of the ACM Sigmod Conference. 25--36. Google Scholar
Digital Library
- Imielinski, T., Viswanathan, S., and Badrinath, B. R. 1997. Data on Air: Organisation and Access, IEEE Trans. Knowl. Data Eng. 9, 3, 353--371. Google Scholar
Digital Library
- Interdynamics, Planimate<sup>TM</sup>-Animated Planning Platforms (online). http://www.interdynamics.com/products/planimate.html (last accessed Jan. 2013).Google Scholar
- Jamil, M., Shaikh, S. P., Shahzad, M., and Awais, Q. 2008. 4G: The future mobile technology. In Proceedings of the IEEE (TENCON). 1--6.Google Scholar
- Jayaputera, J. and Taniar, D. 2005. Data retrieval for location-dependent queries in a multi-cell wireless environment. Mobile Inf. Syst. 1, 2, 91--108. Google Scholar
Digital Library
- Lee, W. C. and Lee, D. L. 1996. Using Signature techniques for information filtering in wireless and mobile environments. J. Distrib. Paral. Datab. 4, 3, 205--227.Google Scholar
Cross Ref
- Link, M. S. and Dockery, D. W. 2010. Air pollution and the triggering of cardiac arrhythmias. Curr. Opin. Cardiol. 25, 1, 16--22.Google Scholar
Cross Ref
- Lo, S.-C. and Chen, A. L. P. 2007. Efficient index and data allocation for wireless broadcast services. Data Knowl. Eng. 60, 1, 235--55. Google Scholar
Digital Library
- MESSAGE: Mobile Environmental Sensing System Across Grid Environments (ONLINE). http://bioinf.ncl.ac.uk/message/ (last accessed: Feb., 2011).Google Scholar
- Mino, G., Barolli, L., Xhafa, F., Durresi, A., and Koyama, A. 2009. Implementation and performance evaluation of two fuzzy-based handover systems for wireless cellular networks. Mobile Inf. Syst. 5, 4, 339--361. Google Scholar
Digital Library
- Ohio EPA's AirOhio (Online). 2011. http://www.epa.ohio.gov/dapc/airohio/index.aspx (last accessed: Feb., 2011).Google Scholar
- Park, K. J., Song, M. B., and Hwang, C.-S. 2006. Adaptive data dissemination schemes for location-aware mobile services. J. Syst. Softw. 75, 5, 674--688. Google Scholar
Digital Library
- Prabhakara, K., Hua, K. A., and Jiang, N. 2000. Multi-level multi-channel air cache designs for broadcasting in a mobile environment. In Proceedings of the IEEE International Conference on Data Engineering (ICDE). 167--176. Google Scholar
Digital Library
- Taniar, D. and Rahayu, J. W. 2002. A taxonomy of indexing schemes for parallel database systems. Distrib. Parall. Datab. 12, 1, 73--106. Google Scholar
Digital Library
- Taniar, D. and Rahayu, W. 2004. Global parallel index for multi-processors database systems. Inf. Sci. 165, 1--2, 103--127. Google Scholar
Digital Library
- Tran, D. A., Hua, K. A., and Jiang, N. 2001. A generalized design for broadcasting on multiple physical-channel air-cache. In Proceedings of the ACM Symposium on Applied Computing (SAC). 387--392. Google Scholar
Digital Library
- Tran, Q. T., Taniar, D., and Safar, M. 2010. Bichromatic reverse nearest-neighbor search in mobile systems. IEEE Syst. J. 4, 2, 230--242.Google Scholar
Cross Ref
- Waluyo, A. B., Srinivasan, B., and Taniar, D. 2003. Optimal broadcast channel for data dissemination in mobile database environment. In Proceedings of the 5th Advanced Parallel Programming Technologies (APPT). Lecture Notes in Computer Science. Vol. 2834, Springer, 655--664.Google Scholar
Cross Ref
- Waluyo, A. B., Srinivasan, B., and Taniar, D. 2004. A taxonomy of broadcast indexing schemes for multi channel data dissemination in mobile database. In Proceedings of the 18th International Conference on Advanced Information Networking and Applications (AINA). 1, 213--218. Google Scholar
Digital Library
- Waluyo, A. B., Srinivasan, B., and Taniar, D. 2005a. Research in mobile database query optimization and processing. Mobile Inf. Sys. 1, 4, 225--252. Google Scholar
Digital Library
- Waluyo, A. B., Srinivasan, B., and Taniar, D. 2005b. Research on location-dependent queries in mobile databases. Int. J. Comput. Syst. Sci. Eng. 20, 2, 79--95.Google Scholar
- Waluyo, A. B., Rahayu, W., Taniar, D., and Srinivasan, B. 2011a. A novel structure and access mechanism for mobile broadcast data in digital ecosystems. IEEE Transactions on Industrial Electronics. 58, 6, 2173--2182.Google Scholar
Cross Ref
- Waluyo, A. B., Taniar, D., Rahayu, W., and Srinivasan, B. 2011b. Mobile broadcast services with MIMO antennae in 4G wireless networks. World Wide Web J. 14, 4, 351--375. Google Scholar
Digital Library
- World Health Organisation: Air Pollution (Online). 2011. http://www.who.int/topics/air_pollution/en (last accessed: Feb. 2011).Google Scholar
- Xu, J., Lee, W. C., Tang, X., Gao, Q., and Li, S. 2006. An error-resilient and tunable distributed indexing scheme for wireless data broadcast. IEEE Trans. Knowl. Data Eng. 18, 3, 392--404. Google Scholar
Digital Library
- Xu, J., Zheng, B., Lee, W.-C., and Lee, D. L. 2003. Energy efficient index for querying location-dependent data in mobile broadcast environments. In Proceedings of the 19th IEEE International Conference on Data Engineering (ICDE). 239--250.Google Scholar
- Xuan, K., Zhao, G., Taniar, D., and Srinivasan, B. 2008. Continuous range search query processing in mobile navigation. In Proceedings of the 14th International Conference on Parallel and Distributed Systems (ICPADS). 361--368. Google Scholar
Digital Library
- Xuan, K., Zhao, G., Taniar, D., Rahayu, W., Safar, M., and Srinivasan, B. 2011. Voronoi-based range and continuous range query processing in mobile databases. J. Comput. Syst. Sci. 77, 4, 637--651. Google Scholar
Digital Library
- Zheng, B., Lee, W.-C., and Lee, D. L. 2004. Spatial queries in wireless broadcast systems. Wireless Netw. 10, 6, 723--736. Google Scholar
Digital Library
- Zou, B., Wilson, J. G., Zhan, F. B., and Zeng, Y. 2009. Air pollution exposure assessment methods utilized in epidemiological studies. J. Environ. Monitor. 11, 3, 475--490.Google Scholar
Cross Ref
Index Terms
Mobile query services in a participatory embedded sensing environment
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