Abstract
The future large-scale deployment of pervasive sensor network infrastructures calls for mechanisms enabling the extraction of general-purpose data at limited energy costs. The approach presented in this article relies on a simple algorithm to let a sensor network self-organize a virtual partitioning in correspondence to spatial regions characterized by similar sensing patterns, and to let distributed aggregation of sensorial data take place on a per-region basis. The result of this process is that a sensor network can be modeled as a collection of virtual macro sensors, each associated to a well-characterized region of the physical environment. Within each region, each physical sensor has the local availability of aggregated data about its region and is able to act as an access point to such data. This feature promises to be very suitable for a number of emerging usage scenarios. Our approach is described and evaluated in both a simulation environment and a real test bed, and quantitatively compared with related works in the area. Current limitations and areas of future development are also discussed.
- Ahn, Y.-Y., Bagrow, J. P., and Lehmann, S. 2010. Slink communities reveal multiscale complexity in networks. Nature 1038, 1--5.Google Scholar
- Bandini, S., Bonomi, A., Vizzari, G., and Acconci, V. 2011. Ca-based self-organizing environments. J. Supercomput. 57, 2, 109--120. Google Scholar
Digital Library
- Baumgarten, M., Bicocchi, N., Mulvenna, M., and Zambonelli, F. 2006. Self-organizing knowledge networks for smart world infrastructures. In Proceedings of the International Conference on Self-Organization in Multiagent Systems.Google Scholar
- Bicocchi, N., Baumgarten, M., Kusber, R., Mamei, M., Mulvenna, M., and Zambonelli, F. 2010. Self-organizing data ecologies for pervasive autonomic services: the knowledge networks approach. IEEE Trans. Syst. Man Cybern. A Syst. Humans 40, 4, 789--802. Google Scholar
Digital Library
- Boulis, A., Ganerival, S., and Srivastava, M. 2003. Aggregation in sensor network: An energy-accuracy trade-off. In Proceedings of the International Workshop on Sensor and Actuator Network Protocols and Applications.Google Scholar
- Castelli, G., Rosi, A., Mamei, M., and Zambonelli, F. 2007. A simple model and infrastructure for context-aware browsing of the world. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication. Google Scholar
Digital Library
- Catterall, E., Laerhoven, K., and Strohbach, M. 2002. Self-organization in ad-hoc sensor networks: An empirical study. In Proceedings of the International Conference on Simulation and Synthesis of Living Systems.Google Scholar
- Cormode, G., Garofalaki S, M., Muthukrishnan, S., and Rastogi, R. 2005. Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In Proceedings of the ACM International Conference on Management of Data. Google Scholar
Digital Library
- Corradi, A., Leonardi, L., and Zambonelli, F. 1999. Diffusive load balancing policies for dynamic applications. IEEE Concurrency 7, 11, 22--31. Google Scholar
Digital Library
- Costa, P., Mottola, L., Murphy, A., and Picco, P. 2007. Programming wireless sensor networks with the teenylime middleware. In Proceedings of the ACM Middleware Conference. Google Scholar
Digital Library
- Curino, C., Giani, M., Giorgetta, M., Giusti, A., Murphy, A., and Picco, G. 2005. Mobile data collection in sensor networks: The tinylime middleware. J. Pervasive Mobile Comput. 4, 1, 446--469. Google Scholar
Digital Library
- Dimaki S, A., Sarwate, A., and Wainwriht, M. 2006. Geographic gossip: Efficient aggregation for sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks. Google Scholar
Digital Library
- Gehrke, J. and Madden, S. 2004. Query processing in sensor networks. IEEE Pervasive Comput. 3, 1, 46--65. Google Scholar
Digital Library
- Jelasity, M. and Kermarrec, A. 2006. Ordered slicing of very large-scale overlay networks. In Proceedings of the IEEE International Conference on Peer-to-Peer Computing. Google Scholar
Digital Library
- Jelasity, M., Montresor, A., and Babaoglu, O. 2005. Gossip-based aggregation in large dynamic networks. ACM Trans. Comput. Syst. 23, 3, 219--252. Google Scholar
Digital Library
- Jiang, X., Polastre, J., and Culler, D. 2005. Perpetual environmentally powered sensor networks. In Proceedings of the International Symposium on Information Processing in Sensor Networks. Google Scholar
Digital Library
- Kabaday, S. and Julien, C. 2007. Scenes: Abstracting interaction in immersive sensor networks. J. Pervasive Mobile Comput. 3, 6, 635--658. Google Scholar
Digital Library
- Khelil, A., Shaikh, F. K., Ali, A., and Suri, N. 2009. gmap: Efficient construction of global maps for mobility-assisted wireless sensor networks. In Proceedings of the International Conference on Wireless On-Demand Network Systems and Services. Google Scholar
Digital Library
- Lane, N., Xu, Y., Lu, H., Campbell, A., Choudhury, T., and Eisenman, S. 2011. Exploiting social networks for large-scale human behavior modeling. IEEE Pervasive Comput. 10, 4, 45--53. Google Scholar
Digital Library
- Lee, Y., Iyengar, S., Min, C., Ju, Y., Kang, S., Park, T., Lee, J., Rhee, Y., and Song, J. 2012. Mobicon: A mobile context-monitoring platform user context is defined by data generated through everyday physical activity in sensor-rich, resource-limited mobile environments. Comm. ACM 55, 3, 54--65. Google Scholar
Digital Library
- Lotfinezhad, M., Liang, B., and Sousa, E. 2008. Adaptive cluster-based data collection in sensor networks with direct sink access. IEEE Trans. Mobile Comput. 7, 7, 884--897. Google Scholar
Digital Library
- Lu, C., Xing, G., Chipara, O., Fok, C., and Bhattacharya, S. 2005. A spatiotemporal query service for mobile users in sensor networks. In Proceedings of the International Conference on Distributed Computing Systems. Google Scholar
Digital Library
- Madden, S. and Hellerstein, J. 2002. Distributing queries over low-power wireless sensor networks. In Proceedings of the ACM International Conference on Management of Data. Google Scholar
Digital Library
- Mottola, G. and Picco, G. P. 2006. Logical neighborhoods: A programming abstraction for wireless sensor networks. In Proceedings of the IEEE International Conference on Distributed Computing in Sensor Systems. Google Scholar
Digital Library
- Mottola, L. and Picco, G. 2011. Programming wireless sensor networks: Fundamental concepts and state-of-the-art. ACM Comput. Surv. 43, 3. Google Scholar
Digital Library
- Muller, R. and Alonso, G. 2005. Shared queries in sensor networks for multi-user support. Tech. rep. 508. ETH, Zurich.Google Scholar
- Newton, R. and Welsh, M. 2004. Region streams: Functional macroprogramming for sensor networks. In Proceedings of the International Workshop on Data Management for Sensor Networks. Google Scholar
Digital Library
- Panangadan, A. and Sukhatme, G. 2005. Data segmentation for region detection in a sensor network. In Tech. rep. 05-005. University of Southern California.Google Scholar
- Ramanathan, N., Balzano, L., Estrin, D., Hansen, M., Harmon, T., Jay, J., Kaise R, W., and Khatme, G. 2006. Designing wireless sensor networks as a shared resource for sustainable development. In Proceedings of the International Conference on Information and Communication Technologies and Development.Google Scholar
- Rosi, A., Bicocchi, N., Castelli, G., Mamei, M., and Zambonell I, F. 2011. Landslide monitoring with sensor networks: Experiences and lessons learnt from a real-world deployment. Int. J. Sensor Netw. 10, 3. Google Scholar
Digital Library
- Sakaki, T., Okazaki, M., and Matsuo, Y. 2010. Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the International World Wide Web Conference. Google Scholar
Digital Library
- Sarkar, R., Zhu, X., and Gao, J. 2007. Hierarchical spatial gossip for multi-resolution representations in sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks. Google Scholar
Digital Library
- Schoellhammer, T., Greenstein, B., and Estrin, D. 2006. Hyper: A routing protocol to support mobile users of sensor networks. In Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing.Google Scholar
- Skraba, P., Fang, Q., Nguyen, A., and Guibas, L. 2006. Sweeps over wireless sensor networks. In Proceedings of the International Conference on Information Processing in Sensor Networks. Google Scholar
Digital Library
- Want, R. and Pering, T. 2005. System challenges for ubiquitous and pervasive computing. In Proceedings of the ACM International Conference on Software Engineering. Google Scholar
Digital Library
- Werner-Allen, G., Tewari, G., Patel, A., Welsh, M., and Nagpal, R. 2005. Firefly-inspired sensor network synchronicity with realistic radio effects. In Proceedings of the ACM International Conference on Embedded Networked Sensor Systems. Google Scholar
Digital Library
- Yang, H., Ye, F., and Sikdar, B. 2008. A swarm intelligence based protocol for data acquisition in networks with mobile sinks. IEEE Trans. Mobile Comput. 7, 8, 931--945. Google Scholar
Digital Library
- Younis, O., Fahmy, S., and Santi, P. 2005. An architecture for robust sensor network communications. Internat. J. Distrib. Sensor Netw. 1, 3, 305--327.Google Scholar
Cross Ref
- Zhu, X., Sarkar, R., Gao, J., and Mitchell, J. 2008. Light-weight contour tracking in wireless sensor networks. In Proceedings of the IEEE Conference on Computer Communications.Google Scholar
Index Terms
Self-organizing virtual macro sensors
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