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Dynamic collaborative in-network event detection in wireless sensor networks

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Abstract

Many applications of wireless sensor networks (WSNs) require events be detected around moving targets and delivered to a user, whose location may also change over time. Consequently, it is not desirable to have a single sensor node serving as the sink node for centralized event processing. Furthermore, an event usually occurs across different locations and lasts for a period of time that are unknown in advance. Hence it cannot be detected by a static set of sensor nodes. In this paper, we propose a general framework for such dynamic in-network event detection in WSNs. This framework enables a flexible number of sensor nodes to dynamically collaborate in detecting and delivering any specified event. So far there has been no such work reported in the literature. We have designed a protocol for the sensor nodes to autonomously determine whether they should participate in and which tasks they should perform to collaboratively process an event. We have implemented a prototype of the proposed framework using MicaZ motes and evaluated it through both simulations and experiments. The results demonstrate that our framework is effective and efficient in terms of energy cost and event processing delay.

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References

  1. Abdelzaher, T., Blum, B., Cao, Q., Chen, Y., Evans, D., George, J., George, S., Gu, L., He, T., Krishnamurthy, S., Luo, L., Son, S., Stankovic, J., Stoleru, R., & Wood, A. (2004). Envirotrack: Towards an environmental computing paradigm for distributed sensor networks. ICDCS.

  2. An, W., Lin, J., Luo, H., & Ci, S. (2013). Significance-based energy-efficient path selection for multi-source underwater sensor networks. International Journal of Sensor Networks, 13(1), 30–43.

    Article  Google Scholar 

  3. Bhatti, S., Carlson, J., Dai, H., Deng, J., Rose, J., Sheth, A., et al. (2005). Mantis os: An embedded multithreaded operating system for wireless micro sensor platforms. Mobile Networks and Applications, 10(4), 563–579. doi:10.1145/1160162.1160178.

    Article  Google Scholar 

  4. Cai, Z., Ji, S., & Li, J. (2012). Data caching-based query processing in multi-sink wireless sensor networks. International Journal of Sensor Networks, 11(2), 109–125. doi:10.1504/IJSNET.2012.045960.

    Article  Google Scholar 

  5. Chakravarthy, S., Krishnaprasad, V., Anwar, E., & Kim, S.K. (1994). Composite events for active databases: Semantics, contexts and detection. In: VLDB.

  6. Chakravarthy, S., & Mishra, D. (1994). Snoop: An expressive event specification language for active databases. Data & Knowledge Engineering, 14(1), 1–26. doi:10.1016/0169-023X(94)90006-X.

    Article  Google Scholar 

  7. Chen, W. P., Hou, J., & Sha, L. (2004). Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Transactions on Mobile Computing, 3(3), 258–271. doi:10.1109/TMC.2004.22.

    Article  Google Scholar 

  8. Chen, W., Mei, T., Liu, Y., Meng, M.H., & Liang, H. (2008). Dynamic event data aggregation in wireless sensor networks. In IEEE International Conference on Mechatronics and Automation, 2008. ICMA 2008 (pp. 43–48). doi:10.1109/ICMA.2008.4798723.

  9. Crossbow: http://www.xbow.com.

  10. Fan, K.W., Liu, S., & Sinha, P. (2006). Scalable data aggregation for dynamic events in sensor networks. In SenSys ’06: Proceedings of the 4th international conference on Embedded networked sensor systems (pp. 181–194). ACM, New York, NY, USA. doi:10.1145/1182807.1182826.

  11. Gatziu, S., & Dittrich, K. (1994). Detecting composite events in active database systems using petri nets. In Proceedings Fourth International Workshop on Research Issues in Data Engineering, Active Database Systems. doi:10.1109/RIDE.1994.282859.

  12. Gay, D., Levis, P., von Behren, J.R., Welsh, M., Brewer, E.A., & Culler, D.E. (2003). The nesc language: A holistic approach to networked embedded systems. In PLDI.

  13. Han, C.C., Kumar, R., Shea, R., Kohler, E., & Srivastava, M. (2005). A dynamic operating system for sensor nodes. In MobiSys ’05: Proceedings of the 3rd international conference on Mobile systems, applications, and services (pp. 163–176). ACM. doi:10.1145/1067170.1067188.

  14. He, T., Vicaire, P., Yan, T., Luo, L., Gu, L., Zhou, G., Stoleru, R., Cao, Q., Stankovic, J.A., & Abdelzaher, T.F. (2006). Achieving real-time target tracking using wireless sensor networks. In IEEE Real Time Technology and Applications Symposium (pp. 37–48).

  15. Honeywell: http://www.ssec.honeywell.com.

  16. Hubbell, N., & Han, Q. (2012). Dragon: Detection and tracking of dynamic amorphous events in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 23(7), 1193–1204.

    Article  Google Scholar 

  17. Kaveti, L., Pulluri, S., & Singh, G. (2009). Event ordering in pervasive sensor networks. In IEEE International Conference on Pervasive Computing and Communications, 2009. PerCom. doi:10.1109/PERCOM.2009.4912861.

  18. Levis, P., Lee, N., Welsh, M., & Culler, D. (2003). Tossim: Accurate and scalable simulation of entire tinyos applications. In Proceedings of the 1st international conference on Embedded networked sensor systems SenSys (pp. 126–137). ACM. doi:10.1145/958491.958506.

  19. Levis, P., Madden, S., Gay, D., Polastre, J., Szewczyk, R., Woo, A., Brewer, E.A., & Culler, D.E. (2004). The emergence of networking abstractions and techniques in tinyos. In NSDI.

  20. Li, S., Lin, Y., Son, S. H., Stankovic, J. A., & Wei, Y. (2004). Event detection services using data service middleware in distributed sensor networks. Telecommunication Systems, 26(2–4), 351–368. doi:10.1023/B:TELS.0000029046.79337.8f.

    Article  Google Scholar 

  21. Lin, C. Y., Peng, W. C., & Tseng, Y. C. (2006). Efficient in-network moving object tracking in wireless sensor networks. IEEE Transactions on Mobile Computing, 5(8), 1044–1056. doi:10.1109/TMC.2006.115.

    Article  Google Scholar 

  22. Lundquist, D., & Ouksel, A.M. (2007). An efficient demand-driven and density-controlled publish/subscribe protocol for mobile environments. In H.A. Jacobsen, G. Mühl, M.A. Jaeger (eds.) DEBS, ACM International Conference Proceeding Series volume 233, (pp. 26–37). ACM.

  23. Madden, S. R., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2005). Tinydb: An acquisitional query processing system for sensor networks. ACM Transactions on database systems, 30(1), 122–173.

    Article  Google Scholar 

  24. Marta, M., Yang, Y., & Cardei, M. (2009). Energy-efficient composite event detection in wireless sensor networks. In WASA (pp. 94–103).

  25. Nakamura, E.F., Ramos, H.S., Villas, L.A., de Oliveira, H.A., de Aquino, A.L., & Loureiro, A.A. (2008). A reactive role assignment for data routing in event-based wireless sensor networks. Computer Networks, 53(12), 1980–1996. doi:10.1016/j.comnet.2009.03.009. http://www.sciencedirect.com/science/article/B6VRG-4VY2CG6-1/2/e935e74f689bc644efea8138c7ca8d05

  26. de Oca, M. M. M., Gomez, J., & López-Guerrero, M. (2014). DISAGREE: Disagreement-based querying in wireless sensor networks. Telecommunication Systems, 56(3), 399–416. doi:10.1007/s11235-013-9852-5.

    Article  Google Scholar 

  27. Öllös, G., & Vida, R. (2014). Event signature extraction and traffic modeling in wsns. Telecommunication Systems, 55(4), 513–523. doi:10.1007/s11235-013-9806-y.

    Article  Google Scholar 

  28. Pietzuch, P.R., Shand, B., & Bacon, J. (2003). A framework for event composition in distributed systems. In Middleware.

  29. Pietzuch, P. R., Shand, B., & Bacon, J. (2004). Composite event detection as a generic middleware extension. IEEE Network, 18(1), 44–55.

    Article  Google Scholar 

  30. Sharp, C., Schaffert, S., Woo, A., Sastry, N., Karlof, C., Sastry, S., & Culler, D. (2005). Design and implementation of a sensor network system for vehicle tracking and autonomous interception. Proceeedings of the Second European Workshop on Wireless Sensor Networks.

  31. Yao, Y., & Gehrke, J. (2002). The cougar approach to in-network query processing in sensor networks. ACM SIGMOD Record, 31(3), 9–18.

    Article  Google Scholar 

  32. Zhai, Y., Tian, Y.L., & Hampapur, A. (2008). Composite spatiotemporal event detection in multi-camera surveillance networks. In Workshop on Multi-Camera and Multi-Model Sensor Fusion Algorithms and Applications, ECCV.

  33. Zhang, D., Chen, M., Guizani, M., Xiong, H., & Zhang, D. (2014a). Mobility prediction in telecom cloud using mobile calls. IEEE Wireless Communications, 21(1), 26–32. doi:10.1109/MWC.2014.6757894.

  34. Zhang, Y., Chen, M., Mao, S., Hu, L., & Leung, V. C. (2014b). Cap: Community activity prediction based on big data analysis. IEEE Network, 28(4), 52–57.

  35. Zhang, D., Yang, Z., Raychoudhury, V., Chen, Z., & Lloret, J. (2013). An energy-efficient routing protocol using movement trends in vehicular ad hoc networks. The Computer Journal, 56(8), 938–946.

    Article  Google Scholar 

  36. Zhang, D., Zhang, D., Xiong, H., Hsu, C. H., & Vasilakos, A. (2014). Basa: Building mobile ad-hoc social networks on top of android. IEEE Network, 28(1), 4–9. doi:10.1109/MNET.2014.6724100.

    Article  Google Scholar 

Download references

Acknowledgments

This research is partially supported by the Major Project of Chinese National Programs for Fundamental Research and Development (No. 2015CB352400), the National Natural Science Foundation of China (No. 61202416), Shenzhen Overseas High-Caliber Personnel Innovation Funds (No. KQCX20140521115045446), and Shenzhen Strategic Emerging Industry Development Funds (No. JCYJ20120615130218295).

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Authors and Affiliations

  1. Department of Computer Science, School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Hejun Wu

  2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Jiannong Cao

  3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Xiaopeng Fan

Authors
  1. Hejun Wu
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  2. Jiannong Cao
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  3. Xiaopeng Fan
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Corresponding author

Correspondence to Xiaopeng Fan.

Appendix

Appendix

Table 5 New clauses for event detection
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Wu, H., Cao, J. & Fan, X. Dynamic collaborative in-network event detection in wireless sensor networks. Telecommun Syst 62, 43–58 (2016). https://doi.org/10.1007/s11235-015-9981-0

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