Skip to main content

Advertisement

SpringerLink
Log in

Optimizing event detection in low duty-cycled sensor networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Duty cycling is a fundamental approach to conserving energy in sensor networks; however, it brings challenges to event detection in the sense that an event may be undetected or undergo a certain delay before it is detected, in particular when sensors are low duty-cycled. We investigate the fundamental relationship between event detection and energy efficiency. We quantify event detection performance by deriving the closed forms of detection delay and detectability with a relatively simple model. We also characterize the intrinsic tradeoff that exists between detection performance and system lifetime, which helps flexible design decisions for sensor networks. In addition, we propose a fully localized algorithm called CAS to cooperatively determine sensor wakeups. Without relying on location information, the distributed algorithm is easy to implement and scalable to network density and scale. Theoretical bounds of event detection are also studied to facilitate comparative study. Comprehensive experiments are conducted and results demonstrate that the proposed algorithm significantly improves event detection performance in terms of detection latency and detection probability. It reduces as high as 31% of detection delay and increases as much as 25% of detectability compared with the random independent scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Dutta, P., Grimmer, M., Arora, A., Bibyk, S., & Culler, D. (2005). Design of a wireless sensor network platform for detecting rare, random, and ephemeral events. In ACM/IEEE IPSN.

  2. Ngan, H., Zhu, Y., Ni, L. M., & Xiao, R. (2005). SAS: Stimulus-based adaptive sleeping for wireless sensor networks. In ICPP, Norway.

  3. Brennan, S., Mielke, A., & Torney, D. (2005). Radioactive source detection by sensor networks. IEEE Transactions on Nuclear Science, 52(3), 813–819.

    Article  Google Scholar 

  4. Hsin, C., & Liu, M. (2004). Network coverage using low duty-cycled sensors: Random & coordinated sleep algorithms. In ACM/IEEE IPSN.

  5. Kumar, S., Lai, T. H., & Balogh, J. (2004). On k-coverage in a mostly sleeping sensor network. Philadelphia, PA: ACM MobiCom.

    Google Scholar 

  6. Tian, D., & Georganas, N. D. (2003). A node scheduling scheme for energy conservation in large wireless sensor networks. Wireless Communication and Mobile Computing, 3, 271–290.

    Article  Google Scholar 

  7. Ye, F., Zhong, G., Cheng, J., Lu, S., & Zhang, L. (2003). PEAS: A robust energy conserving protocol for long-lived sensor networks. In IEEE ICDCS.

  8. Yan, T., He, T., & Stankovic, J. A. (2003). Differentiated surveillance for sensor networks. Los Angeles, California, USA: ACM SenSys.

    Google Scholar 

  9. Cao, Q., Abdelzaher, T., He, T., & Stankovic, J. (2005). Towards optimal sleep scheduling in sensor networks for rare-event detection. In ACM/IEEE IPSN.

  10. Ren, S., Li, Q., Wang, H., Chen, X., & Zhang, X. (2004). Probabilistic coverage for object tracking in sensor networks. Philadelphia, Pennsylvania: ACM MobiCom poster.

    Google Scholar 

  11. Gui, C., & Mohapatra, P. (2004). Power conservation and quality of surveillance in target tracking sensor networks (pp. 129–143). ACM MobiCom: Philadelphia, PA, USA.

    Google Scholar 

  12. He, T., Krishnamurthy, S., Stankovic, J., Abdelzaher, T., Luo, L., Yan, T., et al. (2004). Energy-efficient surveillance systems using wireless sensor networks. In ACM Mobisys.

  13. He, T., Vicaire, P., Yan, T., Cao, Q., Zhou, G., Gu, L., et al. (2006). Achieving long-term surveillance in VigilNet. In INFOCOM.

  14. Polastre, J., Hill, J., & Culler, D. (2004). Versatile low power media access for wireless sensor networks. In ACM SenSys.

  15. Ye, W., Heidemann, J., & Estrinf, D. (2002). An energy-efficient mac protocol for wireless sensor networks. New York, NY, USA: In IEEE INFOCOM.

    Google Scholar 

  16. Dam, T. V., & Langendoen, K. (2003). An adaptive energy-efficient mac protocol for wireless sensor networks. Los Angeles, CA, USA: ACM SenSys.

    Google Scholar 

  17. Xu, Y., Heidemann, J., & Estrin, D. (2001). Geography-informed energy conservation for Ad Hoc routing (pp. 70–84). Rome, Italy: MobiCom.

    Google Scholar 

  18. Hui, J., Ren, Z., & Krogh, B. (2003). Sentry-based power management in wireless sensor networks. In ACM/IEEE IPSN.

  19. Chen, B., Jamieson, K., Balakrishnan, H., & Morris, R. (2001). Span: An energy-efficient coordination algorithm for topology maintenance in Ad Hoc wireless networks (pp. 85–96). Rome, Italy: ACM MobiCom.

    Google Scholar 

  20. Tseng, Y.-C., Hsu, C.-S., & Hsieh, T.-Y. (2002). Power-saving protocols for IEEE 802.11-based multi-hop Ad Hoc networks. In INFOCOM.

  21. Keshavarzian, A., Lee, H., Venkatraman, L., Lal, D., Chintalapudi, K., & Srinivasan, B. (2006). Wakeup scheduling in wireless sensor networks. In ACM MobiHoc.

  22. Armstrong, T. (2005). Wake-up based power management in multi-hop wireless networks. Technical Report. University of Toronto, Toronto.

  23. Zheng, R., Hou, J., & Sha, L. (2003). Asynchronous wakeup for Ad Hoc networks. Annapolis, Maryland: ACM MobiHoc.

    Google Scholar 

  24. Shih, E., Cho, S. H., Ickes, N., Min, R., Sinha, A., Wang, A., et al. (2001). Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks. In ACM MobiCom.

  25. Shnayder, V., Hempstead, M., Chen, B.-R., Allen, G. W., & Welsh, M. (2004). Simulating the power consumption of large-scale sensor network applications. ACM SenSys.

  26. Elson, J., Girod, L., & Estrin, D. (2002). Fine-grained network time synchronization using reference broadcasts. Boston, MA: USENIX OSDI.

    Google Scholar 

  27. Crossbow (2006). Power management and batteries. http://www.xbow.com/support/appnote.htm.

  28. Savvides, A., Han, C., & Srivastava, M. (2001). Dynamic fine grained localization in Ad-Hoc sensor networks. Rome, Italy: ACM MobiCom.

    Google Scholar 

  29. Patwari, N., Hero, III, A. O., Perkins, M., Correal, N. S., & O’Dea, R. J. (2003). Relative location estimation in wireless sensor networks. IEEE Transactions on Signal Processing, 51(8), 2137–2148.

  30. Hua, C., & Yum, T. S. P. (2007). Asynchronous random sleeping for sensor networks. ACM Transactions on Sensor Networks (TOSN), 3(3), 15-es.

    Article  Google Scholar 

  31. Wang, X., Xing, G., Zhang, Y., Lu, C., Pless, R., & Gill, C. (2003). Integrated coverage and connectivity configuration in wireless sensor networks. Los Angeles, CA: ACM SenSys.

    Google Scholar 

  32. Zhu, Y., Ni, L. M., & Zhang, Z. (2007). Low-power distributed event detection in sensor networks. In IEEE INFOCOM mini-conference.

  33. Xue, W., Luo, Q., Chen, L., & Liu, Y. (2006). Contour map matching for event detection in sensor networks. In ACM SIGMOD.

  34. Ding, M., Chen, D., Xing, K., & Cheng, X. (2005). Localized fault-tolerant event boundary detection in sensor networks. In IEEE INFOCOM.

  35. Li, M., Liu, Y., & Chen, L. (2008). Nonthreshold-based event detection for 3D environment monitoring in sensor networks. Knowledge and Data Engineering, IEEE Transactions on, 20(12), 1699–1711.

    Article  Google Scholar 

  36. Xing, G., Lu, C., Pless, R., & O’Sullivan, J. A. (2004). Co-grid: An efficient coverage maintenance protocol for distributed sensor networks. In ACM/IEEE IPSN.

  37. Luo, X., Dong, M., & Huang, Y. (2006). On distributed fault-tolerant detection in wireless sensor networks. IEEE Transactions on Computers, 55(1), 58–70.

    Google Scholar 

Download references

Acknowledgment

This research is supported by NSFC (No. 61170238, 60903190, 61027009, 60970106, 60673166 and 60803124), Shanghai Pu Jiang Talents Program (10PJ1405800), Shanghai Chen Guang Program (10CG11), 973 Program (2005CB321901), MIIT of China (2009ZX03006-001-01 and 2009ZX03006-004), Doctoral Fund of Ministry of Education of China (20100073120021), 863 Program (2009AA012201 and 2011AA010500). In addition, it is partially supported by the Open Fund of the State Key Laboratory of Software Development Environment (Grant No. SKLSDE-2010KF-04), Beijing University of Aeronautics and Astronautics.

Author information

Authors and Affiliations

  1. Shanghai Key Lab of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yanmin Zhu

  2. The Third Research Institute of the Ministry of Public Security of China, Shanghai, China

    Yunhuai Liu

  3. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Lionel M. Ni

Authors
  1. Yanmin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunhuai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lionel M. Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanmin Zhu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhu, Y., Liu, Y. & Ni, L.M. Optimizing event detection in low duty-cycled sensor networks. Wireless Netw 18, 241–255 (2012). https://doi.org/10.1007/s11276-011-0397-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-011-0397-9

Keywords

Search

Navigation