Data Suppression Algorithms for Surveillance Applications of Wireless Sensor and Actor Networks

  • Bartłomiej PłaczekEmail author
  • Marcin Bernas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 522)


This paper introduces algorithms for surveillance applications of wireless sensor and actor networks (WSANs) that reduce communication cost by suppressing unnecessary data transfers. The objective of the considered WSAN system is to capture and eliminate distributed targets in the shortest possible time. Computational experiments were performed to evaluate effectiveness of the proposed algorithms. The experimental results show that a considerable reduction of the communication costs together with a performance improvement of the WSAN system can be obtained by using the communication algorithms that are based on spatiotemporal and decision aware suppression methods.


Wireless sensor and actor networks Data suppression Target tracking Surveillance applications 


  1. 1.
    Kamali, M., Laibinis, L., Petre, L., Sere, K.: Formal development of wireless sensor-actor networks. Sci. Comput. Program. 80, 25–49 (2014)Google Scholar
  2. 2.
    Akyildiz, I.F., Kasimoglu, I.H.: Wireless sensor and actor networks: research challenges. Ad Hoc Netw. 2(4), 351–367 (2004)Google Scholar
  3. 3.
    Khamis, A., ElGindy, A.: Minefield mapping using cooperative multirobot systems. J. Robot. 2012, 1–7 (2012). article ID 698046Google Scholar
  4. 4.
    Vedantham, R., Zhuang, Z., Sivakumar, R.: Mutual exclusion in wireless sensor and actor networks. In: 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks SECON 2006, vol. 1, pp. 346–355 (2006)Google Scholar
  5. 5.
    Kumar, M.S., Rajasekaran, S.: Detection and extinguishing forest fires using wireless sensor and actor networks. Int. J. Comput. Appl. 24(1), 31–35 (2011)Google Scholar
  6. 6.
    Płaczek, B.: Selective data collection in vehicular networks for traffic control applications. Transp. Res. Part C Emer. Technol. 23, 14–28 (2012)Google Scholar
  7. 7.
    Płaczek, B.: Uncertainty-dependent data collection in vehicular sensor networks. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2012. CCIS, vol. 291, pp. 430–439. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  8. 8.
    Bernaś, M.: WSN power conservation using mobile sink for road traffic monitoring. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2013. CCIS, vol. 370, pp. 476–484. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Porwik, P.: The spectral test of the boolean function linearity. Int. J. Appl. Math. Comput. Sci. 13(4), 567–576 (2003)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Alippi, C., Anastasi, G., Di Francesco, M., Roveri, M.: An adaptive sampling algorithm for effective energy management in wireless sensor networks with energy-hungry sensors. IEEE Trans. Instrum. Meas. 59(2), 335–344 (2010)Google Scholar
  11. 11.
    Zhang, Y., Lum, K., Yang, J.: Failure-aware cascaded suppression in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 25(5), 1042–1055 (2013)Google Scholar
  12. 12.
    Zhou, X., Xue, G., Qian, C., Li, M.: Efficient data suppression for wireless sensor networks. In: 14th IEEE International Conference on Parallel and Distributed Systems ICPADS 2008, pp. 599–606 (2008)Google Scholar
  13. 13.
    Evans, W.C., Bahr, A., Martinoli, A.: Distributed spatiotemporal suppression for environmental data collection in real-world sensor networks. In: IEEE International Conference on Distributed Computing in Sensor Systems DCOSS, pp. 70–79 (2013)Google Scholar
  14. 14.
    Silberstein, A., Gelfand, A., Munagala, K., Puggioni, G., Yang, J.: Making sense of suppressions and failures in sensor data: a bayesian approach. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 842–853 (2007)Google Scholar
  15. 15.
    Puggioni, G., Gelfand, A.E.: Analyzing space-time sensor network data under suppression and failure in transmission. Stat. Comput. 20(4), 409–419 (2010)MathSciNetGoogle Scholar
  16. 16.
    Yigitel, M.A., Incel, O.D., Ersoy, C.: QoS-aware MAC protocols for wireless sensor networks: a survey. Comput. Netw. 55(8), 1982–2004 (2011)Google Scholar
  17. 17.
    Płaczek, B.: Communication-aware algorithms for target tracking in wireless sensor networks. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2014. CCIS, vol. 431, pp. 69–78. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  18. 18.
    Płaczek, B., Bernaś, M.: Optimizing data collection for object tracking in wireless sensor networks. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2013. CCIS, vol. 370, pp. 485–494. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations