Telecommunication Systems

, Volume 68, Issue 1, pp 115–127 | Cite as

Effective sensing radius (ESR) and performance analysis of static and mobile sensor networks

  • Sunandita Debnath
  • Ashraf Hossain
  • Sultan Mahmood Chowdhury
  • Abhishek Kumar Singh


In wireless sensor networks (WSNs), deterministic sensing model has been widely studied and explored for its coverage analysis. Boolean sensing model falls under the deterministic category, which considers a fixed sensing radius, although it is not a realistic assumption. In the literature other probabilistic sensing models like Elfes sensing model and shadow fading sensing model are also considered. However, the linking between the Boolean sensing model and probabilistic sensing models has not yet been analyzed. Nowadays, mobile sensor networks (MSNs) are becoming a hot research topic for their diverse area of applications. It comes with many mathematical and analytical complexities for coverage performance analysis due to continuous topological changes. In this paper, we derive the expression of effective sensing radius (ESR) for probabilistic sensing models to make the link between these sensing models. We also extend our study to MSNs for studying performance analysis such as network coverage fraction and intruder detection time by utilizing ESR of probabilistic sensing models. Our proposed ESR is suitable for analyzing and planning of WSNs.


Effective sensing radius Intruder detection time Mobile sensor networks Network coverage Sensing models 



The authors would like to thank the editor and the anonymous reviewers for their valuable comments which helped to improve the quality of the paper.

Compliance with ethical standards

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  2. 2.
    Bisnik, N., Abouzeid, A., & Isler, V. (2006). Stochastic event capture using mobile sensors subject to a quality metric. IEEE Transactions on Robotics, 23(4), 676–692.CrossRefGoogle Scholar
  3. 3.
    Luo, J., & Hubaux, J.-P. (2005). Joint mobility and routing for lifetime elongation in wireless sensor networks. In Proceedings of 24th Annual Joint Conference of the IEEE Computer and Communication Societies (INFCOM 2005) (pp. 1735–1746). Miami, FL, 13–17 March.Google Scholar
  4. 4.
    Keung, G. Y., Li, B., & Zhang, Q. (2012). The intrusion detection in mobile sensor network. IEEE/ACM Transaction on Networking, 20(4), 1152–1161.CrossRefGoogle Scholar
  5. 5.
    Wang, W., Srinivasan, V., & Chua, K.-C. (2008). Extending the lifetime of wireless sensor networks through mobile relays. IEEE/ACM Transactions on Networking, 16(5), 1108–1120.CrossRefGoogle Scholar
  6. 6.
    Wang, Y., Zhang, Y., Liu, J., & Bhandari, R. (2015). Coverage, connectivity, and deployment in wireless sensor networks. In Chapter 2, signals and communication technology (1st ed., pp. 25–44). Springer. doi: 10.1007/978-81-322-2129-6_2.
  7. 7.
    Tsai, Y.-R. (2008). Sensing coverage for randomly distributed wireless sensor networks in shadowed environments. IEEE Transactions on Vehicular Technology, 57(1), 556–564.CrossRefGoogle Scholar
  8. 8.
    Clouqueur, T., Phipatanasuphorn, V., Ramanathan, P., & Saluja, K. K. (2002). Sensor deployment strategy for target detection. In Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks Application—WSNA ’02 (pp. 42—48) 28 Sep 2002.Google Scholar
  9. 9.
    Sun, W., & Su, X. (2011). Wireless sensor network node localization based on genetic algorithm. In Proceedings of IEEE 3rd International Conference on Communication Software and Networks (pp. 316–319). Xi’an, China, 27–29 May 2011.Google Scholar
  10. 10.
    Duan, H., Zhou, Y., & Liu, M. (2017). Fault tolerant scheduling algorithm in distributed sensor networks. Journal of Information Hiding and Mutlimedia Signal Processing, 8(1), 127–37.Google Scholar
  11. 11.
    Ye, F., Zhong, G., Lu, S., & Zhang, L. (2003). PEAS: A robust energy conserving protocol for long-lived sensor networks. In Proceedings of 23rd International Conference on Distributed Computing Systems 2003 (pp. 28–37). Rhode Island, USA, 19–22 May 2003.Google Scholar
  12. 12.
    Wang, G., & Yuan, Z. (2008). Sensor deployment strategy for collaborative target detection with guaranteed accuracy. In Proceedings of 4th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2008 (pp. 68–71). Wuhan, China, 10–12 Dec 2008.Google Scholar
  13. 13.
    Kong, L., Pan, J.-S., Tsai, P.-W., Vaclav, S., & Ho, J.-H. (2015). A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network. International Journal of Distributed Sensor Networks, 11, 729680:1–729680:10.CrossRefGoogle Scholar
  14. 14.
    Nguyen, T.-T., Pan, J.-S., Chu, S.-C., Roddick, J. F., & Dao, T.-K. (2016). Optimization localization in wireless sensor network based on multi-objective firefly algorithm. Journal of Network Intelligence, 1(4), 130–138.Google Scholar
  15. 15.
    Poduri, S., & Sukhatme, G. S. (2004). Constrained coverage in mobile sensor networks. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA’04) (pp. 165–171). New Orleans, LA, 26 April–1 May 2004.Google Scholar
  16. 16.
    Rezazadeh, J., Moradi, M., & Ismail, A. S. (2012). Mobile wireless sensor networks overview. International Journal of Computer Communications and Networks (IJCCN), 2(1), 17–22.Google Scholar
  17. 17.
    Natalizio, E., & Loscrí, V. (2011). Controlled mobility in mobile sensor networks: Advantages, issues and challenges. Telecommunication Systems, 52(4), 2411–2418. doi: 10.1007/s11235-011-9561-x.CrossRefGoogle Scholar
  18. 18.
    Liu, B., & Towsley, D. (2004). A study of the coverage of large-scale sensor networks. In Proceedings of 1st IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS’04) (pp. 475–483). Fort Lauderdale, Florida, USA, 25–27 Oct 2004.Google Scholar
  19. 19.
    Liu, M., Cao, J., Lou, W., Chen, L., & Li, X. (2005). Coverage analysis for wireless sensor networks. In X. Jia, J. Wu, Y. He (Eds.), Mobile ad-hoc and sensor networks. MSN 2005. Lecture notes in computer sciences (LNCS) (Vol. 3794, pp. 711–720). Berlin: Springer.Google Scholar
  20. 20.
    Singh, A., & Sharma, T. P. (2014). A survey on area coverage in wireless sensor networks. In Proceedings of International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 829–836). Kanyakumari, India, 10–11 July 2014.Google Scholar
  21. 21.
    Hossain, A., Biswas, P. K., & Chakrabarti, S. (2008). sensing models and its impact on network coverage in wireless sensor network. In Proceedings of 3rd International Conference on Industrial and Information Systems (ICIIS-2008). Indian Institute of Technology, Kharagpur, India, 8–10 Dec 2008.Google Scholar
  22. 22.
    Hossain, A., Chakrabarti, S., & Biswas, P. K. (2012). Impact of sensing model on wireless sensor network coverage. IET Wireless Sensor Systems, 2(3), 272–281.CrossRefGoogle Scholar
  23. 23.
    Wang, X., Yoo, Y., Wang, Y., & Agrawal, D. P. (2006). Impact of node density and sensing range on intrusion detection in wireless sensor networks. In Proceedings of 15th International Conference on Computer Communications and Networks (ICCCN) (pp. 323–327). Arlington, Virginia USA, 9–11 Oct 2006.Google Scholar
  24. 24.
    Kumar, S., & Lobiyal, D. K. (2013). Sensing coverage prediction for wireless sensor networks in shadowed and multipath environment. The Scientific World Journal, 11. doi: 10.1155/2013/565419.
  25. 25.
    Bai, X., Li, S., & Xu, J. (2010). Mobile sensor deployment optimization for k-coverage in wireless sensor networks with a limited mobility model. IETE Technical Review, 27, 124–137.CrossRefGoogle Scholar
  26. 26.
    Radha, S., & Shanmugavel, S. (2007). Mobility models in mobile ad hoc network. IETE Journal of Research, 53(1), 3–12.CrossRefGoogle Scholar
  27. 27.
    Liu, B., Dousse, O., Nain, P., & Towsley, D. (2013). Dynamic coverage of mobile sensor networks. IEEE Transactions on Parallel and Distributed Systems, 24(2), 301–311.CrossRefGoogle Scholar
  28. 28.
    Yang, T., Kulla, E., Barolli, L., Mino, G., & Takizawa, M. (2013). Performance analysis of wireless sensor networks for different speeds of sink and sensor nodes. In Proceedings of 7th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2013) (pp. 53–58). Taichung, Taiwan, 3–5 July 2013.Google Scholar
  29. 29.
    Gautam, N., Sofat, S., & Vig, R. (2015). Data collection model for energy-efficient wireless sensor networks. Annals of Telecommunication, 70(11), 501–511. doi: 10.1007/s12243-015-0471-x.CrossRefGoogle Scholar
  30. 30.
    Cherkaoui, E. H., Toni, L., Rossi, L., Fontaine, J. G., & Agoulmine, N. (2011). On the effects of node mobility on energy efficiency and delay sensitive applications in underwater sensor network. In Proceedings of IEEE OCEANS’ 2011. Santander, Spain, 6–9 June 2011.Google Scholar
  31. 31.
    Liu, T., Li, Z., Xia X., & Luo, S. (2009). Shadowing effects and edge effect on sensing coverage for wireless sensor networks. In Proceedings of 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCom’2009). Beijing, China, 24–26 Sep 2009.Google Scholar
  32. 32.
    Debnath, S., & Hossain, A. (2016). Impact of boundary effect on coverage fraction in wireless sensor network. In Proceedings of International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT 2016) (pp. 2133–2137). Chennai, India, 3–5 March 2016Google Scholar
  33. 33.
    Elfes, A. (1991). Occupancy grids: A stochastic spatial representation for active robot perception. In S. S. Iyenger & A. Elfes (Eds.), Autonomous mobile robots: Perception, mapping and navigation (Vol. 1, pp. 60–70). Washington D.C.: IEEE Computer Society Press.Google Scholar
  34. 34.
    Lambrou, T. P., & Panayiotou, C. G. (2013). Collaborative path planning for event search and exploration in mixed sensor networks. The International Journal of Robotics Research, 32(12), 1424–1437.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Sunandita Debnath
    • 1
  • Ashraf Hossain
    • 1
  • Sultan Mahmood Chowdhury
    • 1
  • Abhishek Kumar Singh
    • 1
  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology (NIT) SilcharSilcharIndia

Personalised recommendations