[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A Distributed Border Surveillance (DBS) System for Rectangular and Circular Region of Interest with Wireless Sensor Networks in Shadowed Environments

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Border surveillance is indeed one of the most pertinent applications of wireless sensor networks, primarily used for security purposes such as intrusion detection in border regions or protected areas. In order to detect unauthorized access or penetration through the region of interest, sensor nodes are deployed to form barriers, that acts as the performance metric of wireless sensor networks. In this paper, a Distributed Border Surveillance (DBS) system incorporating shadowing effects is proposed for a wireless sensor network deployed in a rectangular region of interest. The DBS system evaluates the number of required barriers to monitor the given region and conserves energy. Besides, a log-normal shadowing model is considered, which incorporates the asymmetry in sensing range along with the stochastic nature of wireless channels. The performance of the proposed DBS system is analyzed based on the number of barriers obtained. Then, the impact of various network and system parameters such as the number of nodes, sensing range of nodes, height and width of the network region on the number of barriers obtained in a rectangular region are analyzed. The same approach is extended for a circular region of interest in terms of sensing range of nodes. The proposed system is implemented in NS-2.35 simulator, and it is found that the performance of the proposed DBS system is 75% better than the existing binary sensing range model-based DBS system.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

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

Similar content being viewed by others

References

  1. Mostafaei, H., Chowdhury, M. U, & & Obaidat, M. S., (2018). Border surveillance with WSN systems in a distributed manner. IEEE Systems Journal, 12(4), 3703–3712.

    Article  Google Scholar 

  2. Singh, A., Sharma, S., & Singh, J., & Kumar, R., (2019). Mathematical modelling for reducing the sensing of redundant information in WSNs based on biologically inspired techniques. Journal of Intelligent & Fuzzy Systems, 37(5), 6829–6839.

    Article  Google Scholar 

  3. Ayaz, M., Ammad-uddin, M., & Baig, I. (2017). Wireless sensor’s civil applications, prototypes, and future integration possibilities: A review. IEEE Sensors Journal, 18(1), 4–30.

    Article  Google Scholar 

  4. Pachauri, G., & Sharma, S. (2015). Anomaly detection in medical wireless sensor networks using machine learning algorithms. Procedia Computer Science, 70, 325–333.

    Article  Google Scholar 

  5. Nagar, J., & Sharma, S. (2018). k-Barrier coverage-based intrusion detection for wireless sensor networks (pp. 373–385). Singapore: In Cyber Security Springer.

    Google Scholar 

  6. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  7. Nagar, J., Chaturvedi, S. K., & Soh, S. (2020). An analytical model to estimate the performance metrics of a finite multihop network deployed in a rectangular region. Journal of Network and Computer Applications, 149, 102466.

    Article  Google Scholar 

  8. Farsi, M., Elhosseini, M. A., Badawy, M., Ali, H. A., & Eldin, H. Z. (2019). Deployment techniques in wireless sensor networks, coverage and connectivity: A survey. IEEE Access, 7, 28940–28954.

    Article  Google Scholar 

  9. Ding, X. X., Wang, T. T., Chu, H., Liu, X., & Feng, Y. H. (2019). An enhanced cluster head selection of LEACH based on power consumption and density of sensor nodes in wireless sensor networks. Wireless Personal Communications, 109, 1–11.

    Article  Google Scholar 

  10. Zhu, C., Zheng, C., Shu, L., & Han, G. (2012). A survey on coverage and connectivity issues in wireless sensor networks. Journal of Network and Computer Applications, 35(2), 619–632.

    Article  Google Scholar 

  11. Tsai, Y. R. (2008). Sensing coverage for randomly distributed wireless sensor networks in shadowed environments. IEEE Transactions on Vehicular Technology, 57(1), 556–564.

    Article  MathSciNet  Google Scholar 

  12. Alemayehu, T. S., & Kim, J. H. (2017). Efficient nearest neighbor heuristic TSP algorithms for reducing data acquisition latency of UAV relay WSN. Wireless Personal Communications, 95(3), 3271–3285.

    Article  Google Scholar 

  13. Laouira, M. L., Abdelli, A., Othman, J. B., & Kim, H. (2019). An efficient WSN based solution for border surveillance. IEEE Transactions on Sustainable Computing. https://doi.org/10.1109/TSUSC.2019.2904855.

  14. Boudriga, N. (2016). A WSN-based system for country border surveillance and target tracking. Advances in Remote Sensing, 5, 51–72.

    Article  Google Scholar 

  15. Amutha, J., Sharma, S., & Nagar, J. (2019). WSN strategies based on sensors, deployment, sensing models, coverage and energy efficiency: Review, approaches and open issues. Wireless Personal Communications, 111, 1–27.

    Google Scholar 

  16. Kumar, S., Lai, T. H., & Arora, A. (2005). Barrier coverage with wireless sensors. In Proceedings of the 11th annual international conference on Mobile computing and networking (pp. 284–298).

  17. Mostafaei, H., Shojafar, M., Zaher, B., & Singhal, M. (2017). Barrier coverage of WSNs with the imperialist competitive algorithm. The Journal of Supercomputing, 73(11), 4957–4980.

    Article  Google Scholar 

  18. Kumar, S., Lai, T. H., & Balogh, J. (2004). On k-coverage in a mostly sleeping sensor network. In Proceedings of the 10th annual international conference on Mobile computing and networking (pp. 144–158).

  19. Wang, B. (2011). Coverage problems in sensor networks: A survey. ACM Computing Surveys (CSUR), 43(4), 32.

    Article  Google Scholar 

  20. Rout, M., & Roy, R. (2016). Self-deployment of randomly scattered mobile sensors to achieve barrier coverage. IEEE Sensors Journal, 16(18), 6819–6820.

    Article  Google Scholar 

  21. Chen, A., Kumar, S., & Lai, T. H. (2009). Local barrier coverage in wireless sensor networks. IEEE Transactions on Mobile Computing, 9(4), 491–504.

    Article  Google Scholar 

  22. He, S., Chen, J., Li, X., Shen, X. S., & Sun, Y. (2013). Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Transactions on Mobile Computing, 13(6), 1268–1282.

    Google Scholar 

  23. Xu, B., Zhu, Y., Kim, D., Li, D., Jiang, H., & Tokuta, A. O. (2016). Strengthening barrier-coverage of static sensor network with mobile sensor nodes. Wireless Networks, 22(1), 1–10.

    Article  Google Scholar 

  24. Fan, G., & Jin, S. (2010). Coverage problem in wireless sensor network: A survey. Journal of Networks, 5(9), 1033.

    Article  Google Scholar 

  25. Cheng, C. F., & Wang, C. W. (2017). The barrier-breach problem of barrier coverage in wireless sensor networks. IEEE Communications Letters, 21(10), 2262–2265.

    Article  Google Scholar 

  26. Farzinvash, L., Najjar-Ghabel, S., & Javadzadeh, T. (2019). A distributed and energy-efficient approach for collecting emergency data in wireless sensor networks with mobile sinks. AEU-International Journal of Electronics and Communications., 108, 79–86.

    Article  Google Scholar 

  27. Li, X. Y., Wan, P. J., & Frieder, O. (2003). Coverage in wireless ad hoc sensor networks. IEEE Transactions on Computers, 52(6), 753–763.

    Article  Google Scholar 

  28. Chen, J., & Li, J., & Lai, T. H., (2013). Trapping mobile targets in wireless sensor networks: An energy-efficient perspective. IEEE Transactions on Vehicular Technology, 62(7), 3287–3300.

    Article  Google Scholar 

  29. Hussain, C. S., Park, M. S., Bashir, A. K., Shah, S. C., & Lee, J. (2013). A collaborative scheme for boundary detection and tracking of continuous objects in WSNs. Intelligent Automation & Soft Computing, 19(3), 439–456.

    Article  Google Scholar 

  30. Silvestri, S., & Goss, K. (2017). MobiBar: An autonomous deployment algorithm for barrier coverage with mobile sensors. Ad Hoc Networks, 54, 111–129.

    Article  Google Scholar 

  31. Li, L., Zhang, B., Shen, X., Zheng, J., & Yao, Z. (2011). A study on the weak barrier coverage problem in wireless sensor networks. Computer Networks, 55(3), 711–721.

    Article  Google Scholar 

  32. Commuri, S., & Watfa, M. K. (2006). Coverage strategies in wireless sensor networks. International Journal of Distributed Sensor Networks, 2(4), 333–353.

    Article  Google Scholar 

  33. Cardei, M., MacCallum, D., Cheng, M. X., Min, M., Jia, X., Li, D., et al. (2002). Wireless sensor networks with energy efficient organization. Journal of Interconnection Networks, 3(4), 213–229.

    Article  Google Scholar 

  34. Wang, Z., Chen, H., Cao, Q., Qi, H., Wang, Z., & Wang, Q. (2017). Achieving location error tolerant barrier coverage for wireless sensor networks. Computer Networks, 112, 314–328.

    Article  Google Scholar 

  35. Mostafaei, H., & Meybodi, M. R. (2014). An energy efficient barrier coverage algorithm for wireless sensor networks. Wireless Personal Communications, 77(3), 2099–2115.

    Article  Google Scholar 

  36. Hanh, N. T., Binh, H. T. T., Hoai, N. X., & Palaniswami, M. S. (2019). An efficient genetic algorithm for maximizing area coverage in wireless sensor networks. Information Sciences, 488, 58–75.

    Article  MathSciNet  Google Scholar 

  37. Zhang, H., & Hou, J. C. (2005). Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc & Sensor Wireless Networks, 1(1–2), 89–124.

    Google Scholar 

  38. Abbas, T., Sjöberg, K., Karedal, J., & Tufvesson, F. (2015). A measurement based shadow fading model for vehicle-to-vehicle network simulations. International Journal of Antennas and Propagation, 2015, 190607. https://doi.org/10.1155/2015/190607.

  39. Hossain, A., Biswas, P. K., & Chakrabarti, S. (2008). Sensing models and its impact on network coverage in wireless sensor network. In 2008 IEEE region 10 and the third international conference on industrial and information systems (pp. 1–5).

  40. Entezari, A., & Tadaion, A. (2019). Coverage and rate analysis in cellular networks with Nakagami–Lognormal fading channel employing soft frequency reuse. Physical Communication, 36, 100757.

    Article  Google Scholar 

  41. Dong, Z., Shang, C., Chang, C. Y., & Roy, D. S. (2020). Barrier coverage mechanism using adaptive sensing range for renewable WSNs. IEEE Access, 8, 86065–86080.

    Article  Google Scholar 

  42. Si, P., Ma, J., Tao, F., Fu, Z., & Shu, L. (2020). Energy-efficient barrier coverage with probabilistic sensors in wireless sensor networks. IEEE Sensors Journal, 20(10), 5624–5633.

    Article  Google Scholar 

  43. Njoya, A. N., Ari, A. A. A., Awa, M. N., Titouna, C., Labraoui, N., Effa, J. Y., et al. (2020). Hybrid wireless sensors deployment scheme with connectivity and coverage maintaining in wireless sensor networks. Wireless Personal Communications, 112, 1893–1917.

Download references

Author information

Authors and Affiliations

  1. School of Information and Communication Technology, Gautam Buddha University (GBU), Greater Noida, Uttar Pradesh, 201312, India

    J. Amutha & Sandeep Sharma

  2. Subir Chowdhury School of Quality and Reliability, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, West Bengal, 721302, India

    Jaiprakash Nagar

Authors
  1. J. Amutha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaiprakash Nagar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandeep Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandeep Sharma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amutha, J., Nagar, J. & Sharma, S. A Distributed Border Surveillance (DBS) System for Rectangular and Circular Region of Interest with Wireless Sensor Networks in Shadowed Environments. Wireless Pers Commun 117, 2135–2155 (2021). https://doi.org/10.1007/s11277-020-07963-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07963-2

Keywords

Search

Navigation