Skip to main content
SpringerLink
Log in

Detection and Tracking of Mobile Intruder in Harsh Geographical Terrains Using Surveillance Wireless Sensor Networks

  • Chapter
  • First Online:
Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1132))

  • 912 Accesses

Abstract

The physical threat is a critical issue for the security of the sensitive regions. The sensitive regions are very much prone to unauthorized access. Likewise, the border area of a country is the sensitive region because it is the place where most of the intrusion has been taking place. Most of the border areas comprise harsh geographical terrains. The human-being cannot surveil such terrains. Therefore, the characteristics of a sensor node such as miniaturization, low-cost, ease of deployment, self-configuration, and stealthiness in harsh environmental conditions make them eligible to surveil harsh border terrains. A set of sensor nodes with sensing, coordination and communication capability, form a wireless sensor network (WSN). The prime challenge for deployed WSN is to detect and track the intruder with maximum detection probability and minimum false alarm-rate. The reliable and in-time transmission of detection and tracking results to the base-station is another issue for intruder detection and tracking application. Along with this, random deployment, coverage, network lifetime, energy conservation and network partitioning are other challenges that disrupt the performance of WSN. This chapter presents various intruder detection and tracking attributes and protocols for mobile intruder detection and tracking application. This chapter also presents the various challenges that emerge for WSNs in intruder detection and tracking application.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Sharma, A., Chauhan, S.: Target coverage computation protocols in wireless sensor networks: a comprehensive review. Int. J. Comput. Appl., 1–23 (2019). https://doi.org/10.1080/1206212x.2019.1663382

  2. Sun, Z., Wang, P., Vuran, M.C., Al-Rodhaan, M.A., Al-Dhelaan, A.M., Akyildiz, I.F.: BorderSense: border patrol through advanced wireless sensor networks. Ad Hoc Netw. 9, 468–477 (2011). https://doi.org/10.1016/j.adhoc.2010.09.008

    Article  Google Scholar 

  3. Komar, C., Donmez, M.Y., Ersoy, C.: Detection quality of border surveillance wireless sensor networks in the existence of trespassers’ favorite paths. Comput. Commun. 35, 1185–1199 (2012). https://doi.org/10.1016/j.comcom.2012.03.002

    Article  Google Scholar 

  4. Wang, B.: Coverage problems in sensor networks: a survey. ACM Comput. Surv. (CSUR) 43, 32 (2011). https://doi.org/10.1145/1978802.1978811

    Article  Google Scholar 

  5. Li, Y., Jha, D.K., Ray, A., Wettergren, T.A.: Information fusion of passive sensors for detection of moving targets in dynamic environments. IEEE Trans. Cybern. 47, 93–104 (2016). https://doi.org/10.1109/TCYB.2015.2508024

    Article  Google Scholar 

  6. Yang, X., Zhang, W.A., Yu, L., Xing, K.: Multi-rate distributed fusion estimation for sensor network-based target tracking. IEEE Sens. J. 16, 1233–1242 (2015). https://doi.org/10.1109/JSEN.2015.2497464

    Article  Google Scholar 

  7. Bhuiyan, M.Z.A., Wang, G., Vasilakos, A.V.: Local area prediction-based mobile target tracking in wireless sensor networks. IEEE Trans. Comput. 64, 1968–1982 (2014). https://doi.org/10.1109/TC.2014.2346209

    Article  MathSciNet  MATH  Google Scholar 

  8. Wang, X., Fu, M., Zhang, H.: Target tracking in wireless sensor networks based on the combination of KF and MLE using distance measurements. IEEE Trans. Mob. Comput. 11, 567–576 (2011). https://doi.org/10.1109/TMC.2011.59

    Article  Google Scholar 

  9. Liu, C., Fang, D., Yang, Z., Jiang, H., Chen, X., Wang, W., Xing, T., Cai, L.: RSS distribution-based passive localization and its application in sensor networks. IEEE Trans. Wirel. Commun. 15, 2883–2895 (2015). https://doi.org/10.1109/TWC.2015.2512861

    Article  Google Scholar 

  10. Jain, S., Pattanaik, K.K., Shukla, A.: QWRP: query-driven virtual wheel based routing protocol for wireless sensor networks with mobile sink. J. Netw. Comput. Appl. 147, 102430 (2019). https://doi.org/10.1016/j.jnca.2019.102430

    Article  Google Scholar 

  11. He, J., Yu, Y., Wang, Q.: RSS assisted TOA-based indoor geolocation. Int. J. Wirel. Inf. Netw. 20, 157–165. https://doi.org/10.1007/s10776-012-0198-9

    Article  Google Scholar 

  12. Lee, J., Cho, K., Lee, S., Kwon, T., Choi, Y.: Distributed and energy-efficient target localization and tracking in wireless sensor networks. Comput. Commun. 29, 2494–2505 (2006). https://doi.org/10.1016/j.comcom.2006.02.004

    Article  Google Scholar 

  13. Tomic, S., Beko, M., Dinis, R.: RSS-based localization in wireless sensor networks using convex relaxation: noncooperative and cooperative schemes. IEEE Trans. Veh. Technol. 64, 2037–2050 (2014). https://doi.org/10.1109/TVT.2014.2334397

    Article  Google Scholar 

  14. Souza, É.L., Nakamura, E.F., Pazzi, R.W.: Target tracking for sensor networks: a survey. ACM Comput. Surv. (CSUR) 49, 30 (2016). https://doi.org/10.1145/2938639

    Article  Google Scholar 

  15. Ribeiro, A., Schizas, I.D., Roumeliotis, S.I., Giannakis, G.: Kalman filtering in wireless sensor networks. IEEE Control Syst. Mag. 30, 66–86 (2010). https://doi.org/10.1109/MCS.2009.935569

    Article  MathSciNet  Google Scholar 

  16. Abdollahzadeh, S., Navimipour, N.J.: Deployment strategies in the wireless sensor network: a comprehensive review. Comput. Commun. 91, 1–16 (2016). https://doi.org/10.1016/j.comcom.2016.06.003

    Article  Google Scholar 

  17. Sharma, V., Patel, R.B., Bhadauria, H.S., Prasad, D.: Deployment schemes in wireless sensor network to achieve blanket coverage in large-scale open area: a review. Egypt. Inform. J. 17, 45–56 (2016). https://doi.org/10.1016/j.eij.2015.08.003

    Article  Google Scholar 

  18. Altınel, İ.K., Aras, N., Güney, E., Ersoy, C.: Binary integer programming formulation and heuristics for differentiated coverage in heterogeneous sensor networks. Comput. Netw. 52, 2419–2431 (2008). https://doi.org/10.1016/j.comnet.2008.05.002

    Article  MATH  Google Scholar 

  19. Onur, E., Ersoy, C., Deliç, H., Akarun, L.: Surveillance wireless sensor networks: deployment quality analysis. IEEE Netw. 21, 48–53 (2007). https://doi.org/10.1109/MNET.2007.4395110

    Article  Google Scholar 

  20. Hefeeda, M., Ahmadi, H.: Energy-efficient protocol for deterministic and probabilistic coverage in sensor networks. IEEE Trans. Parallel Distrib. Syst. 21, 579–593 (2009). https://doi.org/10.1109/TPDS.2009.112

    Article  Google Scholar 

  21. Kashi, S.S., Sharifi, M.: Coverage rate calculation in wireless sensor networks. Computing 94, 833–856 (2012). https://doi.org/10.1007/s00607-012-0192-1

    Article  MathSciNet  Google Scholar 

  22. Sharma, A., Chauhan, S.: Optimal threshold coverage area (OTCA) algorithm for random deployment of sensor nodes in large asymmetrical terrain. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol. 906. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-1813-9_4

    Google Scholar 

  23. Zhang, H., Hou, J.C.: Maintaining sensing coverage and connectivity in large sensor networks. Ad Hoc Sens. Wirel. Netw. 1, 89–124 (2005)

    Google Scholar 

  24. Akram, V.K., Dagdeviren, O.: DECK: a distributed, asynchronous and exact k-connectivity detection algorithm for wireless sensor networks. Comput. Commun. 116, 9–20 (2018). https://doi.org/10.1016/j.comcom.2017.11.005

    Article  Google Scholar 

  25. Biswas, S., Das, R., Chatterjee, P.: Energy-efficient connected target coverage in multi-hop wireless sensor networks. In: Bhattacharyya, S., Sen, S., Dutta, M., Biswas, P., Chattopadhyay, H. (eds.) Industry Interactive Innovations in Science, Engineering and Technology. Lecture Notes in Networks and Systems, vol. 11. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-3953-9_40

    Google Scholar 

  26. Wang, H., Roman, H.E., Yuan, L., Huang, Y., Wang, R.: Connectivity, coverage and power consumption in large-scale wireless sensor networks. Comput. Netw. 75, 212–225 (2014). https://doi.org/10.1016/j.comnet.2014.10.008

    Article  Google Scholar 

  27. Abo-Zahhad, M., Amin, O., Farrag, M., Ali, A.: Survey on energy consumption models in wireless sensor networks. Open Trans. Wirel. Sens. Netw. 1, 63–79 (2014)

    Google Scholar 

  28. Pantazis, N.A., Nikolidakis, S.A., Vergados, D.D.: Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15, 551–591 (2012). https://doi.org/10.1109/SURV.2012.062612.00084

    Article  Google Scholar 

  29. Yadav, S., Yadav, R.S.: A review on energy efficient protocols in wireless sensor networks. Wirel. Netw. 22, 335–350 (2016). https://doi.org/10.1007/s11276-015-1025-x

    Article  Google Scholar 

  30. Liu, K.S., Gao, J., Lin, S., Huang, H., Schiller, B.: Joint sensor duty cycle scheduling with coverage guarantee. In: Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 11–20. ACM (2016). https://doi.org/10.1145/2942358.2942379

  31. Assad, N., Elbhiri, B., Faqihi, M.A., Ouadou, M., Aboutajdine, D.: Efficient deployment quality analysis for intrusion detection in wireless sensor networks. Wirel. Netw. 22, 991–1006 (2016). https://doi.org/10.1007/s11276-015-1015-z

    Article  Google Scholar 

  32. Wang, W., Srinivasan, V., Chua, K.C., Wang, B.: Energy-efficient coverage for target detection in wireless sensor networks. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 313–322. ACM (2007). https://doi.org/10.1145/1236360.1236401

  33. D’Costa, A., Sayeed, A.M.: Data versus decision fusion in wireless sensor networks. In: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 4, pp. IV-832–835. IEEE (2003). https://doi.org/10.1109/icassp.2003.1202772

  34. Varshney, P.K.: Distributed Detection and Data Fusion. Springer, Heidelberg (2012)

    Google Scholar 

  35. Duarte, M., Hu, Y.H.: Distance-based decision fusion in a distributed wireless sensor network. Telecommun. Syst. 26, 339–350 (2004). https://doi.org/10.1023/B:TELS.0000029045.03170.e9

    Article  Google Scholar 

  36. Katenka, N., Levina, E., Michailidis, G.: Local vote decision fusion for target detection in wireless sensor networks. IEEE Trans. Signal Process. 56, 329–338 (2007). https://doi.org/10.1109/TSP.2007.900165

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhu, M., Ding, S., Wu, Q., Brooks, R.R., Rao, N.S., Iyengar, S.S.: Fusion of threshold rules for target detection in wireless sensor networks. ACM Trans. Sens. Netw. (TOSN) 6, 18 (2010). https://doi.org/10.1145/1689239.1689248

    Article  Google Scholar 

  38. Zhao, H., Chen, L., Feng, W.: A signal detection scheme for wireless sensor networks based on convex optimization. In: 2016 IEEE SENSORS, pp. 1–3. IEEE (2016). https://doi.org/10.1109/icsens.2016.7808713

  39. Kurt, S., Tavli, B.: Path-loss modeling for wireless sensor networks: a review of models and comparative evaluations. IEEE Antennas Propag. Mag. 59, 18–37 (2017). https://doi.org/10.1109/MAP.2016.2630035

    Article  Google Scholar 

  40. Niculescu, D., Nath, B.: Ad hoc positioning system (APS) using AOA. In: IEEE INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1734–1743. IEEE (2003). https://doi.org/10.1109/infcom.2003.1209196

  41. Hassani, A., Bertrand, A., Moonen, M.: Distributed node-specific direction-of-arrival estimation in wireless acoustic sensor networks. In: 21st European Signal Processing Conference, pp. 1–5. IEEE (2013)

    Google Scholar 

  42. Girod, L., Estrin, D.: Robust range estimation using acoustic and multimodal sensing. In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the Next Millennium, pp. 1312–1320. IEEE (2001). https://doi.org/10.1109/iros.2001.977164

  43. Liu, G., Liu, H., Chen, H., Zhou, C., Shu, L.: Position-based adaptive quantization for target location estimation in wireless sensor networks using one-bit data. Wirel. Commun. Mob. Comput. 16, 929–941 (2016). https://doi.org/10.1002/wcm.2576

    Article  Google Scholar 

  44. Niu, R., Varshney, P.K.: Distributed detection and fusion in a large wireless sensor network of random size. EURASIP J. Wirel. Commun. Netw. 2005, 462–472 (2005). https://doi.org/10.1155/WCN.2005.462

    Article  MATH  Google Scholar 

  45. Deng, F., Guan, S., Yue, X., Gu, X., Chen, J., Lv, J., Li, J.: Energy-based sound source localization with low power consumption in wireless sensor networks. IEEE Trans. Ind. Electron. 64, 4894–4902 (2017). https://doi.org/10.1109/TIE.2017.2652394

    Article  Google Scholar 

  46. Li, D., Hu, Y.H.: Energy-based collaborative source localization using acoustic microsensor array. EURASIP J. Adv. Signal Process. 2003, 985029 (2003). https://doi.org/10.1155/S1110865703212075

    Article  MATH  Google Scholar 

  47. Ciuonzo, D., Rossi, P.S.: Distributed detection of a non-cooperative target via generalized locally-optimum approaches. Inf. Fusion 36, 261–274 (2017). https://doi.org/10.1016/j.inffus.2016.12.006

    Article  Google Scholar 

  48. Guerriero, M., Svensson, L., Willett, P.: Bayesian data fusion for distributed target detection in sensor networks. IEEE Trans. Signal Process. 58, 3417–3421 (2010). https://doi.org/10.1109/TSP.2010.2046042

    Article  MathSciNet  MATH  Google Scholar 

  49. Köse, M., Taşcioğlu, S., Telatar, Z.: Signal-to-noise ratio estimation of noisy transient signals. Commun. Fac. Sci. Univ. Ankara Ser. A2-A3 57, 11–19 (2015). https://doi.org/10.1501/commua1-2_0000000084

    Article  Google Scholar 

  50. Qin, F., Dai, X., Mitchell, J.E.: Effective-SNR estimation for wireless sensor network using Kalman filter. Ad Hoc Netw. 11, 944–958 (2013). https://doi.org/10.1016/j.adhoc.2012.11.002

    Article  Google Scholar 

  51. Zhang, W., Cao, G.: DCTC: dynamic convoy tree-based collaboration for target tracking in sensor networks. IEEE Trans. Wirel. Commun. 3, 1689–1701 (2004). https://doi.org/10.1109/TWC.2004.833443

    Article  Google Scholar 

  52. Tsai, H.W., Chu, C.P., Chen, T.S.: Mobile object tracking in wireless sensor networks. Comput. Commun. 30(8), 1811–1825 (2007). https://doi.org/10.1016/j.comcom.2007.02.018

    Article  Google Scholar 

  53. Cao, Q., Yan, T., Stankovic, J., Abdelzaher, T.: Analysis of target detection performance for wireless sensor networks. In: International Conference on Distributed Computing in Sensor Systems, pp. 276–292. Springer, Heidelberg (2005). https://doi.org/10.1007/11502593_22

    Chapter  Google Scholar 

  54. Wang, Y., Wang, X., Xie, B., Wang, D., Agrawal, D.P.: Intrusion detection in homogeneous and heterogeneous wireless sensor networks. IEEE Trans. Mob. Comput. 7, 698–711 (2008). https://doi.org/10.1109/TMC.2008.19

    Article  Google Scholar 

  55. Donmez, M.Y., Kosar, R., Ersoy, C.: An analytical approach to the deployment quality of surveillance wireless sensor networks considering the effect of jammers and coverage holes. Comput. Netw. 54, 3449–3466 (2010). https://doi.org/10.1016/j.comnet.2010.07.007

    Article  MATH  Google Scholar 

  56. Rumyantsev, K., Zikiy, A., Zlaman, P.: Detection of signals by the frequency-time contrast method. In: Singh, P., Paprzycki, M., Bhargava, B., Chhabra, J., Kaushal, N., Kumar, Y. (eds.) Futuristic Trends in Network and Communication Technologies. FTNCT 2018. Communications in Computer and Information Science, vol. 958. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3804-5_7

    Google Scholar 

  57. Boulanouar, I., Lohier, S., Rachedi, A., Roussel, G.: DTA: deployment and tracking algorithm in wireless multimedia sensor networks. Ad Hoc Sens. Wirel. Netw. 28, 115–135 (2015)

    Google Scholar 

  58. Al-Jarrah, M.A., Al-Dweik, A., Kalil, M., Ikki, S.S.: Decision fusion in distributed cooperative wireless sensor networks. IEEE Trans. Veh. Technol. 68, 797–811 (2018). https://doi.org/10.1109/TVT.2018.2879413

    Article  Google Scholar 

  59. Alaybeyoglu, A., Dagdeviren, O., Kantarci, A., Erciyes, K.: A distributed wakening based target tracking protocol for wireless sensor networks. In: 2010 Ninth International Symposium on Parallel and Distributed Computing, pp. 165–172. IEEE (2010). https://doi.org/10.1109/ispdc.2010.33

  60. Wang, G., Bhuiyan, M.Z.A., Cao, J., Wu, J.: Detecting movements of a target using face tracking in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 25, 939–949 (2013). https://doi.org/10.1109/TPDS.2013.91

    Article  Google Scholar 

  61. Xue, L., Liu, Z., Guan, X.: Prediction-based protocol for mobile target tracking in wireless sensor networks. J. Syst. Eng. Electron. 22, 347–352 (2011). https://doi.org/10.3969/j.issn.1004-4132.2011.02.024

    Article  Google Scholar 

  62. Hsu, J.M., Chen, C.C., Li, C.C.: POOT: an efficient object tracking strategy based on short-term optimistic predictions for face-structured sensor networks. Comput. Math. Appl. 63, 391–406 (2012). https://doi.org/10.1016/j.camwa.2011.07.034

    Article  MATH  Google Scholar 

  63. Jiang, B., Ravindran, B., Cho, H.: Probability-based prediction and sleep scheduling for energy-efficient target tracking in sensor networks. IEEE Trans. Mob. Comput. 12, 735–747 (2012). https://doi.org/10.1109/TMC.2012.44

    Article  Google Scholar 

  64. Souza, É.L., Pazzi, R.W., Nakamura, E.F.: A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks. Wirel. Netw. 21, 2263–2278 (2015). https://doi.org/10.1007/s11276-015-0914-3

    Article  Google Scholar 

  65. Ahmadi, H., Viani, F., Bouallegue, R.: An accurate prediction method for moving target localization and tracking in wireless sensor networks. Ad Hoc Netw. 70, 14–22 (2018). https://doi.org/10.1016/j.adhoc.2017.11.008

    Article  Google Scholar 

  66. Misra, S., Singh, S.: Localized policy-based target tracking using wireless sensor networks. ACM Trans. Sens. Netw. (TOSN) 8, 27 (2012). https://doi.org/10.1145/2240092.2240101

    Article  Google Scholar 

  67. Mahfouz, S., Mourad-Chehade, F., Honeine, P., Farah, J., Snoussi, H.: Target tracking using machine learning and Kalman filter in wireless sensor networks. IEEE Sens. J. 14, 3715–3725 (2014). https://doi.org/10.1109/JSEN.2014.2332098

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, National Institute of Technology Hamirpur, Hamirpur, 177005, Himachal Pradesh, India

    Anamika Sharma & Siddhartha Chauhan

Authors
  1. Anamika Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Siddhartha Chauhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anamika Sharma .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

  2. Department of Computer Sciences, Purdue University, West Lafayette, IN, USA

    Bharat K. Bhargava

  3. Polish Academy of Sciences, Systems Research Institute, Warszawa, Poland

    Marcin Paprzycki

  4. Department of Computer Science and Engineering, National Institute of Technology, Delhi, India

    Narottam Chand Kaushal

  5. School of Education Intelligent Technology, Jiangsu Normal University, Xuzhou, Jiangsu, China

    Wei-Chiang Hong

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sharma, A., Chauhan, S. (2020). Detection and Tracking of Mobile Intruder in Harsh Geographical Terrains Using Surveillance Wireless Sensor Networks. In: Singh, P., Bhargava, B., Paprzycki, M., Kaushal, N., Hong, WC. (eds) Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's. Advances in Intelligent Systems and Computing, vol 1132. Springer, Cham. https://doi.org/10.1007/978-3-030-40305-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-40305-8_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-40304-1

  • Online ISBN: 978-3-030-40305-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation