Skip to main content
SpringerLink
Log in

Mobile sensor patrol path planning in partially observable border regions

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

A border surveillance operation requires sophisticated sensor planning, as sensors are usually scarce and cannot cover an entire region simultaneously. Border patrol agents act as moving sensors in a border region, and the border patrol agents’ coverage moves around the region dynamically, increasing the chance of approaching a trespassing agent. Typically, the locations of trespassing agents cannot be fully observed due to the size of the border region and obstacles. In addition, intelligent trespassing agents may dynamically adjust their traveling paths so that the border patrol agents cannot predict their locations easily. Trespassing agents are assumed to leave traces that indicate their footprints, providing their estimated locations. Border patrol agents may use the trespassers’ footprints as partial information to leverage patrol path planning. We propose an adaptive border patrol process as a partially observable Markov decision process (POMDP), in which an individual border patrol agent’s decision is determined dynamically on the basis of trespassing agents’ partially observed locations. The observations are shared among individual border patrol agents, allowing the border patrol agents to cooperate. The zoning technique is used to limit the planning scope of an individual border patrol agent, and Monte Carlo simulation is applied to reduce the complexity of the POMDP planning problem. Empirical experiments are conducted by means of simulated agents. The simulation parameters are derived from the interviews with a group of border patrol experts. The results in different scenarios show that the proposed patrol path planning scheme outperforms other patrol path planning schemes in terms of the trespasser detection rate. The simulation results are validated with respect to subject matter experts (SMEs), where SMEs are the same border patrol experts who had given the interviews. The proposed method has potential in border surveillance as an assisting system for human border patrol or an automated guidance system in robots or drones.

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

Similar content being viewed by others

References

  1. Rossmo D K, Thurman Q C, Jamieson J D, Egan K (2008) Geographic patterns and profiling of illegal crossings of the southern US border. Secur J 21(1-2):29–57

    Article  Google Scholar 

  2. Tsianos V, Karakayali S (2010) Transnational migration and the emergence of the European border regime: an ethnographic analysis. Eur J Soc Theory 13(3):373–387

    Article  Google Scholar 

  3. European Parliament and Council of the European Union (2013) Regulation (EU) No 1052/2013 of the European Parliament and of the Council of 22 October 2013 establishing the European Border Surveillance System (Eurosur)

  4. U.S. Customs and Border Protection (2018) Border Surveillance System (BSS). Retrieved 8 December 2019, from https://www.dhs.gov/sites/default/files/publications/privacy-pia-cbp022-bss-september2018.pdfhttps://www.dhs.gov/sites/default/files/publications/privacy-pia-cbp022-bss-september2018.pdfhttps://www.dhs.gov/sites/default/files/publications/privacy-pia-cbp022-bss-september2018.pdf

  5. Jones R, Johnson C (2016) Border militarisation and the re-articulation of sovereignty. Trans Inst Br Geogr 41(2):187–200

    Article  Google Scholar 

  6. Shumov V V (2019) The model of the rationale for the focus of border security efforts at the state level. Comput Res Model 11(1):187–196

    Article  Google Scholar 

  7. Liu B, Dousse O, Nain P, Towsley D (2013) Dynamic coverage of mobile sensor networks. IEEE Trans Parallel Distrib Syst 24(2):301–311

    Article  Google Scholar 

  8. Clouqueur T, Phipatanasuphorn V, Ramanathan P, Saluja K K (2003) Sensor deployment strategy for detection of targets traversing a region. Mob Netw Appl 8(4):453–461

    Article  Google Scholar 

  9. Elmaliach Y, Agmon N, Kaminka G A (2009) Multi-robot area patrol under frequency constraints. Ann Math Artif Intell 57(3-4):293–320

    Article  MathSciNet  Google Scholar 

  10. Xing G, Wang J, Yuan Z, Tan R, Sun L, Huang Q, et al. (2010) Mobile scheduling for spatiotemporal detection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 21(12):1851–1866

    Article  Google Scholar 

  11. Agmon N, Urieli D, Stone P (2011) Multiagent patrol generalized to complex environmental conditions. In: Twenty-Fifth AAAI conference on artificial intelligence (AAAI)

  12. Komar C, Donmez M Y, Ersoy C (2012) Detection quality of border surveillance wireless sensor networks in the existence of trespassers’ favorite paths. Comput Commun 35(10):1185–1199

    Article  Google Scholar 

  13. Park C H, Kim Y D, Jeong B (2012) Heuristics for determining a patrol path of an unmanned combat vehicle. Comput Ind Eng 63(1):150–160

    Article  Google Scholar 

  14. Kartal B, Godoy J, Karamouzas I, Guy S J (2015) Stochastic tree search with useful cycles for patrolling problems. In: Proceedings of the 2015 IEEE international conference on robotics and automation (ICRA), pp 1289–1294

  15. Sigurdson D, Bulitko V, Yeoh W, Hernández C, Koenig S (2018) Multi-Agent Pathfinding with Real-Time heuristic search. In: Proceedings of the 2018 IEEE conference on computational intelligence and games (CIG), pp 1–8

  16. Pawgasame W, Wipusitwarakun K (2020) Mobile sensors’ patrol path planning in unobservable border region. International Journal of Intelligent Computing and Cybernetics

  17. Kaelbling L, Littman M, Cassandra A (1998) Planning and acting in partially observable stochastic domains. Artif Intell 101(1-2):99–134

    Article  MathSciNet  Google Scholar 

  18. Silver D, Veness J (2010) Monte-carlo planning in large POMDPs. Proc Adv Neural Inf Process Syst 23:2164–2172

    Google Scholar 

  19. Pastore T H, Everett HR, Bonner K (1999) Mobile robots for outdoor security applications. Space And Naval Warfare Systems Center, San Diego Ca

  20. Keung G, Li B, Zhang Q (2012) The intrusion detection in mobile sensor network. IEEE/ACM Trans Netw 20(4):1152–1161

    Article  Google Scholar 

  21. Ȧström K (1965) Optimal control of Markov processes with incomplete state information. J Math Anal Appl 10(1):174–205

    Article  MathSciNet  Google Scholar 

  22. Smallwood R D, Sondik E J (1973) The optimal control of partially observable Markov processes over a finite horizon. Oper Res 21(5):1071–1088

    Article  Google Scholar 

  23. Russell S J, Norvig P (2016) Artificial intelligence: a modern approach. Pearson Education Limited, Harlow

  24. Bellman R E (1957) Dynamic programming. Princeton University Press, Princeton

  25. Pineau J, Gordon G, Thrun S (2003) Point-based value iteration: An anytime algorithm for POMDPs. Proc Eighteenth Int Joint Conf Artif Intell 3:1025–1032

    Google Scholar 

  26. Ross S, Pineau J, Paquet S, Chaib-Draa B (2008) Online planning algorithms for POMDPs. J Artif Intell Res 32:663–704

    Article  MathSciNet  Google Scholar 

  27. Shani G, Pineau J, Kaplow R (2013) A survey of point-based POMDP solvers. Auton Agent Multi-Agent Syst 27(1):1–51

    Article  Google Scholar 

  28. Spaan M T, Vlassis N (2004) A point-based POMDP algorithm for robot planning. Proceedings of the 2004 IEEE International Conference on Robotics and Automation (ICRA), pp 2399–2404

  29. Paquet S, Chaib-draa B, Ross S (2006) Hybrid POMDP algorithms. In: Proceedings of the workshop on multi-agent sequential decision making in uncertain domains, pp 133–147

  30. Kurniawati H, Hsu D, Lee W S (2008) Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces. Robotics: Science and systems 2008

  31. Spaan M T, Gonçalves N, Sequeira J (2010) Multirobot coordination by auctioning POMDPs. In: Proceedings of the 2010 IEEE international conference on robotics and automation (ICRA). Anchorage, pp 1446–1451

  32. Bai H, Cai S, Ye N, Hsu D, Lee W S (2015) Intention-aware online POMDP planning for autonomous driving in a crowd. In: Proceedings of the 2015 IEEE international conference on robotics and automation (ICRA). Seattle, pp 454–460

  33. Luo Y, Bai H, Hsu D, Lee W S (2019) Importance sampling for online planning under uncertainty. Int J Robot Res 38(2-3):162–181

    Article  Google Scholar 

  34. Nguyen H V, Chesser M, Koh L P, Rezatofighi S H, Ranasinghe D C (2019) Trackerbots: Autonomous unmanned aerial vehicle for real-time localization and tracking of multiple radio-tagged animals. J Field Robot 36(3):617–635

    Article  Google Scholar 

  35. Kocsis L, Szepesvári C (2006) Bandit based monte-carlo planning. In: Proceedings of the European conference on machine learning, Berlin, pp 282–293

  36. Onur E, Ersoy C, Deliç H, Akarun L (2007) Surveillance wireless sensor networks: Deployment quality analysis. IEEE Netw 21(6):48–53

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sirindhorn International Institute of Technology, School of Information, Computer and Communication Technology, Pathum Thani, Thailand

    Wichai Pawgasame & Komwut Wipusitwarakun

Authors
  1. Wichai Pawgasame
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Komwut Wipusitwarakun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wichai Pawgasame.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

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

Pawgasame, W., Wipusitwarakun, K. Mobile sensor patrol path planning in partially observable border regions. Appl Intell 51, 5453–5473 (2021). https://doi.org/10.1007/s10489-020-02068-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-02068-6

Keywords

Search

Navigation