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Shao, Y.; Wang, Q.; Lu, X.; Wang, Z.; Zhao, E.; Fang, S.; Chen, J.; Kong, L.; Ghafoor, K.Z. Automatic Barrier Coverage in Smart City. Encyclopedia. Available online: https://encyclopedia.pub/entry/51919 (accessed on 26 December 2024).
Shao Y, Wang Q, Lu X, Wang Z, Zhao E, Fang S, et al. Automatic Barrier Coverage in Smart City. Encyclopedia. Available at: https://encyclopedia.pub/entry/51919. Accessed December 26, 2024.
Shao, Ying, Qiwen Wang, Xingjian Lu, Zhanquan Wang, E Zhao, Shuang Fang, Jianxiong Chen, Linghe Kong, Kayhan Zrar Ghafoor. "Automatic Barrier Coverage in Smart City" Encyclopedia, https://encyclopedia.pub/entry/51919 (accessed December 26, 2024).
Shao, Y., Wang, Q., Lu, X., Wang, Z., Zhao, E., Fang, S., Chen, J., Kong, L., & Ghafoor, K.Z. (2023, November 22). Automatic Barrier Coverage in Smart City. In Encyclopedia. https://encyclopedia.pub/entry/51919
Shao, Ying, et al. "Automatic Barrier Coverage in Smart City." Encyclopedia. Web. 22 November, 2023.
Automatic Barrier Coverage in Smart City
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This entry is adapted from the peer-reviewed paper 10.3390/s23187787

Barrier coverage is a fundamental application in wireless sensor networks, which are widely used for smart cities. In applications, the sensors form a barrier for the intruders and protect an area through intrusion detection.

barrier coverage mobile sensor networks boundary detection

1. Introduction

Barrier coverage [1] is one vital application in wireless sensor networks (WSNs) for smart cities [2][3], which forms sensor nodes surrounding barriers to protect a region by detecting all intruders. A wide range of safety scenarios of smart city demand barrier coverage exist, for example, the country border surveillance for stowaway detection. Researchers extend the classic concept of barrier coverage to a new branch, which moves the sensor nodes surrounding a dangerous region and protects any unexpected visitors by warning them away from the dangers, so-called warning barrier coverage (WBC). The WBC is promising in many danger keep out application for smart cities. For example, a WBC can surround debris areas in floods and alarm rescue workers to avoid unnecessary harms. Moreover, WBC can warn people to avoid entering dangerous areas such as hazardous gas leaks and even nuclear radiations in cities.
Researchers compare the classic barrier coverage and WBC in Table 1. Different from the classic barrier coverage, WBC focuses on the danger keep out applications whose boundary of target region is previously unknown. To avoid danger, people cannot become close to the region or deploy sensor nodes manually. Hence, besides the visitor detection, sensor nodes in WBC should have the capability to detect the boundary as well. Based on the sensing results, mobile sensor nodes form the barrier collaboratively.
Table 1. Comparison between classic barrier coverage and warning barrier coverage.
Barrier Coverage Classic Warning
Target Region Known Unknown boundary
Sensing Capability Intruder detection Visitor detection and boundary detection
Moving Capability Mobile/static node Mobile node
Typical Application Border surveillance Danger, keep out!
Regardless of the classic barrier coverage used in WBC, the formation process [4] is crucial because newly deployed sensor nodes lack a dependable infrastructure for communication and detection. The formation process in WBC is defined to form sensor nodes into k barriers enclosing the region, thus detecting or warning unexpected visitors.
Researchers have put forward various solutions in the literature for classic barrier coverage formation for smart cities. For instance, Kumar [1] proposed a centralized algorithm to determine weak k-barrier coverage in a region using randomly deployed sensor networks. Later, ref. [5] devised efficient algorithms to construct strong sensor barriers. And ref. [6] studied the barrier coverage of line-based deployment. In addition, ref. [7] funded a cluster-based barrier construction algorithm in mobile wireless sensor networks.
Different from the known region in classic barrier coverage, the boundary of a dangerous region in WBC is usually unknown. Hence, the classic formation approaches are not appropriate for WBC in smart cities. In addition, most existing works [1][6] fall into the category that forms the barrier coverage by stationary sensor nodes. Nevertheless, if the stationary sensor nodes are stochastically deployed, many redundant nodes will be needed to ensure a strong barrier [5]. On the other hand, if the stationary nodes are manually deployed, a significant amount of manpower and time are consumed. Especially in some cases for smart cities, the region is rather large in scale or hides dangers. Therefore, using stationary nodes is more of a hindrance than a help in WBC. A few state-of-the-art works also considered using mobile sensor nodes [8][9][10] to facilitate the barrier coverage.

2. Automatic Barrier Coverage in Smart City

In the construction of smart cities, information and communication technologies are used to improve the living standards and management of citizens and governments [11][12]. The Internet of Things (IoT) using sensors is widely used in smart cities [13]. In particular, the coverage-related problem [14][15] is a fundamental topic in WSNs to measure the monitoring quality of a sensor network deployed in a given region. Barrier coverage guarantees the detection of any intruder attempting to cross the barrier of sensor networks or penetrating the protected region. There are numerous studies on classical intrusion detection and avoidance [16][17][18]. Ref. [19] presents methods for intrusion detection and tracking with pan–tilt cameras. And ref. [20] proposes a probabilistic sensor tasking algorithm in which cameras sense the environment independently of one another, thus reducing the communication overhead. In addition, diverse directions are excellently studied for coverage problems, such as barrier coverage [1], sweep coverage [21], surface coverage [22], and trap coverage [23].
In these directions, barrier coverage is one valuable and practical application for smart cities, which is advocated in [1] for the purpose of intrusion detection in country borders, critical infrastructure protection, and battlefield perimeter surveillance. The barrier coverage formed by stationary nodes has been widely studied. For instance, the minimum cost for achieving k-barrier coverage is calculated in [24]. In [5], strong sensor barriers were devised. Line-based and curve-based barrier coverage were studied by [6][25], respectively. Multi-round sensor deployment for guaranteed barrier coverage is proposed in [26]. Nevertheless, a significant amount of resources such as redundant nodes in stochastic deployment and manpower cost in manual deployment will be needed due to the reliance on stationary nodes only.
Mobile nodes for barrier coverage was firstly introduced in [4], in which the nodes with limited mobility (e.g., one-step move with one chance) are utilized to improve the quality of barrier coverage. With the rapid development of autonomous robot technology, sensor nodes with strong mobility [27] become practical. In addition, a movement barrier formation algorithm MobiBar designed in [28] presented distributed algorithms for barrier coverage using sensor relocation. Ref. [29] proposed a heuristic target-barrier construction algorithm to solve the target-barrier coverage problem while satisfying the boundary constraint conditions. These works mainly focus on centralized analysis, which is not suitable for large-scale barrier coverage for smart cities.
Distributed algorithms for mobile barrier coverage were also investigated in the literature. The chain reaction algorithm [30] was firstly developed for mobile barrier formation. But it totally ignores the situation of node failure, which may lead to certain loopholes in the barrier. Based on mobility and intruder prior information, PMS [31] is able to improve the quality of barrier coverage. However, PMS assumes that the region knowledge is pre-known, which is not practical in most real WBC applications for smart cities. Moreover, ref. [32] presented a distributed cellular automaton based algorithm for the autonomous deployment of mobile sensors. The limitation is that the number of sensors needs to be deployed in a fixed manner.

References

  1. Kumar, S.; Lai, T.H.; Arora, A. Barrier coverage with wireless sensors. In Proceedings of the 11th Annual International Conference on Mobile Computing and Networking, Cologne, Germany, 28 August–2 September 2005; pp. 284–298.
  2. Chen, C.P.; Mukhopadhyay, S.C.; Chuang, C.L.; Lin, T.S.; Liao, M.S.; Wang, Y.C.; Jiang, J.A. A hybrid memetic framework for coverage optimization in wireless sensor networks. IEEE Trans. Cybern. 2014, 45, 2309–2322.
  3. Yoon, Y.; Kim, Y.H. An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks. IEEE Trans. Cybern. 2014, 43, 1473–1483.
  4. Saipulla, A.; Liu, B.; Xing, G.; Fu, X.; Wang, J. Barrier coverage with sensors of limited mobility. In Proceedings of the Eleventh ACM International Symposium on Mobile ad Hoc Networking and Computing, Chicago, IL, USA, 20–24 September 2010; pp. 201–210.
  5. Tao, D.; Tang, S.J.; Zhang, H.T.; Mao, X.F.; Ma, H.D. Strong barrier coverage in directional sensor networks. Comput. Commun. 2012, 35, 895–905.
  6. Saipulla, A.; Westphal, C.; Liu, B.; Wang, J. Barrier coverage with line-based deployed mobile sensors. IEEE Ad Hoc Netw. 2013, 11, 1381–1391.
  7. Cheng, C.F.; Hsu, C.C.; Pan, M.S.; Srivastava, G.; Lin, J.C.W. A cluster-based barrier construction algorithm in mobile Wireless Sensor Networks. Phys. Commun. 2022, 54, 101839.
  8. La, H.M.; Sheng, W.; Chen, J. Cooperative and active sensing in mobile sensor networks for scalar field mapping. IEEE Trans. Syst. Man Cybern. Syst. 2014, 45, 1–12.
  9. Wang, J.; Pham, K. A Distributed Policy Gradient Algorithm for Optimal Coordination of Mobile Sensor Networks. In Proceedings of the 2022 IEEE Sensors, Dallas, TX, USA, 30 October–2 November 2022; pp. 1–4.
  10. Le, V.A.; Nguyen, L.; Nghiem, T.X. Multistep Predictions for Adaptive Sampling in Mobile Robotic Sensor Networks Using Proximal ADMM. IEEE Access 2022, 10, 64850–64861.
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  13. Singh, S.P.; Viriyasitavat, W.; Juneja, S.; Alshahrani, H.; Shaikh, A.; Dhiman, G.; Singh, A.; Kaur, A. Dual adaption based evolutionary algorithm for optimized the smart healthcare communication service of the Internet of Things in smart city. Phys. Commun. 2022, 55, 101893.
  14. Chu, Y.; Ahmadi, H.; Grace, D.; Burns, D. Deep Learning Assisted Fixed Wireless Access Network Coverage Planning. IEEE Access 2021, 9, 124530–124540.
  15. Sharma, J.; Bhutani, A. Review on Deployment, Coverage and Connectivity in Wireless Sensor Networks. In Proceedings of the 2022 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India, 17–19 June 2022; pp. 802–807.
  16. Bhadauria, S.; Mohanty, T. Hybrid Intrusion Detection System using an Unsupervised method for Anomaly-based Detection. In Proceedings of the 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), Hyderabad, India, 13–16 December 2021; pp. 1–6.
  17. Ouiazzane, S.; Addou, M.; Barramou, F. A Multi-Agent Model for Network Intrusion Detection. In Proceedings of the 2019 1st International Conference on Smart Systems and Data Science (ICSSD), Rabat, Morocco, 3–4 October 2019; pp. 1–5.
  18. Cai, X.; Ding, X. A comparative study of machine vision-based rail foreign object intrusion detection models. In Proceedings of the 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA), Shenyang, China, 19–31 January 2023; pp. 1304–1308.
  19. Biswas, A.; Guha, P.; Mukerjee, A.; Venkatesh, K.S. Intrusion detection and tracking with pan-tilt cameras. In Proceedings of the IET International Conference on Visual Information Engineering, Bangalore, India, 26–28 September 2006; pp. 565–571.
  20. Skraba, P.; Guibas, L. Energy efficient intrusion detection in camera sensor networks. In Proceedings of the Distributed Computing in Sensor Systems: Third IEEE International Conference, Santa Fe, NM, USA, 18–20 June 2007; pp. 309–323.
  21. Li, M.; Cheng, W.; Liu, K.; He, Y.; Li, X.; Liao, X. Sweep coverage with mobile sensors. IEEE Trans. Mob. Comput. (TMC) 2011, 10, 1534–1545.
  22. Kong, L.; Zhao, M.; Liu, X.Y.; Lu, J.; Liu, Y.; Wu, M.Y.; Shu, W. Surface coverage in sensor networks. IEEE Trans. Parallel Distrib. Syst. (TPDS) 2013, 25, 234–243.
  23. Chen, J.; Li, J.; He, S.; He, T.; Gu, Y.; Sun, Y. On energy-efficient trap coverage in wireless sensor networks. ACM Trans. Sens. Netw. (TOSN) 2013, 10, 1–29.
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  25. He, S.; Gong, X.; Zhang, J.; Chen, J.; Sun, Y. Curve-based deployment for barrier coverage in wireless sensor networks. IEEE Trans. Wirel. Commun. 2014, 13, 724–735.
  26. Pang, C.; Shan, G.; Duan, X.; Xu, G. A Multi-Mode Sensor Management Approach in the Missions of Target Detecting and Tracking. Electronics 2019, 8, 71.
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  28. Silvestri, S. Mobibar: Barrier coverage with mobile sensors. In Proceedings of the IEEE Global Telecommunications Conference (GLOBECOM), Houston, TX, USA, 5–9 December 2011; pp. 1–6.
  29. Eftekhari, M.; Kranakis, E.; Krizanc, D.; Morales-Ponce, O.; Narayanan, L.; Opatrny, J.; Shende, S. Distributed algorithms for barrier coverage using relocatable sensors. In Proceedings of the 2013 ACM Symposium on Principles of Distributed Computing, Montreal, QC, Canada, 22–24 July 2013; pp. 383–392.
  30. Kong, L.; Liu, X.; Li, Z.; Wu, M.Y. Automatic barrier coverage formation with mobile sensor networks. In Proceedings of the 2010 IEEE International Conference on Communications, Cape Town, South Africa, 23–27 May 2010; pp. 1–5.
  31. He, S.; Chen, J.; Li, X.; Shen, X.; Sun, Y. Mobility and intruder prior information improving the barrier coverage of sparse sensor networks. IEEE Trans. Mob. Comput. 2013, 13, 1268–1282.
  32. Lloyd, H.; Hammoudeh, M. A Distributed Cellular Automaton Algorithm for Barrier Formation in Mobile Sensor Networks. In Proceedings of the 2010 Wireless Days (WD), Venice, Italy, 20–22 October 2010; pp. 1–4.
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Subjects: Computer Science, Cybernetics
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Ying Shao
,
Qiwen Wang
,
Xingjian Lu
,
Zhanquan Wang
,
E Zhao
,
Shuang Fang
,
Jianxiong Chen
,
Linghe Kong
,
Kayhan Zrar Ghafoor
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Revisions: 2 times (View History)
Update Date: 23 Nov 2023
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