[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 study on boundary detection in wireless sensor networks

  • Review Article
  • Published:
Innovations in Systems and Software Engineering Aims and scope Submit manuscript

Abstract

In wireless sensor networks (WSN), a group of sensor nodes are distributed to oversee an area round the clock so that in case of an unnatural event, like forest fire, earth quake, air pollution, oil spill, etc., the event can be reported and the affected area can be located and estimated immediately. In self-organized large WSN with poor wireless connectivity, it is an important issue to optimize the amount of traffic generated in the network, to achieve the required accuracy of area estimation in real time keeping the communication and hence the energy requirement limited. In this article, we present an in-depth study of the existing models, and the cutting-edge techniques evolved so far based on these models, to solve the problem. It exposes the limitations of the existing sensing models and demands further research to develop appropriate realistic models for sensing. A thorough comparative study of various approaches enables us to find the most befitting one to achieve high precision of area estimation, for a specific application with predefined conditions of node deployment and node characteristics.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Van Dinh D, Yoon B, Le HN, Nguyen UQ, Phan KD, Pham LD (2020) Ict enabling technologies for smart cities. In: 2020 22nd international conference on advanced communication technology (ICACT), pp 1180–1192

  2. Chraim F, Bugra Erol Y, Pister K (2016) Wireless gas leak detection and localization. IEEE Trans Ind Inf 12(2):768–779

    Article  Google Scholar 

  3. Park S, Hong S-W, Lee E, Kim S-H, Crespi N (2015) Large-scale mobile phenomena monitoring with energy-efficiency in wireless sensor networks. Comput Netw 81:116–135

    Article  Google Scholar 

  4. Gulati K, Boddu RS, Kapila D, Bangare SL, Chandnani N, Saravanan G (2022) A review paper on wireless sensor network techniques in Internet of Things (IoT). Mater Today Proc 51:161–165. https://doi.org/10.1016/j.matpr.2021.05.067.CMAE’21

  5. Zhang Y, Wang LMZ, Zhou Z (2018) Boundary region detection for continuous objects in wireless sensor networks. Wirel Commun Mobile Comput 13

  6. Dai G, Lv H, Chen L, Zhou B, Xu P (2016) A novel coverage holes discovery algorithm based on voronoi diagram in wireless sensor networks. Int J Hybrid Inf Technol 9(3):273–282

    Google Scholar 

  7. Zhang Y, Zhang X, Wang Z, Liu H (2013) Virtual edge based coverage hole detection algorithm in wireless sensor networks. In: 2013 IEEE wireless communications and networking conference (WCNC), pp 1488–1492

  8. Li W, Zhang W (2015) Coverage hole and boundary nodes detection in wireless sensor networks. J Netw Comput Appl 48:35–43

    Article  Google Scholar 

  9. Ghosh P, Gao J, Gasparri A, Krishnamachari B (2014) Distributed hole detection algorithms for wireless sensor networks. In: 2014 IEEE 11th international conference on mobile ad hoc and sensor systems, pp 257–261

  10. Mishra TK, Sadhu J, Kumar A (2020) Boundary detection in dynamic wireless sensor networks using convex hull techniques. In: 2020 IEEE Calcutta conference (CALCON), pp 368–372

  11. Renold AP, Chandrakala S (2017) Convex-hull-based boundary detection in unattended wireless sensor networks. IEEE Sens Lett 1(4):1–4

    Article  Google Scholar 

  12. Guo P, Cao J, Zhang K (2015) Distributed topological convex hull estimation of event region in wireless sensor networks without location information. IEEE Trans Parallel Distrib Syst 26(1):85–94

    Article  Google Scholar 

  13. Cheng C-F, Hsu C-C (2022) The deterministic sensor deployment problem for barrier coverage in WSNS with irregular shape areas. IEEE Sens J 22(3):2899–2911. https://doi.org/10.1109/JSEN.2021.3137626

    Article  Google Scholar 

  14. Ping H, Zhou Z, Rahman T (2018) Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks. Pers Ubiquitous Comput ACM 22(3):597–613

    Article  Google Scholar 

  15. Shukla S, Misra R, Prasad A (2017) Efficient disjoint boundary detection algorithm for surveillance capable WSNS. J Parallel Distrib Comput 109:245–257

    Article  Google Scholar 

  16. Singh P, Chen Y-C (2020) Sensing coverage hole identification and coverage hole healing methods for wireless sensor networks. Wirel Netw 26(3):2223–2239

    Article  Google Scholar 

  17. Han G, Shen J, Liu L, Shu L (2018) Brtco: a novel boundary recognition and tracking algorithm for continuous objects in wireless sensor networks. IEEE Syst J 12(3):2056–2065

    Article  Google Scholar 

  18. Hong S-W, Ryu Ho-Yong SP, Kim S-H (2015) Reliable continuous object tracking with cost effectiveness in wireless sensor networks. In: 2015 seventh international conference on ubiquitous and future networks, pp 672–676

  19. Yu J, Feng L, Jia L, Gu X, Yu D (2014) A local energy consumption prediction-based clustering protocol for wireless sensor networks. Sensors 14(12):23017–23040

    Article  Google Scholar 

  20. Han G, Shen J, Liu L, Qian A, Shu L (2016) Tgm-cot: energy-efficient continuous object tracking scheme with two-layer grid model in wireless sensor networks. Pers Ubiquitous Comput 20(3):349–359

    Article  Google Scholar 

  21. Kundu S, Das N, Roy S, Saha D (2016) Irregular shaped event boundary estimation in wireless sensor networks. In: International conference on advanced computing, networking and informatics, pp 423–436. Springer

  22. Ullah I, Youn HY, Han Y-H (2021) An efficient data aggregation and outlier detection scheme based on radial basis function neural network for WSN. J Ambient Intell Hum Comput 1–17

  23. Lai Y-H, Cheong S-H, Zhang H, Si Y-W (2021) Coverage hole detection in WSN with force-directed algorithm and transfer learning. Appl Intell 52:5435–5456

    Article  Google Scholar 

  24. Manatakis DV, Manolakos ES (2015) Estimating the spatiotemporal evolution characteristics of diffusive hazards using wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(9):2444–2458

    Article  Google Scholar 

  25. Muhammed T, Shaikh RA (2017) An analysis of fault detection strategies in wireless sensor networks. J Netw Comput Appl 78:267–287

    Article  Google Scholar 

  26. Zhang Z, Mehmood A, Shu L, Huo Z, Zhang Y, Mukherjee M (2018) A survey on fault diagnosis in wireless sensor networks. IEEE Access 6:11349–11364

    Article  Google Scholar 

  27. Panda M, Khilar PM (2015) Distributed byzantine fault detection technique in wireless sensor networks based on hypothesis testing. Comput Electr Eng 48(C):270–285

    Article  Google Scholar 

  28. Elsayed W, Elhoseny M, Riad AM, Hassanien AE (2018) Autonomic self-healing approach to eliminate hardware faults in wireless sensor networks. In: Proceedings of the international conference on advanced intelligent systems and informatics 2017. Springer, Cham, pp 151–160

  29. Liu B, Xu Z, Chen J, Yang G (2015) Toward reliable data analysis for internet of things by bayesian dynamic modeling and computation. In: IEEE China summit and international conference on signal and information processing (ChinaSIP), pp 1027–1031

  30. Wang J, Liu B (2017) Online fault-tolerant dynamic event region detection in sensor networks via trust model. In: IEEE wireless communications and networking conference (WCNC), pp 1–6

  31. Gharamaleki MM, Babaie S (2020) A new distributed fault detection method for wireless sensor networks. IEEE Syst J 14(4):4883–4890

    Article  Google Scholar 

  32. de Souza PSS, Rubin FP, Hohemberger R, Ferreto TC, Lorenzon AF, Luizelli MC, Rossi FD (2020) Detecting abnormal sensors via machine learning: an iot farming WSN-based architecture case study. Measurement 164:108042

    Article  Google Scholar 

  33. Mekonnen Y, Namuduri S, Burton L, Sarwat A, Bhansali S (2019) Machine learning techniques in wireless sensor network based precision agriculture. J Electrochem Soc 167(3):037522

    Article  Google Scholar 

  34. Amouri A, Alaparthy VT, Morgera SD (2020) A machine learning based intrusion detection system for mobile internet of things. Sensors 20(2):461

    Article  Google Scholar 

  35. Jaint B, Indu S, Pandey N, Pahwa K (2019) Malicious node detection in wireless sensor networks using support vector machine. In: 2019 3rd international conference on recent developments in control, automation & power engineering (RDCAPE), pp 247–252. IEEE

  36. Kundu S (2015) Low latency event boundary detection in wireless sensor networks. In: IEEE international conference on advanced networks and telecommuncations systems (ANTS), pp 1–6. IEEE

  37. Kundu S, Das N (2015) Event boundary detection and gathering in wireless sensor networks. In: Applications and innovations in mobile computing (AIMOC), pp 62–67 . IEEE

  38. Priyadarshi R, Gupta B (2020) Coverage area enhancement in wireless sensor network. Microsyst Technol 26(5):1417–1426

    Article  Google Scholar 

  39. Kundu S, Das N, Saha D (2018) Boundary detection and area estimation of an event region in wireless sensor networks using digital-circles. In: Proceedings of the workshop program of the 19th international conference on distributed computing and networking, ACM. ACM

  40. Das S, Kanti DebBarma M (2018) Computational geometry based coverage hole-detection and hole-area estimation in wireless sensor network. J High Speed Netw 24(4):281–296

    Article  Google Scholar 

  41. Kundu S, Das N, Saha D (2021) Real-time event area localisation and estimation in smart environments based on a realistic sensing model. Int J Commun Netw Distrib Syst 27(4):452–478

    Google Scholar 

  42. Lee H-J, Soe MT, Chauhdary SH, Rhee S, Park M-S (2017) A data aggregation scheme for boundary detection and tracking of continuous objects in WSN. Intell Autom Soft Comput 23(1):135–147

    Article  Google Scholar 

  43. Kundu S, Das N, Das A (2020) Time series snapshot of event boundary detection and area estimation in wireless sensor networks. In: 2020 international conference on communication systems & networks (COMSNETS). IEEE, pp 563–566

  44. Ding Z, Fang Q, Hu Y, Wang H, Tao J (2018) Shape retrieval with adjustable precision for hole detection in 3d wireless sensor networks. In: 2018 24th asia-pacific conference on communications (APCC), pp 622–627. IEEE

  45. Dang X, Shao C, Hao Z (2019) Target detection coverage algorithm based on 3d-voronoi partition for three-dimensional wireless sensor networks. Mobile Inf Syst

  46. Shu L, Mukherjee M, Wu X (2016) Toxic gas boundary area detection in large-scale petrochemical plants with industrial wireless sensor networks. IEEE Commun Mag 54(10):22–28

    Article  Google Scholar 

  47. Beghdad R, Lamraoui A (2016) Boundary and holes recognition in wireless sensor networks. J Innov Digit Ecosyst 3(1):1–14

    Article  Google Scholar 

  48. Yuan K, Ling Q, Tian Z (2015) Communication-efficient decentralized event monitoring in wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(8):2198–2207

    Article  Google Scholar 

  49. Saha D, Pal S, Das N, Bhattacharya B (2017) Fast estimation of area-coverage for wireless sensor networks based on digital geometry. IEEE Trans Multi-Scale Comput Syst 3(3):166–180

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Guru Nanak Institute of Technology, Kolkata, India

    Srabani Kundu

  2. Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India

    Nabanita Das

Authors
  1. Srabani Kundu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nabanita Das
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Srabani Kundu.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kundu, S., Das, N. A study on boundary detection in wireless sensor networks. Innovations Syst Softw Eng 19, 217–225 (2023). https://doi.org/10.1007/s11334-022-00488-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11334-022-00488-w

Keywords

Search

Navigation