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Erschienen in: Innovations in Systems and Software Engineering 2/2023

28.09.2022 | Review Article

A study on boundary detection in wireless sensor networks

verfasst von: Srabani Kundu, Nabanita Das

Erschienen in: Innovations in Systems and Software Engineering | Ausgabe 2/2023

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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.

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Literatur
1.
Zurück zum Zitat 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 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.
Zurück zum Zitat Chraim F, Bugra Erol Y, Pister K (2016) Wireless gas leak detection and localization. IEEE Trans Ind Inf 12(2):768–779CrossRef Chraim F, Bugra Erol Y, Pister K (2016) Wireless gas leak detection and localization. IEEE Trans Ind Inf 12(2):768–779CrossRef
3.
Zurück zum Zitat 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 81:116–135CrossRef 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 81:116–135CrossRef
5.
Zurück zum Zitat Zhang Y, Wang LMZ, Zhou Z (2018) Boundary region detection for continuous objects in wireless sensor networks. Wirel Commun Mobile Comput 13 Zhang Y, Wang LMZ, Zhou Z (2018) Boundary region detection for continuous objects in wireless sensor networks. Wirel Commun Mobile Comput 13
6.
Zurück zum Zitat 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 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
7.
Zurück zum Zitat 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 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.
Zurück zum Zitat Li W, Zhang W (2015) Coverage hole and boundary nodes detection in wireless sensor networks. J Netw Comput Appl 48:35–43CrossRef Li W, Zhang W (2015) Coverage hole and boundary nodes detection in wireless sensor networks. J Netw Comput Appl 48:35–43CrossRef
9.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat Renold AP, Chandrakala S (2017) Convex-hull-based boundary detection in unattended wireless sensor networks. IEEE Sens Lett 1(4):1–4CrossRef Renold AP, Chandrakala S (2017) Convex-hull-based boundary detection in unattended wireless sensor networks. IEEE Sens Lett 1(4):1–4CrossRef
12.
Zurück zum Zitat 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):85–94CrossRef 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):85–94CrossRef
14.
Zurück zum Zitat 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):597–613CrossRef 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):597–613CrossRef
15.
Zurück zum Zitat Shukla S, Misra R, Prasad A (2017) Efficient disjoint boundary detection algorithm for surveillance capable WSNS. J Parallel Distrib Comput 109:245–257CrossRef Shukla S, Misra R, Prasad A (2017) Efficient disjoint boundary detection algorithm for surveillance capable WSNS. J Parallel Distrib Comput 109:245–257CrossRef
16.
Zurück zum Zitat Singh P, Chen Y-C (2020) Sensing coverage hole identification and coverage hole healing methods for wireless sensor networks. Wirel Netw 26(3):2223–2239CrossRef Singh P, Chen Y-C (2020) Sensing coverage hole identification and coverage hole healing methods for wireless sensor networks. Wirel Netw 26(3):2223–2239CrossRef
17.
Zurück zum Zitat 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):2056–2065CrossRef 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):2056–2065CrossRef
18.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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):23017–23040CrossRef 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):23017–23040CrossRef
20.
Zurück zum Zitat 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):349–359CrossRef 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):349–359CrossRef
21.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 52:5435–5456CrossRef 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 52:5435–5456CrossRef
24.
Zurück zum Zitat Manatakis DV, Manolakos ES (2015) Estimating the spatiotemporal evolution characteristics of diffusive hazards using wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(9):2444–2458CrossRef Manatakis DV, Manolakos ES (2015) Estimating the spatiotemporal evolution characteristics of diffusive hazards using wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(9):2444–2458CrossRef
25.
Zurück zum Zitat Muhammed T, Shaikh RA (2017) An analysis of fault detection strategies in wireless sensor networks. J Netw Comput Appl 78:267–287CrossRef Muhammed T, Shaikh RA (2017) An analysis of fault detection strategies in wireless sensor networks. J Netw Comput Appl 78:267–287CrossRef
26.
Zurück zum Zitat Zhang Z, Mehmood A, Shu L, Huo Z, Zhang Y, Mukherjee M (2018) A survey on fault diagnosis in wireless sensor networks. IEEE Access 6:11349–11364CrossRef Zhang Z, Mehmood A, Shu L, Huo Z, Zhang Y, Mukherjee M (2018) A survey on fault diagnosis in wireless sensor networks. IEEE Access 6:11349–11364CrossRef
27.
Zurück zum Zitat Panda M, Khilar PM (2015) Distributed byzantine fault detection technique in wireless sensor networks based on hypothesis testing. Comput Electr Eng 48(C):270–285CrossRef Panda M, Khilar PM (2015) Distributed byzantine fault detection technique in wireless sensor networks based on hypothesis testing. Comput Electr Eng 48(C):270–285CrossRef
28.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat Gharamaleki MM, Babaie S (2020) A new distributed fault detection method for wireless sensor networks. IEEE Syst J 14(4):4883–4890CrossRef Gharamaleki MM, Babaie S (2020) A new distributed fault detection method for wireless sensor networks. IEEE Syst J 14(4):4883–4890CrossRef
32.
Zurück zum Zitat 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 164:108042CrossRef 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 164:108042CrossRef
33.
Zurück zum Zitat 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):037522CrossRef 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):037522CrossRef
34.
Zurück zum Zitat Amouri A, Alaparthy VT, Morgera SD (2020) A machine learning based intrusion detection system for mobile internet of things. Sensors 20(2):461CrossRef Amouri A, Alaparthy VT, Morgera SD (2020) A machine learning based intrusion detection system for mobile internet of things. Sensors 20(2):461CrossRef
35.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat Priyadarshi R, Gupta B (2020) Coverage area enhancement in wireless sensor network. Microsyst Technol 26(5):1417–1426CrossRef Priyadarshi R, Gupta B (2020) Coverage area enhancement in wireless sensor network. Microsyst Technol 26(5):1417–1426CrossRef
39.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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):281–296CrossRef 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):281–296CrossRef
41.
Zurück zum Zitat 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 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
42.
Zurück zum Zitat 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):135–147CrossRef 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):135–147CrossRef
43.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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 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.
Zurück zum Zitat 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):22–28CrossRef 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):22–28CrossRef
47.
Zurück zum Zitat Beghdad R, Lamraoui A (2016) Boundary and holes recognition in wireless sensor networks. J Innov Digit Ecosyst 3(1):1–14CrossRef Beghdad R, Lamraoui A (2016) Boundary and holes recognition in wireless sensor networks. J Innov Digit Ecosyst 3(1):1–14CrossRef
48.
Zurück zum Zitat Yuan K, Ling Q, Tian Z (2015) Communication-efficient decentralized event monitoring in wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(8):2198–2207CrossRef Yuan K, Ling Q, Tian Z (2015) Communication-efficient decentralized event monitoring in wireless sensor networks. IEEE Trans Parallel Distrib Syst 26(8):2198–2207CrossRef
49.
Zurück zum Zitat 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):166–180CrossRef 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):166–180CrossRef
Metadaten
Titel
A study on boundary detection in wireless sensor networks
verfasst von
Srabani Kundu
Nabanita Das
Publikationsdatum
28.09.2022
Verlag
Springer London
Erschienen in
Innovations in Systems and Software Engineering / Ausgabe 2/2023
Print ISSN: 1614-5046
Elektronische ISSN: 1614-5054
DOI
https://doi.org/10.1007/s11334-022-00488-w

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