Area coverage for clustered directional sensor networks using Voronoi diagram

Sharmin, Selina; Nur, Fernaz Narin; Razzaque, Md. Abdur; Rahman, Md. Mustafizur

doi:10.1109/wiecon-ece.2015.7443941

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Guvensan et al [1] surveyed the coverage problem in WMSN [2] comprehensively. The coverage problem is classified into three distinct categories: (1) barrier coverage [3], (2) target coverage [5], and (3) area coverage [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. The main concern of barrier coverage is to detect any intruder attempting to cross the barrier covered by a DSN.…”

Section: Related Workmentioning

confidence: 99%

“…Our main goal is to increase the covered area percentage with less number of active directional sensors. The current solution includes greedy algorithms [16], geometrically inspired methods such as Delaunay triangulation [8,17] and Voronoi diagram [6,7,15,19], and artificial intelligence (AI) algorithms [14]. We have used the Voronoi diagram for solving the problem as used in [6].…”

Section: Introductionmentioning

confidence: 99%

“…The coverage problem has been studied in various forms, such as barrier coverage [3], mobile/stationary target coverage [5], and area coverage [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. We study the area coverage problem.…”

Section: Introductionmentioning

confidence: 99%

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Coverage improvement using Voronoi diagrams in directional sensor networks

Zarei

Bag-Mohammadi

2021

IET Wireless Sensor Systems

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Recently, the area coverage problem has emerged in the directional sensor network (DSN), where the sensor's sensed area depends on its working direction and viewing angle. This study has proposed a new algorithm based on the Voronoi diagram, called prioritized geometric area coverage (PGAC), to increase DSN's covered area. In a Voronoi diagram, all internal points of a convex polygon (cell) formed around a sensor are closer to the sensor than any other sensor. Therefore, the best sensor for covering a Voronoi cell area is the corresponding sensor of the cell. In contrast to similar approaches, PGAC considers the relation between the cell area and the sensor's covered area when selecting a sensor's working direction. It categorizes Voronoi cells, based on their geometric sizes, into three categories. In each category, PGAC adjusts the sensor's working direction to maximize the covered area and minimize the overlapping between adjacent cells. It also turns off redundant sensors for extending the network lifetime. Our simulation results showed that PGAC increases the covered area and decreases the number of active sensors compared to similar methods. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coverage improvement using Voronoi diagrams in directional sensor networks

Zarei

Bag-Mohammadi

2021

IET Wireless Sensor Systems

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“…Wang et al [76] presented a clustering algorithm based on magnetic induction (MI) communication for underwater sensor networks, named IHENPC, which was the improved HENPC (High-Energy Node Priority Clustering) algorithm [77]. In the algorithm, the Voronoi diagram [78] and the node density distribution were used to realize clustering. The CH selection was built on the residual energy and geometry range of underwater nodes.…”

Section: Progress Of Clustering Technologiesmentioning

confidence: 99%

A Software-Defined Clustering Mechanism for Underwater Acoustic Sensor Networks

Wang

Gao

et al. 2019

IEEE Access

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Underwater acoustic sensor networks (UASNs) are important technical means to explore the ocean realm. Clustering is a crucial strategy which determines the performance and the lifetime of UASNs significantly. As a technological innovation, software defined networking (SDN) separates the data plane from the control plane to build a programmable network infrastructure. This paper presents a clustering mechanism based on SDN. Firstly, we introduce the architecture of software-defined UASN named SD-UASN. Secondly, we investigate hardware models of the underwater node and the surface controller. Thirdly, we define the communication procedure of SD-UASN, and implement the clustering mechanism. Finally, we perform simulations to verify the effectiveness of the proposed mechanism. The results reveal that a tradeoff of multiple constraints is achieved, and the performance of the clustering mechanism can be enhanced greatly. This work provides essential theoretical and technical support for software-defined UASNs.INDEX TERMS Underwater acoustic sensor networks (UASNs), clustering algorithm, software defined networking (SDN), software-defined UASN (SD-UASN), OpenFlow.

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“…Coverage problem has been modeled as a 2D problem in the majority of the literature works. In fact, the common approach is to model the sensor coverage area as a 2D region and consider 2D points (point coverage) [3], 2D areas (area coverage) [11], [12], 2D overlap among sensors (strong/weak barrier coverage) [13], [14] or 2D trajectories (path coverage) [10] depending on the application. Even for the line coverage problem, the majority of works use 2D models and consider lines as 2D targets [6], [7] and propose to cover them with 2D coverage areas of sensors.…”

Section: Introductionmentioning

confidence: 99%

1D Modeling of Sensor Selection Problem for Weak Barrier Coverage and Gap Mending in Wireless Sensor Networks

Sadeghi¹,

MohammadReza²,

Valaee³

et al. 2017

Preprint

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In this paper, we first remodel the line coverage as a 1D discrete problem with co-linear targets. Then, an orderbased greedy algorithm, called OGA, is proposed to solve the problem optimally. It will be shown that the existing order in the 1D modeling, and especially the resulted Markov property of the selected sensors can help design greedy algorithms such as OGA. These algorithms demonstrate optimal/efficient performance and have lower complexity compared to the state-of-the-art. Furthermore, it is demonstrated that the conventional continuous line coverage problem can be converted to an equivalent discrete problem and solved optimally by OGA. Next, we formulate the well-known weak barrier coverage problem as an instance of the continuous line coverage problem (i.e. a 1D problem) as opposed to the conventional 2D graph-based models. We demonstrate that the equivalent discrete version of this problem can be solved optimally and faster than the state-of-the-art methods using an extended version of OGA, called K-OGA. Moreover, an efficient local algorithm, called LOGM, is proposed to mend barrier gaps due to sensor failure. In the case of m gaps, LOGM is proved to select at most 2m − 1 sensors more than the optimal while being local and implementable in distributed fashion. We demonstrate the optimal/efficient performance of the proposed algorithms via extensive simulations.

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Area coverage for clustered directional sensor networks using Voronoi diagram

Cited by 11 publications

References 11 publications

Coverage improvement using Voronoi diagrams in directional sensor networks

Coverage improvement using Voronoi diagrams in directional sensor networks

A Software-Defined Clustering Mechanism for Underwater Acoustic Sensor Networks

1D Modeling of Sensor Selection Problem for Weak Barrier Coverage and Gap Mending in Wireless Sensor Networks

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