A Study of k-Coverage and Measures of Connectivity in 3D Wireless Sensor Networks

Ammari, Habib M.; Das, Sajal K.

doi:10.1109/tc.2009.166

Cited by 114 publications

(61 citation statements)

References 35 publications

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“…In Section 4 we discuss the deployment and packing strategy in 3D that exploits the structure of Sixsoid. Section 5 is devoted to the comparison of our model with the results proposed in [2]. We end the paper with conclusion and potential research directions for future.…”

Section: Our Contributionmentioning

confidence: 97%

“…In this paper, we propose another 3D solid (the Sixsoid) for this purpose and an extremely pragmatic deployment strategy. We show that using our deployment strategy one is guaranteed to have 6-coverage of approximately 68.5% and 4-coverage of 100% of a given 3D polycubical Field of Interest which is a significant improvement over the gurantees provided in [1], [2].…”

Section: Introductionmentioning

confidence: 92%

“…Prior Work on 3D k-coverage There are relatively fewer works done to address the problem of 3D k-coverage as compared to the 2D version of the problem. In [1], [2] the authors make significant progress on this problem. They discuss the relevance of Reuleaux Tetrahedron in various issues dealing with connectivity and kcoverage in 3D.…”

Section: Arxiv:14010200v3 [Csni] 3 Sep 2014mentioning

confidence: 99%

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Sixsoid: A new paradigm for k-coverage in 3D wireless sensor networks

Pal

Medhi

2015

2015 International Conference on Computing, Communication and Security (ICCCS)

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Abstract-Coverage in 3D wireless sensor network (WSN) is always a very critical issue to deal with. Coming up with good coverage models implies energy efficient networks. K-coverage is a model that ensures that every point in a given 3D Field of Interest (FoI) is guaranteed to be covered by k sensors. In the case of 3D, coming up with a deployment of sensors that gurantees kcoverage becomes much more complicated than in 2D. The basic idea is to come up with a convex body that is guaranteed to be k-covered by taking a specific arrangement of sensors, and then fill the FoI with non-overlapping copies of this body. In this work, we propose a new geometry for the 3D scenario which we call a Sixsoid. Prior to this work, the convex body which was proposed for coverage in 3D was the so called Reuleaux Tetrahedron [1], [2]. Our construction is motivated from a construction that can be applied to the 2D version of the problem [13] in which it implies better guarantees over the Reuleaux Triangle. Our contribution in this paper is twofold, firstly we show how Sixsoid gurantees more coverage volume over Reuleaux Tetrahedron, secondly we show how Sixsoid also guarantees a simpler and more pragmatic deployment strategy for 3D wireless sensor networks. In this paper, we show the construction of Sixsoid, calculate its volume and discuss its implications on the k-coverage in 3D WSNs.

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Section: Our Contributionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 92%

Section: Arxiv:14010200v3 [Csni] 3 Sep 2014mentioning

confidence: 99%

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Sixsoid: A new paradigm for k-coverage in 3D wireless sensor networks

Pal

Medhi

2015

2015 International Conference on Computing, Communication and Security (ICCCS)

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“…-Coverage of sensor networks is one of research focuses in WSNs [1,[10][11][12][13][14]. Huang et al present a polynomial time algorithm formulating circumference coverage to a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least sensors [10] the optimal deployment density bound for two-coverage deployment patterns is derived in [11] where Voronoi polygons generated by sensor nodes are congruent.…”

Section: Related Workmentioning

confidence: 99%

Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks

Sun

Liu

2013

International Journal of Distributed Sensor Networks

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We discuss the problem of maximizing the sensor field coverage for a specific number of sensors while minimizing the distance traveled by the sensor nodes. Thus, we define the movement task as an optimization problem that involves the adjustment of sensor node positions in a coverage optimization mission. We propose a coverage optimization algorithm based on sampling to enhance the coverage of 3D underwater sensor networks. The proposed coverage optimization algorithm is inspired by the simple random sampling in probability theory. The main objective of this study is to lessen computation complexity by dimension reduction, which is composed of two detailed steps. First, the coverage problem in 3D space is converted into a 2D plane for heterogeneous networks via sampling plane in the target 3D space. Second, the optimization in the 2D plane is converted into an optimization in a line segment by using the line sampling method in the sample plane. We establish a quadratic programming mathematical model to optimize the line segment coverage according to the intersection between sensing circles and line segments while minimizing the moving distance of the nodes. The intersection among sensors is decreased to increase the coverage rate, while the effective sensor positions are identified. Simulation results show the effectiveness of the proposed approach.

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“…The paper of (Ammari & Giudici, 2009) focuses on the problem of connected k-coverage in heterogeneous wireless sensor networks, while in (Ammari & Giudici, 2009) the k-coverage problem is examined in the presence of sensor mobility. Finally, in (Ammari & Das, 2010), the problem of connectivity and k-coverage in 3D WSNs is addressed.…”

Section: Target Coverage Under Qos Constraintmentioning

confidence: 99%