An optimal new-node placement to enhance the coverage of wireless sensor networks

Hou, Yung-Tsung; Chen, Chia-Mei; Jeng, Bingchiang

doi:10.1007/s11276-009-0186-x

Cited by 18 publications

(15 citation statements)

References 14 publications

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“…This method redefines sensor nodes to reduce coverage holes and improve network coverage, saves energy consumption, and guarantees connectivity while limiting sensor mobile costs. By comparing with other deployment methods, it is found that this method can improve network coverage and reduce the mobile costs of WSNs [8].…”

Section: Deployment Methodsmentioning

confidence: 99%

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

Mao

Jiang

Zhang

2019

J Wireless Com Network

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This paper mainly studies the deployment of wireless sensor network nodes in the warehouse environment monitoring system, discusses the deployment algorithm of wireless sensor network nodes in the warehouse environment, and finds out the node deployment scheme with better network performance through comparison. Wireless sensor network node deployment is the basis of wireless sensor network application in storage environment monitoring system. It affects the performance of the whole network and is the primary problem to be solved in network application. This paper discusses the advantages of wireless sensor network in the monitoring of storage environment, especially the deployment and simulation analysis of sensor nodes in the warehouse environment. Aiming at the influence of sensor perception model on the effectiveness of the node deployment plan, this paper proposes a node deployment collaborative perception model based on 0-1 perception model and exponential model. The sensor node deployment problem is transformed into a three-dimensional node deployment problem. Finally, the algorithm is applied to tobacco storage environment. In order to verify the effectiveness of the proposed algorithm, the scheme obtained by the proposed algorithm is compared with that obtained by the corresponding deployment algorithm in this paper. The comparison results show that the overall performance of the algorithm is better than that of the usual scheme.

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Section: Deployment Methodsmentioning

confidence: 99%

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

Mao

Jiang

Zhang

2019

J Wireless Com Network

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“…Aziz et al, 2009;Hou et al, 2010). However, for DSNs, these two geometric structures have not drawn much attention of researchers on the field coverage issues.…”

Section: Introductionmentioning

confidence: 99%

Localised sensor direction adjustments with geometric structures of Voronoi diagram and Delaunay triangulation for directional sensor networks

Sung

Yang

2015

IJAHUC

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A directional sensor network (DSN) consists of directional sensors instead of the omnidirectional ones in the conventional wireless sensor networks. The sensing coverage of a DSN depends on the directionality and size-specific sensing angle of the sensors. The conditions are dissimilar from those of omnidirectional sensor networks for researches, especially on the sensing coverage. For coverage problem in DSNs, the geometric structures of Voronoi diagram and Delaunay triangulation have not drawn the attention of researchers. This study utilised Voronoi diagram and Delaunay triangulation and proposed four basic distributed and localised sensor direction adjustment algorithms with the characteristics of these two geometric structures to explore the field coverage improvement in DSNs. The simulation results and comparisons of coverage performance of the proposed four basic algorithms are provided. They can give the clarity of the performance results and have a reference value for future advanced studies on various coverage issues in DSNs.

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“…For example, when it is required to reduce the length of the longest edge (i.e., the maximum transmission range) in a bottleneck path between two transceivers, s and t, in an existing network consisting of n transceivers, by adding k new transceivers. See [11,12,16] for more applications in sensor networks.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the EBST problem, the EBSP problem is solvable in polynomial time. Ignoring (only for now) the dependence on k, Hou et al [11] gave an O(n 2 log n))-time exact algorithm for the problem, based on a binary search in the set of all potential lengths (of the longest edge). In this paper, we show that it suffices to consider a certain subset of potential lengths.…”

Section: Introductionmentioning

confidence: 99%

“…Given a set P of n points in the plane, source and destination points s, t ∈ P , and an integer k > 0, one has to locate k Steiner points, such that the length of the longest edge of a bottleneck path between s and t is minimized. In this paper, we present an O(n log 2 n)-time algorithm that computes an optimal solution, for any constant k. This problem was previously studied by Hou et al [11], who gave an O(n 2 log n)-time algorithm. We also study the dual version of the problem, where a value λ > 0 is given (instead of k), and the goal is to locate as few Steiner points as possible, so that the length of the longest edge of a bottleneck path between s and t is at most λ.…”

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confidence: 98%

See 1 more Smart Citation

The euclidean bottleneck steiner path problem

Abu-Affash

Carmi

Katz

et al. 2011

Proceedings of the Twenty-Seventh Annual Symposium on Computational Geometry

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We consider a geometric optimization problem that arises in network design. Given a set P of n points in the plane, source and destination points s, t ∈ P , and an integer k > 0, one has to locate k Steiner points, such that the length of the longest edge of a bottleneck path between s and t is minimized. In this paper, we present an O(n log 2 n)-time algorithm that computes an optimal solution, for any constant k. This problem was previously studied by Hou et al. [11], who gave an O(n 2 log n)-time algorithm. We also study the dual version of the problem, where a value λ > 0 is given (instead of k), and the goal is to locate as few Steiner points as possible, so that the length of the longest edge of a bottleneck path between s and t is at most λ.Our algorithms are based on two new geometric structures that we develop -an (α, β)-pair decomposition of P and * a floor (1 + ε)-spanner of P . For real numbers β > α > 0, an (α, β)-pair decomposition of P is a collection W = {(A1, B1), . . . , (Am, Bm)} of pairs of subsets of P , satisfying: (i) For each pair (Ai, Bi) ∈ W, the radius of the minimum enclosing circle of Ai (resp. Bi) is at most α, and (ii) For any p, q ∈ P , such that |pq| ≤ β, there exists a single pair (Ai, Bi) ∈ W, such that p ∈ Ai and q ∈ Bi, or vice versa. We construct (a compact representation of) an (α, β)-pair decomposition of P in time O((β/α) 3 n log n).Finally, for the complete graph with vertex set P and weight function w(p, q) = |pq| , we construct a (1 + ε)-spanner of size O(n/ε 4 ) in time O((1/ε 4 )n log 2 n), even though w is not a metric.

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An optimal new-node placement to enhance the coverage of wireless sensor networks

Cited by 18 publications

References 14 publications

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

Analysis of node deployment in wireless sensor networks in warehouse environment monitoring systems

Localised sensor direction adjustments with geometric structures of Voronoi diagram and Delaunay triangulation for directional sensor networks

The euclidean bottleneck steiner path problem

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