Accuracy of homology based approaches for coverage hole detection in wireless sensor networks

Yan, Feng; Martins, Philippe; Decreusefond, Laurent

doi:10.1109/icc.2012.6364341

Cited by 23 publications

(12 citation statements)

References 14 publications

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“…One can see easily that the Vietoris-Rips complex R 2r (ω) is an approximation of theCech complex C r (ω) that is way easier to compute since it is a clique complex based only on the neighbors graph information. This approximation is quite good: in the case of a random uncorrelated deployment with network nodes deployed according to a Poisson point process the error is less than 0.06% in the computation of the covered area [15]. An example of ȃ Cech complex representing a wireless network can be seen in Fig.…”

Section: Definition 3 (Vietoris-rips Complex)mentioning

confidence: 95%

Computing the k-coverage of a wireless network

Vergne

Decreusefond

Martins

2019

Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools

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Coverage is one of the main quality of service of a wireless network. k-coverage, that is to be covered simultaneously by k network nodes, is synonym of reliability and numerous applications such as multiple site MIMO features, or handovers. We introduce here a new algorithm for computing the k-coverage of a wireless network. Our method is based on the observation that k-coverage can be interpreted as k layers of 1-coverage, or simply coverage. We use simplicial homology to compute the network's topology and a reduction algorithm to indentify the layers of 1-coverage. We provide figures and simulation results to illustrate our algorithm.

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Section: Definition 3 (Vietoris-rips Complex)mentioning

confidence: 95%

Computing the k-coverage of a wireless network

Vergne

Decreusefond

Martins

2019

Proceedings of the 12th EAI International Conference on Performance Evaluation Methodologies and Tools

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“…Please refer to [16] for the proof. A PPP whose intensity is proportional to the Lebesgue measure is stationary in the sense that any translation of its atoms by a fixed vector does not change its law.…”

Section: Bounds On Proportion Of the Area Of Triangular Holesmentioning

confidence: 99%

Homology-Based Distributed Coverage Hole Detection in Wireless Sensor Networks

Yan

Vergne

Martins

et al. 2015

IEEE/ACM Trans. Networking

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International audienceHomology theory provides new and powerful solutions to address the coverage problems in wireless sensor networks (WSNs). They are based on algebraic objects, such as Čech complex and Rips complex. Čech complex gives accurate information about coverage quality, but requires a precise knowledge of the relative locations of nodes. This assumption is rather strong and hard to implement in practical deployments. Rips complex provides an approximation of Čech complex. It is easier to build and does not require any knowledge of nodes location. This simplicity is at the expense of accuracy. Rips complex cannot always detect all coverage holes. It is then necessary to evaluate its accuracy. This work proposes to use the proportion of the area of undiscovered coverage holes as performance criteria. Investigations show that it depends on the ratio between communication and sensing radii of a sensor. Closed-form expressions for lower and upper bounds of the accuracy are also derived. For those coverage holes that can be discovered by Rips complex, a homology-based distributed algorithm is proposed to detect them. Simulation results are consistent with the proposed analytical lower bound, with a maximum difference of 0.5%. Upper-bound performance depends on the ratio of communication and sensing radii. Simulations also show that the algorithm can localize about 99% coverage holes in about 99% cases

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“…These concepts are good at rapid detection of coverage holes [ 17 , 18 ] and optimization compensation [ 19 ]. Furthermore, compared with the rigorous Ĉech complex that has difficulty in satisfying its application conditions, the Rips complex was verified to have a missing rate problem that could be mended in coverage hole detection by adjusting the ratio between the communication radius and the sensing radius [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

“…Nodes, whichare able to probe object independently, are be assumed toobtain their distances and calculatepositions through GPS [ 23 ], directional antennas technology [ 28 ] or others recommended technologies [ 16 , 17 , 19 , 20 , 22 , 26 ]. Just like many researches [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ], given the position of

, its Euclidean distance to

is regarded as an important adjustment factor in the detection/sensing job.…”

Section: System Modelmentioning

confidence: 99%

A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks

Qin

Zheng

Tian

2015

Sensors

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To solve the problem of coverage performance assessment, this study proposes an evaluation method based on the trajectory of the target, which is applicable to universal sensor networks, including both heterogeneous and homogeneous sensor networks. Different from the traditional Voronoi algorithm, the proposed Improved Coverage Force Division (ICFD) plans a coverage force division map whichscales the qualitative coverage performancebasedon both covering intensities andlocations of the nodes. Furthermore, the Trajectory-based Evaluating Schedule (TES) is responsible for solving the quantitative coverage evaluationproblem by measuringthe resulting trajectories’ Balance Values (BVs). A model of weak-point ranking conjoined in consideration of coverage force and distance can guide future deployment to compensate coverage. Comparative trials using the greedy algorithm, Voronoi algorithm, and the proposed TES verify that TES achieves the approximate results for two-stage and multistage heterogeneous sensor networks with acceptable difference and lower complexity, and it is superior to the Voronoi algorithm in homogeneous sensor networks interms of breaking the four-point circle block.

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Accuracy of homology based approaches for coverage hole detection in wireless sensor networks

Cited by 23 publications

References 14 publications

Computing the k-coverage of a wireless network

Computing the k-coverage of a wireless network

Homology-Based Distributed Coverage Hole Detection in Wireless Sensor Networks

A Trajectory-Based Coverage Assessment Approach for Universal Sensor Networks

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