Accuracy of Homology based Coverage Hole Detection for Wireless Sensor Networks on Sphere

Abstract : Homology theory has attracted great attention because it can provide novel and powerful solutions to address coverage problems in wireless sensor networks. They usually use an easily computable algebraic object, Rips complex, to detect coverage holes. But Rips complex may miss some coverage holes in some cases. In this paper, we investigate homology-based coverage hole detection for wireless sensor networks on sphere. The situations when Rips complex may miss coverage holes are first presented. Then we choose the proportion of the area of coverage holes missed by Rips complex as a metric to evaluate the accuracy of homology-based coverage hole detection approaches. Three different cases are considered for the computation of accuracy. For each case, closed-form expressions for lower and upper bounds of the accuracy are derived. Simulation results are well consistent with the analytical lower and upper bounds, with maximum differences of 0.5\% and 3\% respectively. Furthermore, it is shown that the radius of sphere has little impact on the accuracy if it is much larger than communication and sensing radii of each sensor.
Document type :
Journal articles
Complete list of metadatas

Cited literature [26 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-00783248
Contributor : Feng Yan <>
Submitted on : Tuesday, July 15, 2014 - 3:55:59 PM
Last modification on : Wednesday, March 13, 2019 - 5:24:35 PM
Long-term archiving on : Friday, November 21, 2014 - 6:40:14 PM

File

TWC_final_submission.pdf
Files produced by the author(s)

Identifiers

Citation

Feng Yan, Philippe Martins, Laurent Decreusefond. Accuracy of Homology based Coverage Hole Detection for Wireless Sensor Networks on Sphere. IEEE Transactions on Wireless Communications, Institute of Electrical and Electronics Engineers, 2014, pp.3583 - 3595. ⟨10.1109/TWC.2014.2314106⟩. ⟨hal-00783248v4⟩

Share

Metrics

Record views

1047

Files downloads

193