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Worst-case drift detection of sensor networks: performances and algorithms

Abstract : The goal of this paper is to derive algorithms that are able to detect unreliable/drifted sensors, in a sensor network. To recover the state of each sensor, we restrict ourselves to a particular decoder, inspired by graph partitioning problems. We provide necessary and sufficient conditions over the measurements such that the decoder perfectly recovers each sensor binary state. The outputs of the decoder can be computed using a dynamic programming approach. One challenging part of this approach is the complexity of the dynamic programming equation. Indeed, the resolution time will increase exponentially with the number of sensors. Therefore, we propose an efficient heuristic method that approximately solves the problem. We study the performance of our algorithm using simulations.
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Contributor : Alexandre Reiffers-Masson <>
Submitted on : Thursday, April 23, 2020 - 5:41:43 PM
Last modification on : Wednesday, August 5, 2020 - 3:47:24 AM


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  • HAL Id : hal-02552670, version 1


Alexandre Reiffers-Masson. Worst-case drift detection of sensor networks: performances and algorithms. 2020. ⟨hal-02552670⟩



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