TY - JOUR
T1 - Distributed Detection of Sparse Signals with Physical Layer Secrecy Constraints
T2 - A Falsified Censoring Strategy
AU - Li, Chengxi
AU - Li, Gang
AU - Varshney, Pramod K.
N1 - Funding Information:
Manuscript received May 31, 2020; revised September 6, 2020; accepted September 29, 2020. Date of publication October 7, 2020; date of current version October 29, 2020. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Augusto Aubry. This work was supported in part by the National Natural Science Foundation of China under Grants 61790551 and 61925106, and in part by the National Science Foundation of USA under Grant ENG 60064237. (Corresponding author: Gang Li.) Chengxi Li and Gang Li are with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (e-mail: lcx18@mails.tsinghua.edu.cn; gangli@tsinghua.edu.cn).
Publisher Copyright:
© 1991-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - In this paper, we investigate the problem of distributed detection of sparse signals in wireless sensor networks (WSNs) with censoring sensors in the presence of an Eavesdropper (Eve). The Eve, which is able to perfectly monitor the 'idle' and 'busy' states of the communication channels between the local sensors and the fusion center (FC), also wants to detect the sparse signals. For the classical problem of distributed detection with censoring sensors, applying appropriate censoring thresholds to attain the same transmission probability under either hypothesis to ensure perfect secrecy has previously been studied. We refer to it as the clairvoyant censoring method since it requires full knowledge of the distributions of the observations. However, the clairvoyant censoring method is not practical to implement for the detection of sparse signals with an unknown sparsity level. In this paper, a falsified censoring (FACE) strategy is proposed, in which a group of cooperating deceitful nodes censor their local observations in a way that is opposite to what would be done at the regular nodes. Based on this setup, the optimization problem to maximize the detection performance at the FC under communication and secrecy constraints is formulated and numerical methods are provided to find the near optimal system parameters. Simulation results exhibit excellent performance of our proposed strategy.
AB - In this paper, we investigate the problem of distributed detection of sparse signals in wireless sensor networks (WSNs) with censoring sensors in the presence of an Eavesdropper (Eve). The Eve, which is able to perfectly monitor the 'idle' and 'busy' states of the communication channels between the local sensors and the fusion center (FC), also wants to detect the sparse signals. For the classical problem of distributed detection with censoring sensors, applying appropriate censoring thresholds to attain the same transmission probability under either hypothesis to ensure perfect secrecy has previously been studied. We refer to it as the clairvoyant censoring method since it requires full knowledge of the distributions of the observations. However, the clairvoyant censoring method is not practical to implement for the detection of sparse signals with an unknown sparsity level. In this paper, a falsified censoring (FACE) strategy is proposed, in which a group of cooperating deceitful nodes censor their local observations in a way that is opposite to what would be done at the regular nodes. Based on this setup, the optimization problem to maximize the detection performance at the FC under communication and secrecy constraints is formulated and numerical methods are provided to find the near optimal system parameters. Simulation results exhibit excellent performance of our proposed strategy.
KW - Censoring strategy
KW - eavesdroppers
KW - physical layer secrecy
KW - sparse signal detection
KW - wireless sensor networks
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U2 - 10.1109/TSP.2020.3028700
DO - 10.1109/TSP.2020.3028700
M3 - Article
AN - SCOPUS:85096034543
SN - 1053-587X
VL - 68
SP - 6040
EP - 6054
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
M1 - 9216141
ER -