TY - JOUR
T1 - Distributed Detection of Sparse Signals with Censoring Sensors Via Locally Most Powerful Test
AU - Li, Chengxi
AU - Li, Gang
AU - Varshney, Pramod K.
N1 - Funding Information:
Manuscript received November 28, 2019; revised January 12, 2020; accepted January 23, 2020. Date of publication January 30, 2020; date of current version March 6, 2020. This work was supported in part by National Natural Science Foundation of China under Grants 61790551 and 61925106, in part by the Civil Space Advance Research Program of China under Grant D010305, and in part by National Science Foundation of USA under Grant ENG 60064237. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ioannis Schizas. (Corresponding author: Gang Li.) C. Li and G. 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:
© 1994-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - In this letter, we consider the problem of distributed detection of stochastic sparse signals in battery-powered sensor networks (SNs). For this problem, an original locally most powerful test (oLMPT) detector has previously been developed, where compressed measurements are collected from all local sensors and then fused at the fusion center (FC) for making the global decision. However, since the sensors always operate on limited energy resources, allowing all the nodes to send their observations to the FC all the time exerts tremendous pressure on their energy consumption and hinders the longevity of the sensors. To solve this problem, we propose a new censoring LMPT (cen-LMPT) detector by combining the strengths of censoring strategy and the oLMPT detector, where sensors are designated to merely send observations deemed informative enough so as to utilize the local energy more efficiently, and the FC still makes the global decision based on LMPT. We analytically derive the relationship between the detection performance and the communication rate for the proposed detector. It is shown that, compared with the oLMPT detector, the proposed cen-LMPT detector with the same number of nodes can achieve almost the same detection performance with significantly lower communication rate and, therefore, much lower local energy consumption. The simulation results verify our theoretical findings.
AB - In this letter, we consider the problem of distributed detection of stochastic sparse signals in battery-powered sensor networks (SNs). For this problem, an original locally most powerful test (oLMPT) detector has previously been developed, where compressed measurements are collected from all local sensors and then fused at the fusion center (FC) for making the global decision. However, since the sensors always operate on limited energy resources, allowing all the nodes to send their observations to the FC all the time exerts tremendous pressure on their energy consumption and hinders the longevity of the sensors. To solve this problem, we propose a new censoring LMPT (cen-LMPT) detector by combining the strengths of censoring strategy and the oLMPT detector, where sensors are designated to merely send observations deemed informative enough so as to utilize the local energy more efficiently, and the FC still makes the global decision based on LMPT. We analytically derive the relationship between the detection performance and the communication rate for the proposed detector. It is shown that, compared with the oLMPT detector, the proposed cen-LMPT detector with the same number of nodes can achieve almost the same detection performance with significantly lower communication rate and, therefore, much lower local energy consumption. The simulation results verify our theoretical findings.
KW - Censoring sensors
KW - distributed detection
KW - energy constraints
KW - locally most powerful tests
KW - sparse signals
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U2 - 10.1109/LSP.2020.2970580
DO - 10.1109/LSP.2020.2970580
M3 - Article
AN - SCOPUS:85081997998
SN - 1070-9908
VL - 27
SP - 346
EP - 350
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
M1 - 8976280
ER -