Browsing by Author "Wang, Tsang-Yi"

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A Neyman-Pearson Type Sensor Censoring Scheme for Compressive Distributed Sparse Signal Recovery
(2018-01-01)
Wu, Jwo-Yuh
;
Yang, Ming-Hsun
;
Wang, Tsang-Yi
;
電機工程學系
;
電信工程研究所
To strike a balance between energy efficiency and data quality control, this paper proposes a Neyman-Pearson type sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based on wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor (i) directly transmits the real-valued compressed data if the sensing vector support is detected to be overlapped with the signal support, (ii) sends a one-bit hard decision if empty support overlap is inferred, (iii) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. Computer simulations are used to illustrate the performance of the proposed scheme.
A Sliding Window Approach for Dynamic Event-Region Detection in Sensor Networks
(2014-01-01)
Wang, Tsang-Yi
;
Yang, Ming-Hsun
;
Wu, Jwo-Yuh
;
電機工程學系
;
Department of Electrical and Computer Engineering
Event region detection is an important problem in wireless sensor networks. However, a fundamental assumption required for the event-region detection schemes proposed in the literature is that the event-region under detection is quasi-static, i.e., the event-regions do not change during the detection period. This assumption could fail in detecting fast varying events, such as forest fire events and mudslides. Therefore, the detection task considered in the present study is to cope with the continuously varying event regions. Specifically, we adopt a sliding window approach, in which only the sensor measurements from the present to a fixed length time ago are utilized for making decision at any particular time slot. Our simulation results demonstrate the advantages of the proposed detection scheme.
A Space-Time Fusion Scheme for Dynamic-Event Region Detection in Sensor Networks
(2016-01-01)
Wang, Tsang-Yi
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Yang, Ming-Hsun
;
Wu, Jwo-Yuh
;
電機工程學系
;
Department of Electrical and Computer Engineering
Collaborative detection for continuously-varying event-region scenarios using wireless sensor networks (WSNs) has attracted much attention recently. However, most existing works adopt either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive data exchange takes place among the sensors by means of real-value data communications. By contrast, the present study proposes a distributed dynamic event-region detection scheme for WSNs. The proposed scheme is based on a two-phase cooperative space-time decision fusion protocol. In Phase I, each sensor makes an initial decision in accordance with its Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions in accordance with the decision information received from their neighboring nodes. The performance of the proposed scheme is compared with that of a semi-centralized detection scheme by means of computer simulations.
Channel-Aware Decision Fusion With Unknown Local Sensor Detection Probability
(2010-03-01)
Wu, Jwo-Yuh
;
Wu, Chan-Wei
;
Wang, Tsang-Yi
;
Lee, Ta-Sung
;
電機工程學系
Existing channel-aware decision fusion schemes assume that the local detection probability is known at the fusion center (FC). However, this paradigm ignores the possibility of unknown sensor alarm responses to the occurrence of events. Accordingly, this correspondence examines the binary decision fusion problem under the assumption that the local detection probability is unknown. Treating the communication links between the nodes and the FC as binary symmetric channels and assuming that the sensor nodes transmit simple one-bit reports to the FC, the global fusion rule is formulated initially in terms of the generalized likelihood ratio test (GLRT). Adopting the assumption of a high SNR regime, an approximate maximum likelihood (ML) estimate is derived for the unknown parameter required to implement the GLRT that is affine in the received data. The GLRT-based formulation is intuitively straightforward, but does not permit a tractable performance analysis. Therefore, motivated by the affine nature of the approximate ML solution, a simple alternative fusion rule is proposed in which the test statistic remains affine in the received data. It is shown that the proposed fusion rule facilitates the analytic characterization of the channel effect on the global detection performance. In addition, given a reasonable range of the local detection probability, it is shown that the global detection probability can be improved by reducing the total link error. Thus, a sensor power allocation scheme is proposed for enhancing the detection performance by improving the link quality. Simulation results show that: 1) the alternative fusion rule outperforms the GLRT; and 2) the detection performance of the fusion rule is further improved when the proposed power loading method is applied.
Collaborative spectrum sensing by combining energy and Cyclostationarity-based Detection schemes
(2013)
Wang, Tsang-Yi
;
Hsu, Ming-Fong
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Wu, Jwo-Yuh
;
Gao, Jian-Lang
;
電機工程學系
In this paper, the problem of spectrum sensing in cognitive radio communication networks is considered. This paper proposes a robust decision fusion scheme that can perform well when interference caused by other sources is present. Specifically, the proposed detection scheme is based on fusing the local decisions from energy detectors and cyclostationarity-based detectors. The proposed fusion scheme is different from other power spectrum-sensing technology developed so far in that other fusion technologies are based on fusing the local decisions from the same type of detectors instead of the different types of detectors as considered here. The proposed scheme was compared with schemes fusing the same type of detectors, and the results show that the proposed scheme is more robust against possible interference.
Cooperative Communications Using Reliability-Forwarding Relays
(2013-05-01)
Wang, Tsang-Yi
;
Wu, Jwo-Yuh
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
This study presents a new relay strategy and associated diversity combining technique for improving the communication performance in semi-blind cooperative networks. In constructing the network model, it is assumed that each relay can obtain the perfect channel state information (CSI) from the source to itself, and the destination can acquire the perfect CSI from all the relays to itself, but does not require the CSI from the source to the participating relays. In performing the considered semi-blind cooperative communications, the relays forward their reliability to the destination using a quantized reliability-relaying (QRR) scheme. Specifically, the relays partition their reliability into three levels in accordance with the log-likelihood ratio (LLR) value of the received signal, and forward a regenerative symbol to the destination if the quantized reliability falls within the "send + 1" or "send -1" region; and remain silent otherwise. It is shown theoretically that the QRR scheme achieves a higher deflection coefficient than the regular decode-and-forward (DF) scheme, in which all the participating relays forward their regenerative messages to the destination irrespective of their reliability. Moreover, the simulation results show that given the same semi-blind model, the QRR scheme achieves a lower bit error rate (BER) than existing relay selection DF schemes for all considered values of the average input signal-to-noise ratio (SNR).
Corrections on Cooperative Communications Using Reliability-Forwarding Relays (vol 61, pg 1776, 2013)
(2013-09-01)
Wang, Tsang-Yi
;
Wu, Jwo-Yuh
;
電機資訊學士班
;
Undergraduate Honors Program of Electrical Engineering and Computer Science
Design of Low-Overhead Cooperative Beamforming for Information Relaying in Wireless Sensor Networks Under Imperfect Quantized SNR of Source-to-Relay Links
(2013-03-01)
Wu, Jwo-Yuh
;
Hu, Chung-Hsuan
;
Wang, Tsang-Yi
;
Tsai, Shang-Ho
;
電機資訊學士班
We consider a wireless sensor network employing cooperative beamforming for information relaying. To reduce the signaling overhead, each relay node quantizes the SNR of the source-to-relay (S-R) link into one bit, which is then transmitted through a binary symmetric channel (BSC) with a known crossover probability to the destination. Given the set of the error-corrupted one-bit messages received at the destination, the beamforming design criterion is the maximization of the expected receive SNR, averaged over the conditional bit-flipping distributions of BSCs. We derive an analytic expression for the considered SNR metric, which is a complicated function of the beamforming weights. To facilitate analysis, we further derive a tractable lower bound for the conditional average SNR. By conducting maximization with respect to this lower bound, a closed-form sub-optimal beamformer can be obtained as a solution to a generalized eigenvalue problem. Computer simulations are used to illustrate the performance of the proposed scheme.
Distributed Detection of Dynamic Event Regions in Sensor Networks With a Gibbs Field Distribution and Gaussian Corrupted Measurements
(2016-09)
Wang, Tsang-Yi
;
Yang, Ming-Hsun
;
Wu, Jwo-Yuh
;
電機學院
;
College of Electrical and Computer Engineering
Various methods have been proposed for monitoring continuously varying event-region scenarios using wireless sensor networks (WSNs). However, these methods use either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive real-value data exchange takes place among the sensors. By contrast, this paper proposes a two-phase distributed dynamic event-region detection scheme for WSNs characterized by a space-time Markov random field with a particular Gibbs distribution. In Phase I of the proposed scheme, each sensor makes an initial decision in accordance with its current Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions based on the decision information received from their neighbors. Notably, the proposed scheme has a low-communication-rate characteristic, and is thus ideally suited to WSN applications. The performance of the proposed scheme is compared with that of a semicentralized detection scheme by means of computer simulations.
Does More Transmitting Sensors Always Mean Better Decision Fusion in Censoring Sensor Networks with an Unknown Size?
(2012-08-01)
Wang, Tsang-Yi
;
Wu, Jwo-Yuh
;
傳播研究所
;
電機資訊學士班
;
電機工程學系
This paper examines the impact of sensor censoring on the decision fusion performance in networks with an unknown number of sensors. In performing the decision fusion process, the fusion center applies the Chair-Varshney test; suitably modified to take account of the unknown network size. A closed-form analytical expression is derived for the error probability of the modified fusion rule. It is shown that reducing the censoring probability, i.e., allowing a greater number of sensors to transmit their decisions, does not necessarily improve the decision fusion performance. Rather, there exists a certain censoring probability threshold below which increasing the number of transmitting sensors simply incurs a greater intra-network communication overhead but without improving the global decision performance. Our findings establish that the design of energy-efficient local detection rules should commence with the censoring rate threshold. Hence, it is desirable that the value of this censoring probability threshold be known in advance. Accordingly, the present study proposes an efficient method for identifying the censoring probability threshold value and determining the corresponding local censoring rule.
Does More Transmitting Sensors Always Mean Better Decision Fusion in Censoring Sensor Networks with an Unknown Size?
(2012-08-01)
Wang, Tsang-Yi
;
Wu, Jwo-Yuh
;
電子工程學系及電子研究所
;
電機工程學系
;
電信工程研究所
This paper examines the impact of sensor censoring on the decision fusion performance in networks with an unknown number of sensors. In performing the decision fusion process, the fusion center applies the Chair-Varshney test; suitably modified to take account of the unknown network size. A closed-form analytical expression is derived for the error probability of the modified fusion rule. It is shown that reducing the censoring probability, i.e., allowing a greater number of sensors to transmit their decisions, does not necessarily improve the decision fusion performance. Rather, there exists a certain censoring probability threshold below which increasing the number of transmitting sensors simply incurs a greater intra-network communication overhead but without improving the global decision performance. Our findings establish that the design of energy-efficient local detection rules should commence with the censoring rate threshold. Hence, it is desirable that the value of this censoring probability threshold be known in advance. Accordingly, the present study proposes an efficient method for identifying the censoring probability threshold value and determining the corresponding local censoring rule.
Energy Detection Based Spectrum Sensing with Random Arrival and Departure of Primary User's Signal
(2013-01-01)
Wu, Jwo-Yuh
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Huang, Pei-Hsin
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Wang, Tsang-Yi
;
Wong, Vincent W. S.
;
電機工程學系
Energy detection (ED) is a popular spectrum sensing technique for cognitive radios. The study of ED which takes into account the dynamic of traffic patterns of primary users, in the form of random signal arrival and departure, is of both theoretical and practical importance. Some of the existing works, however, resort to certain approximation techniques to characterize the detection performance. In this paper, given a pair of arrival and departure time instants, we first derive an exact expression for the conditional detection probability. The exact mean detection probability is then obtained via an average operation over the random arrival and departure times. To improve the robustness of the detection performance against random signal arrival and departure, we further propose a Bayesian-based ED scheme. We present simulation results to validate our analytic study, and show the performance gain of our proposed Bayesian approach.
Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery
(2018-05-01)
Wu, Jwo-Yuh
;
Yang, Ming-Hsun
;
Wang, Tsang-Yi
;
電信工程研究所
;
Institute of Communications Engineering
To strike a balance between energy efficiency and data quality control, this paper proposes a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing-based wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor: 1) directly transmits the real-valued compressed data, if the sensing vector support is detected to be overlapped with the signal support; 2) sends a one-bit hard decision if empty support overlap is inferred; and 3) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. In addition, the average communication cost is analyzed. To aid global signal reconstruction under the proposed censoring framework, we propose a modified l(1)-minimization based algorithm, which exploits certain sparse nature of the hard decision vector received at the fusion center. Analytic performance guarantees, characterized in terms of the restricted isometry property, are also derived. Computer simulations are used to illustrate the performance of the proposed scheme.
Fault-tolerance analysis of a wireless sensor network with distributed classification codes
(2006)
Chen, Po-Ning
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Wang, Tsang-Yi
;
Han, Yunghsiang S.
;
Varshney, Pramod K.
;
Yao, Chien
In this work, we analyze the performance of a wireless sensor network with distributed classification codes, where independence across sensors, including local observations, local classifications and sensor-fusion link noises, is assumed. In terms of large deviations technique, we establish the necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity. With the necessary and sufficient condition and the upper performance bounds, the relation between the fault-tolerance capability of a distributed classification code and its pair-wise Hamming distances is characterized.
FURTHER RESULTS ON DECISION FUSION IN CENSORING SENSOR NETWORKS: AN UNKNOWN NETWORK SIZE
(2011)
Wang, Tsang-Yi
;
Wu, Jwo-Yuh
;
電機工程學系
;
Department of Electrical and Computer Engineering
Energy consumption is a vital concern when implementing distributed decision fusion in most wireless sensor networks. This paper studies the impact of sensor censoring on the decision fusion performance when the number of sensors is unknown at the fusion center. The global decision rule adopted at the fusion center is the Chair-Varshney fusion rule modified to take account of the unknown network size. It is shown that under the assumption of equally likely hypotheses, allowing more transmitting sensors does not necessarily yield better decision fusion; rather, there exists a censoring probability threshold below which the increase in the number of active sensors just incurs more intra-network communication overhead but will not improve the global decision performance. Our findings establish that the design of energy-efficient local detection rules should commence with the censoring rate threshold.
Fusion-Based Cooperative Support Identification for Compressive Networked Sensing
(2020-02-01)
Yang, Ming-Hsun
;
Wu, Jwo-Yuh
;
Wang, Tsang-Yi
;
Maunder, Robert G.
;
Gau, Rung-Hung
This letter proposes a fusion-based cooperative support identification scheme for distributed compressive sparse signal recovery via resource-constrained wireless sensor networks. The proposed support identification protocol involves: (i) local sparse sensing for economizing data gathering and storage, (ii) local binary decision making for partial support knowledge inference, (iii) binary information exchange among active nodes, and (iv) binary data aggregation for support estimation. Then, with the aid of the estimated signal support, a refined local decision is made at each node. Only the measurements of those informative nodes will be sent to the fusion center, which employs a weighted l(1)-minimization for global signal reconstruction. The design of a Bayesian local decision rule is discussed, and the average communication cost is analyzed. Computer simulations are used to illustrate the effectiveness of the proposed scheme.
Low-Latency Compressive Active User Identification over Frequency-Selective Fading Channels
(2018-01-01)
Chang, Chun-Yi
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Wu, Jwo-Yuh
;
Yang, Ming-Hsun
;
Wang, Tsang-Yi
;
Shao, Shuai
This paper proposes a compressive sensing (CS) based active user identification scheme over frequency-selective fading channels. Unlike the conventional cyclic prefix (CP) based preamble transmission, our approach does not utilize CP in order to conserve signaling overhead, in turn reducing the system-wide processing latency. Our approach first estimates the multi-user channel impulse response vectors by solving a mixed l(2 )/ l(1) - norm optimization problem; then, the active users are identified via a sorting of the norms of the estimated channel vectors. By exploiting the Toeplitz channel matrix structure resulting from CP-free preamble transmission, analytic performance guarantee in term of the block restricted isometry property of the preamble matrix is given. Computer simulations are used to illustrate the performance of the proposed method.
Low-Overhead Cooperative Beamforming Under Imperfect Quantized SNR of Source-to-Relay Links
(2012)
Wu, Jwo-Yuh
;
Hu, Chung-Hsuan
;
Wang, Tsang-Yi
;
電機工程學系
;
Department of Electrical and Computer Engineering
We consider a cooperative beamforming system with the amplify-and-forward protocol. To reduce the signaling overhead, each relay quantizes the SNR of the source-to-relay (S-R) link into one bit, which is then transmitted through a binary symmetric channel (BSC) with a known crossover probability to the destination. Given the set of the error-corrupted one-bit messages received at the destination, the beamforming design criterion is the maximization of the expected receive SNR, averaged over the conditional bit-flipping distributions of BSCs. We derive an analytic expression for the considered SNR metric, which is a complicated function of the beamforming weights. To facilitate analysis, we further derive a tractable lower bound for the conditional average SNR. By conducting maximization with respect to this lower bound, a closed-form sub-optimal beamformer can be obtained as a solution to a generalized eigenvalue problem. Computer simulations are used to illustrate the performance of the proposed scheme.
Malicious Crowdsourcing Worker Detection using Privacy-Aware Group Queries
(2019-01-01)
Yang, Ming-Hsun
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Hong, Y. -W. Peter
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Wang, Tsang-Yi
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Wu, Jwo-Yuh
;
電機工程學系
This work proposes efficient methods for the detection of malicious crowdsourcing workers using only privacy-aware group queries. In the proposed system, the crowdsourcing platform first issues a series of standard tasks to the workers, and allows users (i.e., data owners) to access aggregate responses from the workers through group queries that can be described by sparse encoding vectors. The identities of workers associated with individual responses are not explicitly revealed. By exploiting the sparse nature of the encoding vectors, we first propose an approximate maximum a posteriori probability (Approx. MAP) detector to perform the detection. Then, to further reduce computational complexity, we devise a generalized likelihood ratio test (GLRT) where probable malicious workers are first identified before a simple hypothesis test is performed. The identification of malicious workers is performed by a low-complexity probability-based rule that exploits a certain sparse structure inherent in the crowd data as well as the associated statistical assumptions. Computer simulations show that the proposed methods outperform the conventional energy detector.
On the design of soft-decision fusion rule for coding approach in wireless sensor networks
(2006)
Wang, Tsang-Yi
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
Wang, Yung-Ti
;
電信工程研究所
In this work, two soft-decision fusion rules, which are respectively named the maximum a priori (MAP) and the suboptimal minimum Euclidean distance (MED) fusion rules, are designed based on a given employed sensor code and associated local classification. Their performance comparison with the distributed classification fusion using soft-decision decoding (DCSD) proposed in an earlier work is also performed. Simulations show that when the number of faulty sensors is small, the MAP fusion rule remains the best at either low sensor observation signal-to-noise ratios (OSNRs) or low communication channel signal-to-noise ratios (CSNRs), and yet, the DCSD fusion rule gives the best performance at middle to high OSNRs and high CSNRs. However, when the number of faulty sensor nodes grows large, the least complex MED fusion rule outperforms the MAP fusion rule at high OSNRs and high CSNRs.