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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.
Minimum Byzantine Effort for Blinding Distributed Detection in Wireless Sensor Networks
(2020-01-01)
Lin, Hsuan-Yin
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
Varshney, Pramod K.
;
電信工程研究所
In this work, we consider the general problem of distributed detection in the presence of Byzantines using wireless sensor networks. Instead of attempting to mitigate Byzantine attacks as a system designer, we investigate the issue from the perspective of a Byzantine attacker. The probability for each individual sensor to be compromised (compromising probability) required to blind the system operation is adopted as the attack measure. Under the system setting that the fusion center (FC) declares the most likely hypothesis to be true based on the local sensors, a Byzantine attack policy that can blind the FC with the minimum compromising probability for each individual sensor is derived under the assumption that the Byzantine attacker knows the statistics of the local outputs. The closed-form expression for a blind-achieving Byzantine transition probability that is used to alter the statistics of the local outputs of compromised sensors is also established. Our results indicate that the statistics of the local outputs is essential for the minimization of an attacker's effort.
COLLABORATIVE SENSOR CACHING VIA SEQUENTIAL COMPRESSED SENSING
(2019-01-01)
Yang, Yi-Jen
;
Yang, Ming-Hsun
;
Hong, Y-W Peter
;
Wu, Jwo-Yuh
;
電信工程研究所
This work proposes a collaborative sensor caching and data reconstruction method based on the sequential compressed sensing framework. Here, multiple caches are assumed to exist in the wireless sensor network to store the most recent data gathered from sensors within their respective coverage areas. To reduce the cache size and the data-acquisition overhead, each cache accesses measurements only from a small subset of sensors. This work proposes a collaborative sparse-signal reconstruction method that exploits the presence of sensors simultaneously accessible by multiple caches as anchor nodes to introduce dependency in the reconstruction. The reconstruction is based on the use of the alternating direction method of multipliers (ADMM), which enables distributed implementation of the algorithm. Simulations are provided to demonstrate the effectiveness of the proposed scheme.
A Path-Connected-Cluster Wireless Sensor Network and Its Formation, Addressing, and Routing Protocols
(2012-06-01)
Tsai, Chia-Hung
;
Tseng, Yu-Chee
;
資訊工程學系
;
Department of Computer Science
Although wireless sensor networks (WSNs) have been extensively researched, their deployment is still a main concern. We observe that many monitoring applications for WSNs have adopted a path-connected-cluster (PCC) topology, where regions to be monitored are deployed with clusters of sensor nodes. Since these clusters might be physically separated, paths of sensor nodes are used to connect them together. We call such networks PCC-WSNs. PCC-WSNs may be widely applied in real situations, such as bridge-connected islands, street-connected buildings, and pipe-connected ponds. In this paper, we show that the address assignment scheme defined by ZigBee will perform poorly in terms of address utilization. We then propose a systematical solution, which includes network formation, automatic address assignment, and light-weight routing. Simulation results verify the effectiveness of the proposed solution.
Local Threshold Design for Target Localization Using Error Correcting Codes in Wireless Sensor Networks in the Presence of Byzantine Attacks
(2017-07-01)
Wei, Chun-Yi
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
Varshney, Pramod K.
;
電信工程研究所
In this paper, we revisit the received signal strength (RSS)-based target localization technique presented in Vempaty et al., where a simple threshold quantizer was employed to quantize the RSS values prior to sending them to the fusion center. It was shown that the probability of misclassification of the distributed classification fusion using error correcting codes scheme vanishes as the number of sensors tends to infinity. This result was obtained based on an intuitive threshold design at the local sensors, and the question of how much a careful design of local thresholds can help improve the overall performance was not addressed. In this paper, we demonstrate the significance of threshold design for accurate and robust target localization in wireless sensor networks, particularly, when the number of sensors is finite. With this objective, we derive an upper bound on the probability of misclassification as a function of RSS thresholds by using the union inequality. The RSS thresholds that algorithmically minimize the derived misclassification error bound are then numerically obtained over a mirror-based homomorphic sensor deployment structure. Simulations over fading wireless links show that the scheme based on newly found optimized RSS thresholds considerably outperforms the previous scheme using the thresholds that are intuitively selected, especially in the presence of Byzantine attacks that severely impact information security.
Target Localization Using Sensor Location Knowledge in Wireless Sensor Networks
(2018-06-01)
Wei, Chun-Yi
;
Lin, Hsuan-Yin
;
Chen, Po-Ning
;
Han, Yunghsiang S.
;
Varshney, Pramod K.
Incorporating error correcting coding techniques into target localization provides an immediate advantage that existing decoding algorithms can he used to determine which area the target is most likely located in. The important knowledge of exact sensor positions is, however, ignored in these decoding algorithms. This letter revisits the problem and shows that based on the weighted average of sensor positions with binary weightings from local decisions, a newly proposed decoding criterion can achieve a much better accuracy in target localization than the soft- and hard-decision rules particularly when a certain number of sensors are under Byzantine attacks.
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.
Amplitude-Aided 1-Bit Compressive Sensing Over Noisy Wireless Sensor Networks
(2015-10)
Chen, Ching-Hsien
;
Wu, Jwo-Yuh
;
電機學院
;
College of Electrical and Computer Engineering
One-bit compressive sensing (CS) is known to be particularly suited for resource-constrained wireless sensor networks (WSNs). In this letter, we consider 1-bit CS over noisy WSNs subject to channel-induced bit flipping errors, and propose an amplitude-aided signal reconstruction scheme, by which 1) the representation points of local binary quantizers are designed to minimize the loss of data fidelity caused by local sensing noise, quantization, and bit sign flipping, and 2) the fusion center adopts the conventional l(1)-minimization method for sparse signal recovery using the decoded and de-mapped binary data. The representation points of binary quantizers are designed by minimizing the mean square error (MSE) of the net data mismatch, taking into account the distributions of the nonzero signal entries, local sensing noise, quantization error, and bit flipping; a simple closed-form solution is then obtained. Numerical simulations show that our method improves the estimation accuracy when SNR is low or the number of sensors is small, as compared to state-of-the-art 1-bit CS algorithms relying solely on the sign message for signal recovery.
Range-Free Mobile Actor Relocation in a Two-Tiered Wireless Sensor and Actor Network
(2016-05)
Wu, Fang-Jing
;
Hsu, Hsiu-Chi
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Shen, Chien-Chung
;
Tseng, Yu-Chee
;
資訊工程學系
Two-tiered wireless sensor and actor networks (WSANs) have been proposed to enhance network capabilities, where a set of resource-rich mobile nodes (termed actors) form a connected backbone to relay sensing data from static sensors to the sink and sometimes are requested by sensors to perform a particular action. Such a two-tiered WSAN facilitates scalability and can efficiently reduce the energy consumption incurred by conventional hop-by-hop relaying via only sensors. However, relocating actors to achieve both connectivity and load balance is a challenge, especially when there is no location information of the nodes. Connectivity ensures that the actors are connected, whereas load balance ensures that actors collect and originate a similar amount of sensory data from the sensors. In this article, we formulate the Connected and Balanced Mobile Actor Relocation (CBMAR) optimization problem to address both connectivity and load balance and prove that the problem is NP-hard. We thus propose a dual-mode distributed actor relocation protocol that does not rely on any location information of nodes to relocate actors. The idea is to locally form virtual Voronoi cells of actors (termed covering cells) based on the lower-tiered topology, where each actor locally recruits its own sensor members to form its own covering cell. By maintaining the covering cell, each actor locally relocates itself toward a sensor along the lower-tiered topology. Extensive simulation results show that the protocol can achieve both objectives of connectivity and load balance with low moving and communication overheads.
An energy-efficient two phases cluster head selection in corona-based wireless sensor networks
(2015-01-01)
Fan, Chung-Shuo
;
資訊工程學系
;
Department of Computer Science
Owing to unbalanced traffic pattern of wireless sensor networks (WSNs), larger load is imposed to cluster heads (CHs) around the sink. Under this situation, CHs closer to the BS drain their energy earlier than those farther away from the sink. This paper first formulates cluster density of each corona according to the energy consumption of each CH. Then, this paper defines the range of CH candidates in phase 1 and 2 to choose the suitable CH so that sensor nodes forward data towards the BS and reduce the energy consumption. This paper further adopts cluster maintenance, which implement the cross-level data transmissions when specific conditions happen. Simulation results show that the proposed TPS (Two Phases cluster head Selection in WSNs) effectively prolongs the network lifetime and reduces the energy consumption over low energy adaptive clustering hierarchy (LEACH) and multi-hop routing with LEACH (MR-LEACH).

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