Browsing by Author "Wei, Chun-Yi"

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Distributed Clustering and Spectrum-Based Proximity Device Discovery in a Wireless Network
(2016)
Chang, Tofar C. -Y.
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Wei, Chun-Yi
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Hsu, Min-Chien
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Lin, Chih-Yu
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Su, Yu T.
Direct short-range device-to-device (D2D) conununication enjoys many advantages, such as high transmission rates, low propagation delays, low transmit power and interference. In order to realize the advantages, a device or user equipment participating in the D2D communications have to determine that the devices discovered are in a proximity area and are suitable to establish links with them. Hence, an accurate and efficient clustering scheme, which can organize the devices nearby into multiple D2D communication groups is necessary. Against this background, in this paper we propose a self-clustering scheme which requires no assistance from Cellular Network. The philosophy of our design is to group the devices with similar sensed spectrum profiles. In order to facilitate this self-clustering scheme, we adopt Reed-Solomon (RS) code into our proposed design. More specifically, in our design, a D2D communication group is formed by the D2D devices whose sensed spectrum vectors are mapping to the same valid RS codeword which is broadcast by the form of frequency-hopping (141) pattern. Additionally, the collision event could happen when multiple D2D devices transmit signals to discovery their group members. Hence, we design the FH probing sequence based on a randomly cyclic-shifted non-binary RS codeword to eliminate the possible collision events and interference exited in clustering procedure. In this paper, we provide our detail sequence design and the self-clustering procedure. In the simulation results, we show that the proposed self-clustering is efficient and effective.
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
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Chen, Po-Ning
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Han, Yunghsiang S.
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Varshney, Pramod K.
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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
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Lin, Hsuan-Yin
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Chen, Po-Ning
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Han, Yunghsiang S.
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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.