Browsing by Author "Tseng, Yu-Ghee"

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Optimal coloring for data collection in tree-based wireless sensor networks
(2017-11-14)
Lo, Shih-Ming
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Lin, Wu-Hsiung
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Chen, Chiuyuan
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Tseng, Yu-Ghee
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應用數學系
Data collection is an important operation in a wireless sensor network (WSN). During data collection, the interference among nodes cannot be ignored. In a multi-hop WSN, one conventional way of defining interference neighbors is to prohibit a node from using the same time slot as those of its 1-hop and 2-hop neighbors. Recently, it is proved that for data collection in a WSN, since the set of communication nodes is limited and the transmission directions are toward the sink, a less strict set of interference neighbors can be defined [16]. The interference problem in a duty-cycle WSN (DC-WSN) with a corona structure is studied in [7]. In this paper, we solve the same problem by using the relaxed interference set defined in [16]. In particular, we give a complete classification of non-interference sets in 2-hop neighbors. We also propose a distributed 6-coloring algorithm. We prove a lower bound of six colors that every tree-based data collection algorithm requires in a dense DC-WSN, which proves our algorithm to be optimal. (C) 2017 Elsevier B.V. All rights reserved.
Who Takes What: Using RGB-D Camera and Inertial Sensor for Unmanned Monitor
(2019-01-01)
Kao, Hsin-Wei
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Ke, Ting-Yuan
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Lin, Kate Ching-Ju
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Tseng, Yu-Ghee
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資訊工程學系
Advanced Internet of Things (IoT) techniques have made human-environment interaction much easier. Existing solutions usually enable such interactions without knowing the identities of action performers. However, identifying users who are interacting with environments is a key to enable personalized service. To provide such add-on service, we propose WTW (who takes what), a system that identifies which user takes what object. Unlike traditional vision-based approaches, which are typically vulnerable to blockage, our WTW combines the feature information of three types of data, i.e., images, skeletons and IMU data, to enable reliable user-object matching and identification. By correlating the moving trajectory of a user monitored by inertial sensors with the movement of an object recorded in the video, our WTW reliably identifies a user and matches him/her with the object on action. Our prototype evaluation shows that WTW achieves a recognition rate of over 90% even in a crowd. The system is reliable even when users locate close by and take objects roughly at the same time.