Browsing by Author "Liu, Chuan-Ming"

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A concurrent transmission MAC protocol for enhancing throughout and avoiding spectrum sensing in cognitive radio
(2007)
Wang, Li-Chun
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Wang, Chung-Wei
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Lu, Yin-Chih
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Liu, Chuan-Ming
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交大名義發表
For the cognitive radio (CR) network, a fundamental issue is how to identify the spectrum opportunities. First, each CR node determines whether there exists transmission opportunities on unlicensed bands. If not, this node will find other opportunity on licensed bands. Thus, increasing the opportunities of concurrent transmissions in unlicensed bands can reduce the overhead of wide-band sensing. In this paper, based on the carrier sensing multiple access protocol, we propose a novel concurrent transmissions MAC (CT-MAC) protocol to identify the possibility of establishing the second link in the presence of the first link in the unlicensed band. In addition to reducing the overhead of wide-band sensing, the proposed CT-MAC scheme can enhance overall throughput and is backward compatible with the IEEE 802.11 standard.
A mobility-aware approach for distributed data update on unstructured mobile P2P networks
(2019-01-01)
Lai, Chuan-Chi
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Liu, Chuan-Ming
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
In unstructured mobile peer-to-peer systems (MP2P), the frequent link breakages lead frequent topology mismatching problems and data transmission failures due to high mobility of nodes. The overhead of data transmission and synchronization cannot be neglected. In order to keep the data consistent, flooding is a fundamental and straightforward data synchronization mechanism since a mobile node does not know which else has the same shared data item. However, data flooding causes the broadcast storm problem. In this paper, we propose a mobility-aware data update approach (MADU) to improve the data dissemination and to reduce the overhead of maintaining the consistency of shared data items in an MP2P network. We use safe-time which is derived from the neighbor's location and speed to determine the time for a node to do the checking and updating between the neighbor nodes and itself. We also consider the network connectivity of a mobile node and access frequency of a data item as the factors to trigger the update process. By combining the mobility information of nodes, network connectivity, and access frequency of data items, we set a reasonable data update mechanism, which can significantly decrease the number of retransmissions and redundant messages so as to reduce the overhead of maintaining the data consistency. (C) 2018 Elsevier Inc. All rights reserved.
A Vision-Based Driver Nighttime Assistance and Surveillance System Based on Intelligent Image Sensing Techniques and a Heterogamous Dual-Core Embedded System Architecture
(2012-03-01)
Chen, Yen-Lin
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Chiang, Hsin-Han
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Chiang, Chuan-Yen
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Liu, Chuan-Ming
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Yuan, Shyan-Ming
This study proposes a vision-based intelligent nighttime driver assistance and surveillance system (VIDASS system) implemented by a set of embedded software components and modules, and integrates these modules to accomplish a component-based system framework on an embedded heterogamous dual-core platform. Therefore, this study develops and implements computer vision and sensing techniques of nighttime vehicle detection, collision warning determination, and traffic event recording. The proposed system processes the road-scene frames in front of the host car captured from CCD sensors mounted on the host vehicle. These vision-based sensing and processing technologies are integrated and implemented on an ARM-DSP heterogamous dual-core embedded platform. Peripheral devices, including image grabbing devices, communication modules, and other in-vehicle control devices, are also integrated to form an in-vehicle-embedded vision-based nighttime driver assistance and surveillance system.
An Effective Method for Top-k Dominating Query Processing over Multiple Uncertain Data Streams
(2018-01-01)
Liu, Chuan-Ming
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Wang, Tien-Chun
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Lai, Chuan-Chi
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Wang, Li-Chun
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電機工程學系
This paper explores the top-k dominating query process on multiple uncertain data streams and employs the parallel computation to facilitate the query process. The challenges include how to quickly update the result and reduce the computation cost for processing uncertainty. By referring the related existing papers for certain data, we provide an effective top-k dominating query process on uncertain data streams in terms of time and space and the provided approach can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through extensive simulated experiments. The experimental results indicate that our algorithms can avoid the unnecessary computation effectively and reduce lots of communication throughput between servers, thus achieving the objective of updating the results quickly.
An Intelligent Knowledge-Based and Customizable Home Care System Framework with Ubiquitous Patient Monitoring and Alerting Techniques
(2012-08-01)
Chen, Yen-Lin
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Chiang, Hsin-Han
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Yu, Chao-Wei
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Chiang, Chuan-Yen
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Liu, Chuan-Ming
This study develops and integrates an efficient knowledge-based system and a component-based framework to design an intelligent and flexible home health care system. The proposed knowledge-based system integrates an efficient rule-based reasoning model and flexible knowledge rules for determining efficiently and rapidly the necessary physiological and medication treatment procedures based on software modules, video camera sensors, communication devices, and physiological sensor information. This knowledge-based system offers high flexibility for improving and extending the system further to meet the monitoring demands of new patient and caregiver health care by updating the knowledge rules in the inference mechanism. All of the proposed functional components in this study are reusable, configurable, and extensible for system developers. Based on the experimental results, the proposed intelligent homecare system demonstrates that it can accomplish the extensible, customizable, and configurable demands of the ubiquitous healthcare systems to meet the different demands of patients and caregivers under various rehabilitation and nursing conditions.
Distributed clustering algorithms for data-gathering in wireless mobile sensor networks
(2007-11-01)
Liu, Chuan-Ming
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Lee, Chuan-Hsiu
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Wang, Li-Chun
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交大名義發表
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National Chiao Tung University
One critical issue in wireless sensor networks is how to gather sensed information in an energy-efficient way since the energy is a scarce resource in a sensor node. Cluster-based architecture is an effective architecture for data-gathering in wireless sensor networks. However, in a mobile environment, the dynamic topology poses the challenge to design an energy-efficient data-gathering protocol. In this paper, we consider the cluster-based architecture and provide distributed clustering algorithms for mobile sensor nodes which minimize the energy dissipation for data-gathering in a wireless mobile sensor network. There are two steps in the clustering algorithm: cluster-head election step and cluster formation step. We first propose two distributed algorithms for cluster-head election. Then, by considering the impact of node mobility, we provide a mechanism to have a sensor node select a proper cluster-head to join for cluster formation. Our clustering algorithms will achieve the following three objectives: (1) there is at least one cluster-head elected, (2) the number of cluster-heads generated is uniform, and (3) all the generated clusters have the same cluster size. Last, we validate our algorithms through an extensive experimental analysis with Random Walk Mobility (RWM) model, Random Direction Mobility (RDM) model, and a Simple Mobility (SM) model as well as present our findings. (C) 2007 Elsevier Inc. All rights reserved.
Distributed Continuous Range-Skyline Query Monitoring Over the Internet of Mobile Things
(2019-08-01)
Lai, Chuan-Chi
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Akbar, Zulhaydar Fairozal
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Liu, Chuan-Ming
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Ta, Van-Dai
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Wang, Li-Chun
A range-skyline query (RSQ) is the combination of range query and skyline query. It is one of the practical query types in multicriteria decision services, which may include the spatial and nonspatial information as well as make the resulting information more useful than skyline search when the location is concerned. Furthermore, continuous RSQ (CRSQ) is an extension of RSQ that the system continuously reports the skyline results to a query within a given search range. This paper focuses on the RSQ and CRSQ within a specific range on Internet of Mobile Things (IoMT) applications. Many server-client approaches for CRSQ have been proposed but are sensitive to the number of moving objects. We propose an effective and noncentralized approach, distributed CRSQ process (DCRSQ process), for supporting RSQ and CRSQ in mobile environments. By considering the mobility, the proposed approach can predict the time when an object falls in the query range and ignore more irrelevant information when deriving the results, thus saving the computation overhead. The proposed approach, DCRSQ process, is analyzed on cost and validated with extensive simulated experiments. The results show that DCRSQ process outperforms the existing approaches in different scenarios and aspects.
Optimal Number of Clusters in Dense Wireless Sensor Networks: A Cross-Layer Approach
(2009-02-01)
Wang, Li-Chun
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Wang, Chung-Wei
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Liu, Chuan-Ming
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電信工程研究所
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Institute of Communications Engineering
Cluster-based sensor networks have the advantages of reducing energy consumption and link-maintenance cost. One fundamental issue in cluster-based sensor networks is determining the optimal number of clusters. In this paper, we suggest a physical (PRY)/medium access control (MAC)/network (NET) cross-layer analytical approach for determining the optimal number of clusters, with the objective of minimizing energy consumption in a high-density sensor network. Our cross-layer design can incorporate many effects, including lognormal shadowing and a two-slope path loss model in the PRY layer, various MAC scheduling, and multihop routing schemes. Compared with the base-line case with one cluster per observation area (OA), a sensor network with the proposed optimal number of clusters can reduce the energy consumption by more than 80% in some cases. We also verify by simulations that the analytical optimal cluster number can still effectively function, regardless of the different densities of sensors in various OAs.
Probabilistic Reverse Nearest Neighbors on Uncertain Data Streams
(2018-01-01)
Feng, Lin-Ru
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Liu, Chuan-Ming
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Lai, Chuan-Chi
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電機工程學系
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Department of Electrical and Computer Engineering
hi these days, many applications of IoT, Big Data, and Cloud computing have been proposed and discussed. One of the common important issues in these applications is data processing. The data to be considered now have the properties of velocity, veracity, and volume. Uncertain data streams simultaneously have these properties. Reverse nearest neighbors (RNN) query finds the objects that have the query as the nearest object and plays an important in many applications. We thus study probabilistic RNN (PRNN) query over the uncertain data streams in this paper. Due to the uncertainty. the computation overhead for updates gets much higher and how to reduce the computation cost becomes challenging. We provide efficient PRNN query processes over uncertain data streams in terms of time. Our methods can effectively prune the irrelevant data objects and keep only the possible ones for further updates. thus saving time. The proposed methods are also validated through extensive simulation experiments.
Probabilistic Top-k Dominating Query Monitoring Over Multiple Uncertain IoT Data Streams in Edge Computing Environments
(2019-10-01)
Lai, Chuan-Chi
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Wang, Tien-Chun
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Liu, Chuan-Ming
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Wang, Li-Chun
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電機工程學系
Extracting the valuable features and information in big data has become one of the important research issues in data science. In most Internet of Things (IoT) applications, the collected data are uncertain and imprecise due to sensor device variations or transmission errors. In addition, the sensing data may change as time evolves. We refer an uncertain data stream as a dataset that has velocity, veracity, and volume properties simultaneously. This paper employs the parallelism in edge computing environments to facilitate the top-k dominating query process over multiple uncertain IoT data streams. The challenges of this problem include how to quickly update the result for processing uncertainty and reduce the computation cost as well as provide highly accurate results. By referring to the related existing papers for certain data, we provide an effective probabilistic top-k dominating query process on uncertain data streams, which can be parallelized easily. After discussing the properties of the proposed approach, we validate our methods through the complexity analysis and extensive simulated experiments. In comparison with the existing works, the experimental results indicate that our method can improve almost 60% computation time, reduce nearly 20% communication cost between servers, and provide highly accurate results in most scenarios.