Browsing by Author "Lai, Chuan-Chi"

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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.
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.
Communications and Networking Technologies for Intelligent Drone Cruisers
(2019-01-01)
Wang, Li-Chun
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Lai, Chuan-Chi
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Shuai, Hong-Han
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Lin, Hsin-Piao
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Li, Chi-Yu
Future mobile communication networks require an Aerial Base Station (ABS) with fast mobility and long-term hovering capabilities. At present, unmanned aerial vehicles (UAV) or drones do not have long flight times and are mainly used for monitoring, surveillance, and image post-processing. On the other hand, the traditional airship is too large and not easy to take off and land. Therefore, we propose to develop an "Artificial Intelligence (AI) Drone-Cruiser" base station that can help 5G mobile communication systems and beyond quickly recover the network after a disaster and handle the instant communications by the flash crowd. The drone-cruiser base station can overcome the communications problem for three types of flash crowds, such as in stadiums, parades, and large plaza so that an appropriate number of aerial base stations can be accurately deployed to meet large and dynamic traffic demands. Artificial intelligence can solve these problems by analyzing the collected data, and then adjust the system parameters in the framework of Self-Organizing Network (SON) to achieve the goals of self-configuration, self-optimization, and self-healing. With the help of AI technologies, 5G networks can become more intelligent. This paper aims to provide a new type of service, On-Demand Aerial Base Station as a Service. This work needs to overcome the following live technical challenges: innovative design of drone-cruisers for the long-time hovering, crowd estimation and prediction, rapid 3D wireless channel learning and modeling, 3D placement of aerial base stations and the integration of WiFi front-haul and millimeter wave/WiGig back-haul networks.
Data-Driven 3D Placement of UAV Base Stations for Arbitrarily Distributed Crowds
(2019-01-01)
Lai, Chuan-Chi
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Wang, Li-Chun
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Han, Zhu
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, we consider an Unmanned Aerial Vehicle (UAV)-assisted cellular system which consists of multiple UAV base stations (BSs) cooperating the terrestrial BSs. In such a heterogeneous network, for cellular operators, the problem is how to determine the appropriate number, locations, and altitudes of UAV-BSs to improve the system sumrate as well as satisfy the demands of arbitrarily flash crowds on data rates. We propose a data-driven 3D placement of UAV-BSs for providing an effective placement result with a feasible computational cost. The proposed algorithm searches for the appropriate number, location, coverage, and altitude of each UAV-BS in the serving area with the maximized system sumrate in polynomial time so as to guarantee the minimum data rate requirement of UE. The simulation results show that the proposed approach can improve system sumrate in comparison with the case without UAV-BSs.
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.
On-Demand Density-Aware UAV Base Station 3D Placement for Arbitrarily Distributed Users With Guaranteed Data Rates
(2019-06-01)
Lai, Chuan-Chi
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Chen, Chun-Ting
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Wang, Li-Chun
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電機工程學系
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Department of Electrical and Computer Engineering
In this letter, we study the on-demand unmanned aerial vehicle mounted base stations (UAV-BS) placement problem for arbitrarily distributed users. This UAV-BS placement problem is modeled as a knapsack-like problem, which is NP-complete. We propose a density-aware placement algorithm to maximize the number of covered users subject to the constraint of the minimum required data rates per user. Simulations are conducted to evaluate the performance of the proposed algorithm in a real environment with different user densities. Our numerical results indicate that for various user densities our proposed solution can service more users with guaranteed data rates compared to the existing method, while reducing the transmit power by 29%.
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.