This paper introduces a Simulink-based online algorithm design Interface for a Web-based control laboratory called NCSLab, which provides an online access to experimental devices in the physical laboratory. Since the ...
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ISBN:
(纸本)9789881563903
This paper introduces a Simulink-based online algorithm design Interface for a Web-based control laboratory called NCSLab, which provides an online access to experimental devices in the physical laboratory. Since the development of Information and communication technologies, online control laboratories are able to possess more convenience and possibility. In order to provide users with a more comprehensive and comfortable environment of remote experiment, the functionality of online algorithm design is supposed to be upgraded with the latest techniques. Users are able to design control algorithms with a graphical interactive interface as they do in MATLAB/Simulink in NCSLab, which will be illustrated with examples in this paper.
This paper introduces a Simulink-based online algorithm design Interface for a Web-based control laboratory called NCSLab, which provides an online access to experimental devices in the physical laboratory. Since the ...
详细信息
This paper introduces a Simulink-based online algorithm design Interface for a Web-based control laboratory called NCSLab, which provides an online access to experimental devices in the physical laboratory. Since the development of Information and communication technologies, online control laboratories are able to possess more convenience and possibility. In order to provide users with a more comprehensive and comfortable environment of remote experiment, the functionality of online algorithm design is supposed to be upgraded with the latest techniques. Users are able to design control algorithms with a graphical interactive interface as they do in MATLAB/Simulink in NCSLab, which will be illustrated with examples in this paper.
The device-to-device load balancing (D2D-LB) paradigm has been advocated in recent small-cell architecture design for cellular networks. The idea is to exploit inter-cell D2D communication and dynamically relay traffi...
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The device-to-device load balancing (D2D-LB) paradigm has been advocated in recent small-cell architecture design for cellular networks. The idea is to exploit inter-cell D2D communication and dynamically relay traffic of a busy cell to adjacent under-utilized cells to improve spectrum temporal efficiency, addressing a fundamental drawback of small-cell architecture. Technical challenges of D2D-LB have been studied in previous works. The potential of D2D-LB, however, cannot be fully realized without providing proper incentive mechanism for device participation. In this paper, we address this economical challenge using an online procurement auction framework. In our design, multiple sellers (devices) submit bids to participate in D2D-LB and the auctioneer (cellular service provider) evaluates all the bids and decides to purchase a subset of them to fulfill load balancing requirement with the minimum social cost. Different from similar auction design studies for cellular offloading, battery limit of relaying devices imposes a time-coupled capacity constraint that turns the underlying problem into a challenging multi-slot one. Furthermore, the dynamics in the input to the multi-slot auction problem emphasize the need for online algorithm design. We first tackle the single-slot version of the problem, show that it is NP-hard, and design a polynomial-time offline algorithm with a small approximation ratio. Building upon the single-slot results, we design an onlinealgorithm for the multi-slot problem with sound competitive ratio. Our auction algorithmdesign ensures that truthful bidding is a dominant strategy for devices. Extensive experiments using real-world traces demonstrate that our proposed solution achieves near offline-optimum and reduces the cost by 45% compared with an alternative heuristic.
Traffic congestion and car pollution are becoming serious plagues nowadays. High travel cost brings a great burden to people and society. A ridesharing system mitigates traffic congestion and car pollution by allowing...
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Traffic congestion and car pollution are becoming serious plagues nowadays. High travel cost brings a great burden to people and society. A ridesharing system mitigates traffic congestion and car pollution by allowing passengers to share their travel costs with others. Traditional ridesharing platforms usually require passengers to submit their plans in advance and then design schedules for the drivers who would like to offer a ride. Nowadays, with the development of the smartphone technology, dynamic ridesharing systems enable passengers request a car anytime and anywhere. This paper mainly considers the problems of how to allocate passengers to drivers, how to charge the passengers and how to design feasible schedules for the driver in such online environment. The allocation problem is modeled as an online weighted matching problem with the graph changing over time. Firstly, we give a fair pricing method which is easy to be understood and accepted by the passengers. We develop a greedy algorithm called LioMAx_GRE for the purpose of maximizing liquidity and an algorithm called UTIMAX for the purpose of maximizing utility. LIQMAx_GRE achieves a competitive ratio of 1/lambda+1, where lambda is the maximal number of passengers a car can take. In general, we prove that no onlinealgorithm can have a good guarantee for the design goal of maximizing utility. Innovatively, UTIMAX considers not only the current maximal utility, but also the opportunity cost, which is the utility contributed by occupying a seat. It reflects the utility in the future and thus can be used to handle the difficulty in onlinedesign. We prove that, our algorithm has a competitive ratio of 1/3 in a special case. The schedule problem is NP-hard and we design a heuristic nearest neighbor algorithm to solve it. (C) 2019 Published by Elsevier B.V.
As a rising star of social apps, short video apps, e.g., TikTok, have attracted a large number of mobile users by providing fresh and short video contents that highly match their watching preferences. Meanwhile, the b...
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As a rising star of social apps, short video apps, e.g., TikTok, have attracted a large number of mobile users by providing fresh and short video contents that highly match their watching preferences. Meanwhile, the booming growth of short video apps imposes new technical challenges on the existing computation and communication infrastructure. Traditional solutions maintain all videos on the cloud and stream them to users via contend delivery networks or the Internet. However, they incur huge network traffic and long delay that seriously affects users' watching experiences. In this article, we propose an edge-assisted short video sharing framework to address these challenges by caching some highly preferred videos at edge servers that can be accessed by users via high-speed network connections. Since edge servers have limited computation and storage resources, we design an onlinealgorithm with provable approximation ratio to decide which videos should be cached at edge servers, without the knowledge of future network quality and watching preferences changes. Furthermore, we improve the performance by jointly considering video fetching and user-edge association. Extensive simulations are conducted to evaluate the proposed algorithms under various system settings, and the results show that our proposals outperform existing schemes.
Today's Internet must support applications with increasingly dynamic and heterogeneous connectivity requirements, such as video streaming and the Internet of Things. Yet current network management practices genera...
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ISBN:
(纸本)9781728127002
Today's Internet must support applications with increasingly dynamic and heterogeneous connectivity requirements, such as video streaming and the Internet of Things. Yet current network management practices generally rely on pre-specified flow configurations, which cannot cover all possible scenarios. In this work, we instead propose a model-free learning approach to automatically optimize the policies for heterogeneous network flows. This approach is attractive as no existing comprehensive models quantify how different policy choices affect flow performance under dynamically changing network conditions. We extend multi-armed bandit frameworks to propose new online learning algorithms for protocol selection, addressing the challenge of policy configurations affecting the performance of multiple flows sharing the same network resources. This performance coupling limits the scalability and optimality of existing online learning algorithms. We theoretically prove that our algorithm achieves a sublinear regret and demonstrate its optimality and scalability through data-driven simulations.
Condition monitoring is of great practical significance in modern manufacturing. This paper presents the implementation of an edge computing-based unified condition monitoring system, which provides all-round services...
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Condition monitoring is of great practical significance in modern manufacturing. This paper presents the implementation of an edge computing-based unified condition monitoring system, which provides all-round services for condition monitoring. These services include monitoring algorithmdesign and generation, monitor -ing approaches, and monitoring interfaces. With a visualized interactive interface, the monitoring algorithms can be designed online, generated into executable programs, and then implemented into corresponding edge nodes. The edge computing nodes directly act on the real-time data from production equipment which can improve response time for efficient condition monitoring. During the monitoring process, the parameters of the monitoring algorithms can be adjusted and applied in real time. Besides, the monitoring interface can be configured freely with multiple widgets, including charts and video streaming. The feasibility and practicability of the proposed monitoring system have been demonstrated through a real aluminum cold rolling case in process manufacturing.
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