物件導向軟體之架構(Object-Oriented Software Construction)探討

The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

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Internet of things: Vision, applications and research challenges

A mobile ad hoc network consists of wireless hosts that may move often. Movement of hosts results in a change in routes, requiring some mechanism for determining new routes. Several routing protocols have already been proposed for ad hoc networks. This report suggests an approach to utilize location information (for instance, obtained using the global positioning system) to improve performance of routing protocols for ad hoc networks.

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Location-aided routing (LAR) in mobile ad hoc networks

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

Parallel discrete event simulation (PDES), sometimes called distributed simulation, refers to the execution of a single discrete event simulation program on a parallel computer. PDES has attracted a considerable amount of interest in recent years. From a pragmatic standpoint, this interest arises from the fact that large simulations in engineering, computer science, economics, and military applications, to mention a few, consume enormous amounts of time on sequential machines. From an academic point of view, parallel simulation is interesting because it represents a problem domain that often contains substantial amounts of parallelism (e.g., see [59]), yet paradoxically, is surprisingly difficult to parallelize in practice. A sufficiently general solution to the PDES problem may lead to new insights in parallel computation as a whole. Historically, the irregular, data-dependent nature of PDES programs has identified it as an application where vectorization techniques using supercomputer hardware provide little benefit [14].A discrete event simulation model assumes the system being simulated only changes state at discrete points in simulated time. The simulation model jumps from one state to another upon the occurrence of an event. For example, a simulator of a store-and-forward communication network might include state variables to indicate the length of message queues, the status of communication links (busy or idle), etc. Typical events might include arrival of a message at some node in the network, forwarding a message to another network node, component failures, etc.We are especially concerned with the simulation of asynchronous systems where events are not synchronized by a global clock, but rather, occur at irregular time intervals. For these systems, few simulator events occur at any single point in simulated time; therefore parallelization techniques based on lock-step execution using a global simulation clock perform poorly or require assumptions in the timing model that may compromise the fidelity of the simulation. Concurrent execution of events at different points in simulated time is required, but as we shall soon see, this introduces interesting synchronization problems that are at the heart of the PDES problem.This article deals with the execution of a simulation program on a parallel computer by decomposing the simulation application into a set of concurrently executing processes. For completeness, we conclude this section by mentioning other approaches to exploiting parallelism in simulation problems.Comfort and Shepard et al. have proposed using dedicated functional units to implement specific sequential simulation functions, (e.g., event list manipulation and random number generation [20, 23, 47]). This method can provide only a limited amount of speedup, however. Zhang, Zeigler, and Concepcion use the hierarchical decomposition of the simulation model to allow an event consisting of several subevents to be processed concurrently [21, 98]. A third alternative is to execute independent, sequential simulation programs on different processors [11, 39]. This replicated trials approach is useful if the simulation is largely stochastic and one is performing long simulation runs to reduce variance, or if one is attempting to simulate a specific simulation problem across a large number of different parameter settings. However, one drawback with this approach is that each processor must contain sufficient memory to hold the entire simulation. Furthermore, this approach is less suitable in a design environment where results of one experiment are used to determine the experiment that should be performed next because one must wait for a sequential execution to be completed before results are obtained.

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Parallel discrete event simulation

The FCC has allocated the spectrum between 1850-1910 and 1930-1990 MHz for licensed personal communications services (PCS) and the band between 1920-1930 MHz for unlicensed PCS (UPCS). This paper describes a polling deregistration protocol for a wireless access communications network which would support interoperability between licensed personal communications services (PCS) and unlicensed-PCS (UPCS). We show how the protocol can be efficiently implemented in the personal access communications system (PACS) for licensed PCS and in the PACS-Unlicensed Version B (PACS-UB). An analytical model is proposed to analyze the performance of the polling protocol. A cost function is derived, which can be used to estimate the optimal polling frequency.

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Polling deregistration for unlicensed PCS

In recent years, Long Term Evolution systems have been developed to replace 3G mobile networks. During LTE network deployment, it is essential that mobile operators and manufacturers conduct tests on user equipment and the base station (eNB). Besides conformance testing at the radio layer, it is also important to conduct network-layer performance tests between UE and eNB. This article develops an LTE test environment that can be used to test the IP-based packet performance for TD-LTE and LTE FDD. Specifically, we consider the latency and the throughput performance of IP, TCP, and VoIP (i.e., G.711) packet delivery. This multi-purpose test environment can be utilized to investigate the performance of the UE and/or eNBs of various manufacturers. Furthermore, we use this environment to compare TD-LTE and LTE FDD. The measurement results provide guidelines for UE and eNB vendors to improve their products, and operators to deploy their LTE networks and subsidized UE devices.

https://ir.nctu.edu.tw:443/bitstream/11536/23996/1/000333105100015.pdf

An IP-based packet test environment for TD-LTE and LTE FDD

The quality of voice over Internet protocol (VoIP) is typically measured by mean opinion score (MOS) that significantly varies in wireless environments. Several approaches have been proposed to evaluate the MOS values of voice paths. However, these solutions require extra servers/gateways to conduct heavy computation for MOS measurement of VoIP calls. We propose two MOS measurement procedures for mobile devices. In our approach, a simple program is installed in each of the mobile devices (through an app-store like mechanism). In the perceptual evaluation of speech quality (PESQ) MOS measurement procedure, a lightweight real-time table lookup solution significantly reduces the computation time of PESQ MOS measurement from 315.4 s to 3 s. In the E-model MOS measurement procedure, the E-model MOS value can be accurately computed in 5.35 s.

https://ir.nctu.edu.tw:443/bitstream/11536/14917/1/000297425200011.pdf

Real-Time VoIP Quality Measurement for Mobile Devices

Internet of Things (IoT) has been widely utilized with artificial intelligence (AI). However, it requires substantial effort to integrate AI and big data with IoT. To mitigate this problem, AItalk was proposed. By treating AI as a cyber IoT device, we do not need to write code of the AI mechanism in the network applications, as traditional AI-based IoT applications did. This article describes how the AI tools such as scikit-learn and TensorLayer are accommodated as cyber IoT devices in AItalk, and extends AItalk for non-IoT applications. We use house valuation as an example to show how AItalk can flexibly include the factors that have significant impact on the house price. We show that by adding extra features other than housing profile features, the accuracy for the prediction (valuation) can be improved by 38%. We also investigated the communication overhead of the distributed AItalk structure, which is 3.7% for the computation of one house price valuation.

Implementing AI as Cyber IoT Devices: The House Valuation Example

The problem is discussed of how the use of a new data structure, the codeword structure, can help improve the performance of quicksort when the records to be sorted are long and the keys are alphanumeric sequences of bytes. The codeword is a compact representation of a key with respect to some codeword generator. It consists of a byte for a character count of equal bytes, a byte for the first nonequal byte, and a pointer to the record. It is shown how the ordering of keys is preserved by an adequate choice of the code generator and how this can be applied to the quicksort algorithm. An analysis of the potential saving son various architectures and actual measurements shows the improvements that can be attained by using codewords rather than pointers. Architecturally independent parameters, such as the number of bytes to be compared, the number of swaps, architecture-dependent parameters such as caches and their write policies, and compiler optimizations such as in-line expansion and register allocation are considered. >

Yi-Bing Lin

Papers

Polling deregistration for unlicensed PCS

An IP-based packet test environment for TD-LTE and LTE FDD

Real-Time VoIP Quality Measurement for Mobile Devices

Implementing AI as Cyber IoT Devices: The House Valuation Example

Improving quicksort performance with a codeword data structure