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

A movement predicting method is disclosed. The method utilizes at least one phone communicating in and moving between neighboring cells of base stations to predict population movement in a prediction interval. Firstly, obtain the traffic volumes in the cells generated by the phone and the handover information generated by the phone moving between the cells in a day. Next, calculate the traffic volumes and the handover information to obtain a movement probability for the population moving between the cells and an average residence time that the population stays in the region of each cell in the different periods of the day. Finally, according to the data obtained, predict an appearance probability that the population appears in each region at the end point of the prediction interval.

https://ir.nctu.edu.tw:443/bitstream/11536/105073/1/20130040644.pdf

Movement predicting method

Existing IoT services are based on data communications technologies that do not involve the public switched telephone network (PSTN). Since the telephone numbers have been assigned to machine-type devices, PSTN switches can play a role in IoT service routing. In this article we deploy a PSTN-based IoT mechanism where the interaction between the users and the IoT devices is achieved through PSTN switches. To our knowledge, this is the first PSTN-based IoT solution in the world. With this mechanism, all PSTN customer premises equipment (CPE; fixed-line and mobile phones) can access IoT services without installing any software (mobile apps). By reusing the existing PSTN infrastructure, PSTN-based IoT offers telecom-grade service, security, and network management for IoT, which are very expensive to build in non-PSTN-based IoT. Our approach conveniently enables the existing CPE to access IoT applications, which will significantly promote the IoT service industry.

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Deploying the First PSTN-Based IoT Mechanism

Flexible Resource Allocation scheme for GSM data services

Cellular digital packet data (CDPD) offers on-the-go users access to a low-cost, widespread, wireless data network. CDPD can be overlaid on existing analog cellular systems, and share their infrastructure equipment on a non-interfering basis. Basically, CDPD transmits packet data over idle cellular channels. It autonomously switches to another channel when the current channel is about to be assigned for voice usage. CDPD does not communicate with the underlining cellular network. However, CDPD does use its knowledge about the channel assignment algorithm of the cellular cell to predict the channels available for CDPD use. CDPD may serve as the wireless extension to other data networks (e.g., Internet) or public switched telephone networks. It supports connectionless network services which have packets called network protocol data units (NPDU). Every packet is routed individually based on the destination address of the packet and knowledge of the current network topology. Initially, CDPD provides two connectionless network services: the standard open systems interconnection connectionless network protocol and the Internet protocol.

Yi-Bing Lin

Papers

Movement predicting method

Deploying the First PSTN-Based IoT Mechanism

Flexible Resource Allocation scheme for GSM data services

Cellular digital packet data

Allocating resources for soft requests—a performance study