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

On pig farms, many piglets die because they are crushed when sows roll from side to side or lie down. On average, 1.2 piglets are crushed by sows every day. To resolve the piglet mortality issue, this article proposes PigTalk, an artificial intelligence (AI) based Internet of Things (IoT) platform for detecting and mitigating piglet crushing. Through real-time analysis of the voice data collected in a farrowing house, PigTalk detects if any piglet screaming occurs, and automatically activates sow-alert actuators for emergency handling of the crushing event. We propose an audio clip transform approach to pre-process the raw voice data, and utilizes min-max scaling in machine learning (ML) to detect piglet screams. In our first contribution, the above data preprocessing method together with subtle parameter setups of the machine learning model improve the piglet scream detection accuracy up to 99.4%, which is better than the previous solutions (up to 92.8%). In our second contribution, we show how to design two cyber IoT devices, i.e., DataBank for data pre-processing and ML_device for real-time AI to automatically trigger actuators such as floor vibration and water drop to force a sow to stand up. We conduct analytic analysis and simulation to investigate how the detection delay affects the critical time period to save crushed piglets. Our study indicates that PigTalk can save piglets within 0.05 s with 99.93% of the successful rate. Such results are validated in a commercial farrowing house. PigTalk is a new approach that automatically mitigates piglet crushing, which could not be achieved in the past.

PigTalk: An AI-Based IoT Platform for Piglet Crushing Mitigation

At present, drone delivery systems usually function only in global positioning system (GPS)-friendly environments and thus cannot deliver goods into customers’ houses, which is an element of urban last-mile delivery. In this study, we investigated solutions for enabling drones to fly autonomously in mixed indoor–outdoor environments. We propose a novel Internet of Things (IoT)-based drone delivery system, namely DroneTalk, for mail delivery. DroneTalk combines a GPS, an inertial measurement unit, and visual information to achieve mixed indoor–outdoor autopilot operation. Furthermore, DroneTalk integrates an autonomous drone control system with an IoT device management platform to enable a drone to achieve automatic online weather awareness. Most importantly, DroneTalk is a novel work considering communication, processing, and control latencies simultaneously during autopilot operation to avoid problems related to drone collisions. These problems have always existed but become more serious when drones are flying close to buildings. Simulation results indicate that the proposed system can attain a high user-defined flight success rate (i.e., 99% in our simulations) without any collisions; thus, the proposed system can be feasibly used in real-world environments.

DroneTalk: An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery

This article is a brief overview of the GSM (global system for mobile communications) system. GSM is a wireless digital signaling network standard designed by standardization committees from the major European telecommunications operators and manufacturers. The GSM standard provides a common set of compatible services and capabilities to all mobile users across Europe. The basic requirements of GSM are described in five aspects: services; quality of services and security; radio frequency utilisation; network; and cost. >

No wires attached

Special issue on mobile computing

Today, charging stations (CSs) for electric vehicles (EVs) are much less popular than gas stations. Therefore, searching and selecting CSs is an important issue for the drivers of EVs. This paper investigates the EV charging problem. We propose two types of CS-selection algorithms. The first type only utilizes local information of an EV. The second type utilizes the global information obtained from mobile computing. Specifically, the EVs interact with a Global CS-selection (GCS) server through the mobile telecommunications network. Our study indicates that by using the global information (specifically the workload status of each CS), the EVs can be effectively charged with short waiting times at the CSs.

Yi-Bing Lin

Papers

PigTalk: An AI-Based IoT Platform for Piglet Crushing Mitigation

DroneTalk: An Internet-of-Things-Based Drone System for Last-Mile Drone Delivery

No wires attached

Special issue on mobile computing

Charge Scheduling of Electric Vehicles in Highways through Mobile Computing