Immersive interconnected virtual and augmented reality : a 5G and IoT perspective

Despite remarkable advances, current augmented and virtual reality (AR/VR) applications are a largely individual and local experience. Interconnected AR/VR, where participants can virtually interact across vast distances, remains a distant dream. The great barrier that stands between current technology and such applications is the stringent end-to-end latency requirement, which should not exceed 20 ms in order to avoid motion sickness and other discomforts. Bringing AR/VR to the next level to enable immersive interconnected AR/VR will require significant advances towards 5G ultra-reliable low-latency communication (URLLC) and a Tactile Internet of Things (IoT). In this article, we articulate the technical challenges to enable a future AR/VR end-to-end architecture, that combines 5G URLLC and Tactile IoT technology to support this next generation of interconnected AR/VR applications. Through the use of IoT sensors and actuators, AR/VR applications will be aware of the environmental and user context, supporting human-centric adaptations of the application logic, and lifelike interactions with the virtual environment. We present potential use cases and the required technological building blocks. For each of them, we delve into the current state of the art and challenges that need to be addressed before the dream of remote AR/VR interaction can become reality

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

A middleware framework for wireless sensor network

Advances in wireless and Micro-Electro-Mechanical Systems (MEMS) technology has given birth to a new technology field sensor networks. These new technologies along with pervasive computing have made the dream of a smart environment come true. Sensors being small and capable of sensing, processing and communicating data has opened a whole new era of applications from medicine to military and from indoors to outdoors. Sensor networks although exciting have very limited resources, for example, memory, processing power and bandwidth, with energy being the most precious resource as they are battery operated. However, these amazing devices can collaborate in order to perform a task. Due to these limitations and specific characteristics being application specific and heterogeneous there is a need to devise techniques and software which would utilize the meager resources efficiently keeping in view the unique characteristics of this network. This thesis presents a lightweight, flexible and energy-efficient middleware framework called MidWSeN which combines aspects of queries, events and context of WSN in a single system. It provides a combination of core and optional services which could be adjusted according to the resources available and specific requirements of the application. The availability of multiple copies of services distributed across the network helps in making the system robust. This middleware framework introduces a new Persistent Storage Service which saves data within the sensor network on the nodes for lifetime of the network to provide historical data. A Priority algorithm is being also presented in this thesis to ensure that enough memory is always available. A novel context enhanced aggregation has also been presented in this thesis which aggregates data with respect to context. Application management service (AMS) provides Service optimization within the network is another novel aspect of the proposed framework. To evaluate the functionality of the work presented, different parts of the framework have also been implemented. The tests and results are detailed to prove the ideas presented in the framework. The work has also been evaluated against a set of requirements and compared against existing works to indicate the novel aspects of framework. Finally some ideas are presented for the future works

Building Blocks for IoT Analytics Internet-of-Things Analytics

Internet-of-Things (IoT) Analytics are an integral element of most IoT applications, as it provides the means to extract knowledge, drive actuation services and optimize decision making. IoT analytics will be a major contributor to IoT business value in the coming years, as it will enable organizations to process and fully leverage large amounts of IoT data, which are nowadays largely underutilized. The Building Blocks of IoT Analytics is devoted to the presentation the main technology building blocks that comprise advanced IoT analytics systems. It introduces IoT analytics as a special case of BigData analytics and accordingly presents leading edge technologies that can be deployed in order to successfully confront the main challenges of IoT analytics applications. Special emphasis is paid in the presentation of technologies for IoT streaming and semantic interoperability across diverse IoT streams. Furthermore, the role of cloud computing and BigData technologies in IoT analytics are presented, along with practical tools for implementing, deploying and operating non-trivial IoT applications. Along with the main building blocks of IoT analytics systems and applications, the book presents a series of practical applications, which illustrate the use of these technologies in the scope of pragmatic applications. Technical topics discussed in the book include: Cloud Computing and BigData for IoT analyticsSearching the Internet of ThingsDevelopment Tools for IoT Analytics ApplicationsIoT Analytics-as-a-ServiceSemantic Modelling and Reasoning for IoT AnalyticsIoT analytics for Smart BuildingsIoT analytics for Smart CitiesOperationalization of IoT analyticsEthical aspects of IoT analyticsThis book contains both research oriented and applied articles on IoT analytics, including several articles reflecting work undertaken in the scope of recent European Commission funded projects in the scope of the FP7 and H2020 programmes. These articles present results of these projects on IoT analytics platforms and applications. Even though several articles have been contributed by different authors, they are structured in a well thought order that facilitates the reader either to follow the evolution of the book or to focus on specific topics depending on his/her background and interest in IoT and IoT analytics technologies. The compilation of these articles in this edited volume has been largely motivated by the close collaboration of the co-authors in the scope of working groups and IoT events organized by the Internet-of-Things Research Cluster (IERC), which is currently a part of EU's Alliance for Internet of Things Innovation (AIOTI)

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization

Energy Efficient Servers

Computer scienc

Energy Efficient Servers

Computer scienc

Digital work in the planetary market

Many of the world’s most valuable companies rely on planetary networks of digital work that underpin their products and services. This important book examines implications for both work and workers when jobs are commodified and traded beyond local labor markets. For instance, Amazon’s contractors in Costa Rica, India, and Romania are paid to structure, annotate, and organize conversations captured by ‘Alexa’ to train Amazon’s speech recognition systems. Findings show that despite its planetary connections, labor remains geographically “sticky” and embedded in distinct contexts. The research emphasizes the globe-spanning nature of contemporary networks without resorting to an understanding of “the global” as a place beyond space.Aujourd’hui, de nombreux emplois peuvent être exercés depuis n’importe où. La technologie numérique et la connectivité Internet généralisée permettent à presque n’importe qui, n’importe où, de se connecter à n’importe qui d’autre pour communiquer et interagir à l’échelle planétaire. Ce livre examine les conséquences, tant pour le travail que pour les travailleurs, de la marchandisation et de l’échange des emplois au-delà des marchés du travail locaux. Allant au-delà du discours habituel sur la mondialisation « le monde est plat », les contributeurs examinent à la fois la transformation du travail lui-même et les systèmes, réseaux et processus plus larges qui permettent le travail numérique dans un marché planétaire, en offrant des perspectives empiriques et théoriques. Les contributeurs - des universitaires et des experts de premier plan issus de diverses disciplines - abordent une variété de questions, notamment la modération du contenu, les véhicules autonomes et les assistants vocaux. Ils se penchent d’abord sur la nouvelle expérience du travail et constatent que, malgré ses connexions planétaires, le travail reste géographiquement collé et intégré dans des contextes distincts. Ils examinent ensuite comment les réseaux planétaires de travail peuvent être cartographiés et problématisés, ils discutent de la multiplicité productive et de l’interdisciplinarité de la réflexion sur le travail numérique et ses réseaux et, enfin, ils imaginent comment le travail planétaire pourrait être réglementé. Les directeurs Mark Graham est professeur de géographie de l’Internet à l’Oxford Internet Institute et chargé de cours à l’Alan Turing Institute. Il est l’éditeur du livre Digital Economies at Global Margins (MIT Press et CRDI, 2019). Fabian Ferrari est un candidat au doctorat à l’Oxford Internet Institute

Automation and Control Architecture for Hybrid Pipeline Robots

The aim of this research project, towards the automation of the Hybrid Pipeline Robot (HPR), is the development of a control architecture and strategy, based on reconfiguration of the control strategy for speed-controlled pipeline operations and self-recovering action, while performing energy and time management. The HPR is a turbine powered pipeline device where the flow energy is converted to mechanical energy for traction of the crawler vehicle. Thus, the device is flow dependent, compromising the autonomy, and the range of tasks it can perform. The control strategy proposes pipeline operations supervised by a speed control, while optimizing the energy, solved as a multi-objective optimization problem. The states of robot cruising and self recovering, are controlled by solving a neuro-dynamic programming algorithm for energy and time optimization, The robust operation of the robot includes a self-recovering state either after completion of the mission, or as a result of failures leading to the loss of the robot inside the pipeline, and to guaranteeing the HPR autonomy and operations even under adverse pipeline conditions Two of the proposed models, system identification and tracking system, based on Artificial Neural Networks, have been simulated with trial data. Despite the satisfactory results, it is necessary to measure a full set of robot’s parameters for simulating the complete control strategy. To solve the problem, an instrumentation system, consisting on a set of probes and a signal conditioning board, was designed and developed, customized for the HPR’s mechanical and environmental constraints. As a result, the contribution of this research project to the Hybrid Pipeline Robot is to add the capabilities of energy management, for improving the vehicle autonomy, increasing the distances the device can travel inside the pipelines; the speed control for broadening the range of operations; and the self-recovery capability for improving the reliability of the device in pipeline operations, lowering the risk of potential loss of the robot inside the pipeline, causing the degradation of pipeline performance. All that means the pipeline robot can target new market sectors that before were prohibitive