System Abstractions for Scalable Application Development at the Edge

Recent years have witnessed an explosive growth of Internet of Things (IoT) devices, which collect or generate huge amounts of data. Given diverse device capabilities and application requirements, data processing takes place across a range of settings, from on-device to a nearby edge server/cloud and remote cloud. Consequently, edge-cloud coordination has been studied extensively from the perspectives of job placement, scheduling and joint optimization. Typical approaches focus on performance optimization for individual applications. This often requires domain knowledge of the applications, but also leads to application-specific solutions. Application development and deployment over diverse scenarios thus incur repetitive manual efforts. There are two overarching challenges to provide system-level support for application development at the edge. First, there is inherent heterogeneity at the device hardware level. The execution settings may range from a small cluster as an edge cloud to on-device inference on embedded devices, differing in hardware capability and programming environments. Further, application performance requirements vary significantly, making it even more difficult to map different applications to already heterogeneous hardware. Second, there are trends towards incorporating edge and cloud and multi-modal data. Together, these add further dimensions to the design space and increase the complexity significantly. In this thesis, we propose a novel framework to simplify application development and deployment over a continuum of edge to cloud. Our framework provides key connections between different dimensions of design considerations, corresponding to the application abstraction, data abstraction and resource management abstraction respectively. First, our framework masks hardware heterogeneity with abstract resource types through containerization, and abstracts away the application processing pipelines into generic flow graphs. Further, our framework further supports a notion of degradable computing for application scenarios at the edge that are driven by multimodal sensory input. Next, as video analytics is the killer app of edge computing, we include a generic data management service between video query systems and a video store to organize video data at the edge. We propose a video data unit abstraction based on a notion of distance between objects in the video, quantifying the semantic similarity among video data. Last, considering concurrent application execution, our framework supports multi-application offloading with device-centric control, with a userspace scheduler service that wraps over the operating system scheduler

Bibliographic and Text Analysis of Research on Implementation of the Internet of Things to Support Education

The Internet of Things (IoT) has pervaded practically all aspects of our lives. In this exploratory study, we survey its applications in the field of education. It is evident that technology in general, and, in particular IoT, has been increasingly altering the educational landscape. The goal of this paper is to review the academic literature on IoT applications in education to provide an understanding of the transformation that is underway. Using topic modeling and keyword co-occurrence analysis techniques, we identified five dominant clusters of research. Our findings demonstrate that IoT research in education has mainly focused on the technical aspects; however, the social aspects remain largely unexplored. In addition to providing an overview of IoT research on education, this paper offers suggestions for future research

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services

Current robotic systems handle a different range of applications such as video surveillance, delivery of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems have been embraced not only by the general population but also by the vertical industries to help them in performing daily activities. Traditionally, the robotic systems have been deployed in standalone robots that were exclusively dedicated to performing a specific task such as cleaning the floor in indoor environments. In recent years, cloud providers started to offer their infrastructures to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions are appearing in this form. As a result, standalone robots became software-enhanced objects with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading the heavy processing from the robot to the cloud, it is easier to share services and information from various robots or agents to achieve better cooperation and coordination. Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities (e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications (e.g., remote control, motion planning, autonomous navigation) that can not be executed with cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots. While the cloud providers can ensure certain performance in their infrastructure, very little can be ensured in the network between the robots and the cloud, especially in the last hop where wireless radio access networks are involved. Over the last years advances in edge computing, fog computing, 5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Robotic systems are a key piece in the industrial digital transformation and their benefits are very well studied in the literature. However, designing and implementing a robotic system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. This thesis studies the integration of modern Information andCommunication Technologies (ICTs) in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of robotic services. To evaluate the performance of the proposed enhancements, this thesis departs from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge robotics system serves to represent two exemplary robotic applications. In particular, autonomous navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic system is distributed between the robots and the edge server. The open-source prototype implementation of the designed edge native robotic system resulted in the creation of two real-world testbeds that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in robotic systems. After detailing the design and prototype implementation of the end-to-end edge native robotic system, this thesis proposes several enhancements that can be offered to robotic systems by adapting the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it proposes exemplary network context-aware enhancements in which the real-time information about robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to the actual status of the communication (e.g., radio channel). Three different exemplary context-aware enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later, the thesis studies the capability of the edge native robotic system to offer potential savings by means of computation offloading for robot manipulators in different deployment configurations. Further, the impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a robot manipulator and its remote controller are assessed. In the following part of the thesis, the focus is set on how orchestration solutions can support mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated solutions present high compatibility with the designed edge robotics system where the robot driving range is extended without any interruption of the end-to-end edge robotics service. While the DLT-based federation extends the robot driving range by deploying access point extension on top of external domain infrastructure, OKpi selects the most suitable access point and computing resource in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled mobile robots. To conclude the thesis the focus is set on how robotic systems can improve their performance by leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions. To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are integrated with computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure level. In the last part of the thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer lost commands caused by interference in the wireless channel. The obtained results are showcasing the its potential in simulation and real-world experimentation.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett

The after party: Cynical resignation in Adtech's pivot to privacy

Digital advertising and technology companies are resigned to a new privacy imperative. They are bracing for a world where third-party tracking will be restricted by design or by law. Digital resignation typically refers to how companies cultivate a sense of powerlessness about privacy among internet users. Our paper looks through this optic from the other end of the lens: How is the digital advertising industry coping with the increasing salience of privacy? Recent developments have forced companies to implement “privacy-preserving” designs—or at least promise some semblance of privacy. Yet, the industry remains dependent on flows of data and means of identification to enable still-desired targeting, measurement, and optimization. Our paper analyzes this contradiction by looking at systems that aim to replicate existing functionalities while protecting user “privacy.” We call this a form of “cynical resignation” and characterize its key maneuvers as follows: (a) sanitizing surveillance; (b) party-hopping; and (c) sabotage. We argue that this “cynical resignation” to a privacy imperative represents a policy failure. In the absence of decisive interventions into the underlying business models of data capitalism, companies offer techno-solutionism and self-regulations that seem to conform to new laws and norms while reinforcing commitments to data-driven personalization. This may benefit the largest tech companies, since their privileged access to first-party data will make more companies reliant on them, and their computational power will be even more valuable in a world where modeling is used to compensate for the loss of third-party data and traditional methods of personal identification

Enabling IoT in Manufacturing: from device to the cloud

Industrial automation platforms are experiencing a paradigm shift. With the new technol-ogies and strategies that are being applied to enable a synchronization of the digital and real world, including real-time access to sensorial information and advanced networking capabilities to actively cooperate and form a nervous system within the enterprise, the amount of data that can be collected from real world and processed at digital level is growing at an exponential rate. Indeed, in modern industry, a huge amount of data is coming through sensorial networks em-bedded in the production line, allowing to manage the production in real-time. This dissertation proposes a data collection framework for continuously collecting data from the device to the cloud, enabling resources at manufacturing industries shop floors to be handled seamlessly. The framework envisions to provide a robust solution that besides collecting, transforming and man-aging data through an IoT model, facilitates the detection of patterns using collected historical sensor data. Industrial usage of this framework, accomplished in the frame of the EU C2NET project, supports and automates collaborative business opportunities and real-time monitoring of the production lines

Artificial Intelligence Technology

This open access book aims to give our readers a basic outline of today’s research and technology developments on artificial intelligence (AI), help them to have a general understanding of this trend, and familiarize them with the current research hotspots, as well as part of the fundamental and common theories and methodologies that are widely accepted in AI research and application. This book is written in comprehensible and plain language, featuring clearly explained theories and concepts and extensive analysis and examples. Some of the traditional findings are skipped in narration on the premise of a relatively comprehensive introduction to the evolution of artificial intelligence technology. The book provides a detailed elaboration of the basic concepts of AI, machine learning, as well as other relevant topics, including deep learning, deep learning framework, Huawei MindSpore AI development framework, Huawei Atlas computing platform, Huawei AI open platform for smart terminals, and Huawei CLOUD Enterprise Intelligence application platform. As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence

Comparative analyzes of technological tools between industry 4.0 and smart cities approaches: the new society ecosystem

Today the growth of modern cities is unprecedented in the history of urbanization and the urban environmental problems have also been increased. Unfortunately, there is no much time to modify past failures and improve the status quo, and ensure the protection of the environment. Consequently, it’s important to pay attention to the development of sustainable urban planning and its role in urban management issues is an objective that requires a new approach.On the other hand, Industry 4.0 (I.4.0), as called the 4th Industrial Revolution, carries impacts in the production on companies, the economy and society, with disruptive character, creating new markets and destabilizing the traditional way of doing business. Once I.4.0 is a strategic approach to the integration of advanced control systems with internet technology, enabling communication between people, products and complex systems, it’s expected to follow the same in the Smart Cities development.This article aims to relate technological tools of I.4.0 and the dimensions of “Smart Cities”, based on analytical framework for better understanding the emergence of new society ecosystem focused on the redefinition of the cities’ concept, urbanism and way of life, motivated by this new reconfiguration

Iot-enabled smart cities: evolution and outlook

For the last decade the Smart City concept has been under development, fostered by the growing urbanization of the world’s population and the need to handle the challenges that such a scenario raises. During this time many Smart City projects have been executed–some as proof-of-concept, but a growing number resulting in permanent, production-level deployments, improving the operation of the city and the quality of life of its citizens. Thus, Smart Cities are still a highly relevant paradigm which needs further development before it reaches its full potential and provides robust and resilient solutions. In this paper, the focus is set on the Internet of Things (IoT) as an enabling technology for the Smart City. In this sense, the paper reviews the current landscape of IoT-enabled Smart Cities, surveying relevant experiences and city initiatives that have embedded IoT within their city services and how they have generated an impact. The paper discusses the key technologies that have been developed and how they are contributing to the realization of the Smart City. Moreover, it presents some challenges that remain open ahead of us and which are the initiatives and technologies that are under development to tackle them.This research was partially funded by Spain State Research Agency (AEI) by means of the project FIERCE: Future Internet Enabled Resilient CitiEs (RTI2018-093475-A-I00). Prof. Song was supported by Smart City R&D project of the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (MOLIT), Ministry of Science and ICT (MSIT) (Grant 18NSPS-B149386-01)