Cloud computing

Abstract: This book outlines the background and overall vision for the Internet of Things (IoT) and Machine-to-Machine (M2M) communications and services, including major standards. Key technologies are described, and include everything from physical instrumentation of devices to the cloud infrastructures used to collect data. Also included is how to derive information and knowledge, and how to integrate it into enterprise processes, as well as system architectures and regulatory requirements. Real-world service use case studies provide the hands-on knowledge needed to successfully develop and implement M2M and IoT technologies sustainably and profitably. Finally, the future vision for M2M technologies is described, including prospective changes in relevant standards. This book is written by experts in the technology and business aspects of Machine-to-Machine and Internet of Things, and who have experience in implementing solutions. Standards included: ETSI M2M, IEEE 802.15.4, 3GPP (GPRS, 3G, 4G), Bluetooth Low Energy/Smart, IETF 6LoWPAN, IETF CoAP, IETF RPL, Power Line Communication, Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE), ZigBee, 802.11, Broadband Forum TR-069, Open Mobile Alliance (OMA) Device Management (DM), ISA100.11a, WirelessHART, M-BUS, Wireless M-BUS, KNX, RFID, Object Management Group (OMG) Business Process Modelling Notation (BPMN)Key technologies for M2M and IoT covered: Embedded systems hardware and software, devices and gateways, capillary and M2M area networks, local and wide area networking, M2M Service Enablement, IoT data management and data warehousing, data analytics and big data, complex event processing and stream analytics, knowledge discovery and management, business process and enterprise integration, Software as a Service and cloud computing Combines both technical explanations together with design features of M2M/IoT and use cases. Together, these descriptions will assist you to develop solutions that will work in the real world Detailed description of the network architectures and technologies that form the basis of M2M and IoT Clear guidelines and examples of M2M and IoT use cases from real-world implementations such as Smart Grid, Smart Buildings, Smart Cities, Participatory Sensing, and Industrial Automation A description of the vision for M2M and its evolution towards IoT

Abstract: Rapid technological advances have revolutionized the industrial sector These advances range from automation of industrial processes to autonomous industrial processes, where a human input is not required Internet of Things (IoT), which has emerged a few years ago, has been embraced by industry, resulting in what is known as the Industrial Internet of Things (IIoT) IIoT refers to making industrial processes and entities part of the Internet Restricting the definition of IIoT to manufacturing yields another subset of IoT, known as Industry 40 IIoT and Industry 40, will consist of sensor networks, actuators, robots, machines, appliances, business processes, and personnel Hence, a lot of data of diverse nature would be generated The industrial process requires most of the tasks to be performed locally because of delay and security requirements and structured data to be communicated over the Internet to web services and the cloud To achieve this task, middleware support is required between the industrial environment and the cloud/web services In this context, fog is a potential middleware that can be very useful for different industrial scenarios Fog can provide local processing support with acceptable latency to actuators and robots in a manufacturing industry Additionally, as industrial big data are often unstructured, it can be trimmed and refined by the fog locally, before sending it to the cloud We present an architectural overview of IIoT and Industry 40 We discuss how fog can provide local computing support in the IIoT environment and the core elements and building blocks of IIoT We also present a few interesting prospective use cases of IIoT Finally, we discuss some emerging research challenges related to IIoT

Abstract: The standard objective in machine learning is to train a single model for all users. However, in many learning scenarios, such as cloud computing and federated learning, it is possible to learn a personalized model per user. In this work, we present a systematic learning-theoretic study of personalization. We propose and analyze three approaches: user clustering, data interpolation, and model interpolation. For all three approaches, we provide learning-theoretic guarantees and efficient algorithms for which we also demonstrate the performance empirically. All of our algorithms are model-agnostic and work for any hypothesis class.