Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network

A Novel Method for Adaptive Control of Manufacturing Equipment in Cloud Environments

The ability to adaptively control manufacturing equipment, both in local and distributed environments, is becoming increasingly more important for many manufacturing companies. One important reason for this is that manufacturing companies are facing increasing levels of changes, variations and uncertainty, caused by both internal and external factors, which can negatively impact their performance. Frequently changing consumer requirements and market demands usually lead to variations in manufacturing quantities, product design and shorter product life-cycles. Variations in manufacturing capability and functionality, such as equipment breakdowns, missing/worn/broken tools and delays, also contribute to a high level of uncertainty. The result is unpredictable manufacturing system performance, with an increased number of unforeseen events occurring in these systems. Events which are difficult for traditional planning and control systems to satisfactorily manage. For manufacturing scenarios such as these, the use of real-time manufacturing information and intelligence is necessary to enable manufacturing activities to be performed according to actual manufacturing conditions and requirements, and not according to a pre-determined process plan. Therefore, there is a need for an event-driven control approach to facilitate adaptive decision-making and dynamic control capabilities. Another reason driving the move for adaptive control of manufacturing equipment is the trend of increasing globalization, which forces manufacturing industry to focus on more cost-effective manufacturing systems and collaboration within global supply chains and manufacturing networks. Cloud Manufacturing is evolving as a new manufacturing paradigm to match this trend, enabling the mutually advantageous sharing of resources, knowledge and information between distributed companies and manufacturing units. One of the crucial objectives for Cloud Manufacturing is the coordinated planning, control and execution of discrete manufacturing operations in collaborative and networked environments. Therefore, there is also a need that such an event-driven control approach supports the control of distributed manufacturing equipment. The aim of this research study is to define and verify a novel and comprehensive method for adaptive control of manufacturing equipment in cloud environments. The presented research follows the Design Science Research methodology. From a review of research literature, problems regarding adaptive manufacturing equipment control have been identified. A control approach, building on a structure of event-driven Manufacturing Feature Function Blocks, supported by an Information Framework, has been formulated. The Function Block structure is constructed to generate real-time control instructions, triggered by events from the manufacturing environment. The Information Framework uses the concept of Ontologies and The Semantic Web to enable description and matching of manufacturing resource capabilities and manufacturing task requests in distributed environments, e.g. within Cloud Manufacturing. The suggested control approach has been designed and instantiated, implemented as prototype systems for both local and distributed manufacturing scenarios, in both real and virtual applications. In these systems, event-driven Assembly Feature Function Blocks for adaptive control of robotic assembly tasks have been used to demonstrate the applicability of the control approach. The utility and performance of these prototype systems have been tested, verified and evaluated for different assembly scenarios. The proposed control approach has many promising characteristics for use within both local and distributed environments, such as cloud environments. The biggest advantage compared to traditional control is that the required control is created at run-time according to actual manufacturing conditions. The biggest obstacle for being applicable to its full extent is manufacturing equipment controlled by proprietary control systems, with native control languages. To take the full advantage of the IEC Function Block control approach, controllers which can interface, interpret and execute these Function Blocks directly, are necessary

Context-aware mobile applications design: implications and challeges for a new indusy

Context-aware computing is slowly becoming the new mobile paradigm in which applications can discover and use information “out and about”. Typical sources of knowledge about context are the device’s location, data about the environment at large, the mobile device’s prior activity log and even the user’s biometrics. The mobile industry agrees that this paradigm improves the appeal and value of applications by personalising and adapting them to the context in which they run. However, capturing contextual information and processing it to enhance or create a new application is a daunting task: it involves scattered systems and infrastructures and an increasingly wide array of heterogeneous data, architectures and technological tools. In this paper, we explore and analyse existing mobile context-aware applications and the proposed frameworks that enable them. The paper aims to clarify the echnological choices behind context-aware mobile applications and the challenges that still remain ahead for this area to fulfil the promises it offers

Architecture for intelligent power systems management, optimization, and storage.

The management of power and the optimization of systems generating and using power are critical technologies. A new architecture is developed to advance the current state of the art by providing an intelligent and autonomous solution for power systems management. The architecture is two-layered and implements a decentralized approach by defining software objects, similar to software agents, which provide for local optimization of power devices such as power generating, storage, and load devices. These software device objects also provide an interface to a higher level of optimization. This higher level of optimization implements the second layer in a centralized approach by coordinating the individual software device objects with an intelligent expert system thus resulting in architecture for total system power management. In this way, the architecture acquires the benefits of both the decentralized and centralized approaches. The architecture is designed to be portable, scalable, simple, and autonomous, with respect to devices and missions. Metrics for evaluating these characteristics are also defined. Decentralization achieves scalability and simplicity through modularization using software device objects that can be added and deleted as modules based on the devices of the power system are being optimized. Centralization coordinates these software device objects to bring autonomy and intelligence of the whole power system and mission to the architecture. The centralization approach is generic since it always coordinates software device objects; therefore it becomes another modular component of the architecture. Three example implementations illustrate the evolution of this power management system architecture. The first implementation is a coal-fired power generating station that utilized a neural network optimization for the reduction of nitrogen oxide emissions. This illustrates the limitations of this type of black-box optimization and serves as a motivation for developing a more functional architecture. The second implementation is of a hydro-generating power station where a white-box, software agent approach illustrates some of the benefits and provides initial justification of moving towards the proposed architecture. The third implementation applies the architecture to a vehicle to grid application where the previous hydro-generating application is ported and a new hybrid vehicle application is defined. This demonstrates portability and scalability in the architecture, and linking these two applications demonstrates autonomy. The simplicity of building this application is also evaluated

Vehicle electrification: technologies, challenges and a global perspective for smart grids

Nowadays, due to economic and climate concerns, the private transportation sector is shifting for the vehicle electrification, mainly supported by electric and hybrid plug-in vehicles. For this new reality, new challenges about operation modes are emerging, demanding a cooperative and dynamic operation with the electrical power grid, guaranteeing a stable integration without omitting the power quality for the grid-side and for the vehicle-side. Besides the operation modes, new attractive and complementary technologies are offered by the vehicle electrification in the context of smart grids, which are valid for both on-board and off-board systems. In this perspective, this book chapter presents a global perspective and deals with challenges for the vehicle electrification, covering the key technologies toward a sustainable future. Among others, the flowing topics are covered: (1) Overview of power electronics structures for battery charging systems, including on-board and off-board systems; (2) State-of-the-art of communication technologies for application in the context of vehicular electrification, smart grids and smart homes; (3) Challenges and opportunities concerning wireless power transfer with bidirectional interface to the electrical grid; (4) Future perspectives about bidirectional power transfer between electric vehicles (vehicle-to-vehicle operation mode); (5) Unified technologies, allowing to combine functionalities of a bidirectional interface with the electrical grid and motor driver based on a single system; and (6) Smart grids and smart homes scenarios and accessible opportunities about operation modes.Fundação para a Ciência e Tecnologia (FCT

Internet of Things Applications - From Research and Innovation to Market Deployment

The book aims to provide a broad overview of various topics of Internet of Things from the research, innovation and development priorities to enabling technologies, nanoelectronics, cyber physical systems, architecture, interoperability and industrial applications. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from technology to international cooperation and the global "state of play".The book builds on the ideas put forward by the European research Cluster on the Internet of Things Strategic Research Agenda and presents global views and state of the art results on the challenges facing the research, development and deployment of IoT at the global level. Internet of Things is creating a revolutionary new paradigm, with opportunities in every industry from Health Care, Pharmaceuticals, Food and Beverage, Agriculture, Computer, Electronics Telecommunications, Automotive, Aeronautics, Transportation Energy and Retail to apply the massive potential of the IoT to achieving real-world solutions. The beneficiaries will include as well semiconductor companies, device and product companies, infrastructure software companies, application software companies, consulting companies, telecommunication and cloud service providers. IoT will create new revenues annually for these stakeholders, and potentially create substantial market share shakeups due to increased technology competition. The IoT will fuel technology innovation by creating the means for machines to communicate many different types of information with one another while contributing in the increased value of information created by the number of interconnections among things and the transformation of the processed information into knowledge shared into the Internet of Everything. The success of IoT depends strongly on enabling technology development, market acceptance and standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale. The connected devices are part of ecosystems connecting people, processes, data, and things which are communicating in the cloud using the increased storage and computing power and pushing for standardization of communication and metadata. In this context security, privacy, safety, trust have to be address by the product manufacturers through the life cycle of their products from design to the support processes. The IoT developments address the whole IoT spectrum - from devices at the edge to cloud and datacentres on the backend and everything in between, through ecosystems are created by industry, research and application stakeholders that enable real-world use cases to accelerate the Internet of Things and establish open interoperability standards and common architectures for IoT solutions. Enabling technologies such as nanoelectronics, sensors/actuators, cyber-physical systems, intelligent device management, smart gateways, telematics, smart network infrastructure, cloud computing and software technologies will create new products, new services, new interfaces by creating smart environments and smart spaces with applications ranging from Smart Cities, smart transport, buildings, energy, grid, to smart health and life. Technical topics discussed in the book include: • Introduction• Internet of Things Strategic Research and Innovation Agenda• Internet of Things in the industrial context: Time for deployment.• Integration of heterogeneous smart objects, applications and services• Evolution from device to semantic and business interoperability• Software define and virtualization of network resources• Innovation through interoperability and standardisation when everything is connected anytime at anyplace• Dynamic context-aware scalable and trust-based IoT Security, Privacy framework• Federated Cloud service management and the Internet of Things• Internet of Things Application