New Waves of IoT Technologies Research – Transcending Intelligence and Senses at the Edge to Create Multi Experience Environments

The next wave of Internet of Things (IoT) and Industrial Internet of Things (IIoT) brings new technological developments that incorporate radical advances in Artificial Intelligence (AI), edge computing processing, new sensing capabilities, more security protection and autonomous functions accelerating progress towards the ability for IoT systems to self-develop, self-maintain and self-optimise. The emergence of hyper autonomous IoT applications with enhanced sensing, distributed intelligence, edge processing and connectivity, combined with human augmentation, has the potential to power the transformation and optimisation of industrial sectors and to change the innovation landscape. This chapter is reviewing the most recent advances in the next wave of the IoT by looking not only at the technology enabling the IoT but also at the platforms and smart data aspects that will bring intelligence, sustainability, dependability, autonomy, and will support human-centric solutions.acceptedVersio

Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes

Internet of Things. Information Processing in an Increasingly Connected World

This open access book constitutes the refereed post-conference proceedings of the First IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2018, held at the 24th IFIP World Computer Congress, WCC 2018, in Poznan, Poland, in September 2018. The 12 full papers presented were carefully reviewed and selected from 24 submissions. Also included in this volume are 4 WCC 2018 plenary contributions, an invited talk and a position paper from the IFIP domain committee on IoT. The papers cover a wide range of topics from a technology to a business perspective and include among others hardware, software and management aspects, process innovation, privacy, power consumption, architecture, applications

Validation of a building simulation tool for predictive control in energy management systems

Buildings are responsible for a significant portion of energy consumption worldwide. Intelligent buildings have been devised as a potential solution, where energy consumption and building use are harmonised. At the heart of the intelligent building is the building energy management system (BEMS), the central platform which manages and coordinates all the building monitoring and control subsystems, such as heating and lighting loads. There is often a disconnect between the BEMS and the building it is installed in, leading to inefficient operation, due to incongruous commissioning of sensors and control systems. In these cases, the BEMS has a lack of knowledge of the building form and function, requiring further complex optimisation, to facilitate efficient all year round operation. Flawed BEMS configurations can then lead to ‘sick buildings’. Recently, building energy performance simulation (BEPS) has been viewed as a conceptual solution to assist in efficient building control. Building energy simulation models offer a virtual environment to test many scenarios of BEMS operation strategies and the ability to quickly evaluate their effects on energy consumption and occupant comfort. Challenges include having an accurate building model, but recent advances in building information modelling (BIM) offer the chance to leverage existing building data, which can be translated into a form understood by the building simulator. This study will address these challenges, by developing and integrating a BEMS, with a BIM for BEPS assisted predictive control, and assessing the outcome and potential of the integration

Rule-based integrated building management systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants

On flexibly integrating machine vision inspection systems in PCB manufacture

The objective of this research is to advance computer vision techniques and their applications in the electronics manufacturing industry. The research has been carried out with specific reference to the design of automatic optical inspection (AOI) systems and their role in the manufacture of printed circuit boards (PCBs). To achieve this objective, application areas of AOI systems in PCB manufacture have been examined. As a result, a requirement for enhanced performance characteristics has been identified and novel approaches and image processing algorithms have been evolved which can be used within next generation of AOI systems. The approaches are based on gaining an understanding of ways in which manufacturing information can be used to support AOI operations. Through providing information support, an AOI system has access to product models and associated information which can be used to enhance the execution of visual inspection tasks. Manufacturing systems integration, or more accurately controlled access to electronic information, is the key to the approaches. Also in the thesis methods are proposed to achieve the flexible integration of AOI systems (and computer vision systems in general) within their host PCB manufacturing environment. Furthermore, potential applications of information supported AOI systems at various stages of PCB manufacturing have been studied. It is envisaged that more efficient and cost-effective applications of AOI can be attained through adopting the flexible integration methods proposed, since AOI-generated information can now be accessed and utilized by other processes

A reference architecture for flexibly integrating machine vision within manufacturing

A reference architecture provides an overall framework that may embrace models, methodologies and mechanisms which can support the lifecycle of their target domain. The work described in this thesis makes a contribution to establishing such a generally applicable reference architecture for supporting the lifecycIe of a new generation of integrated machine vision systems. Contemporary machine vision systems consist of a complex combination of mechanical engineering, the hardware and software of an electronic processor, plus optical, sensory and lighting components. "This thesis is concerned with the structure of the software which characterises the system application. The machine vision systems which are currently used within manufacturing industry are difficult to integrate within the information systems required within modem manufacturing enterprises. They are inflexible in all but the execution of a range of similar operations, and their design and implementation is often such that they are difficult to update in the face of the required change inherent within modem manufacturing. The proposed reference architecture provides an overall framework within which a number of supporting models, design methodologies, and implementation mechanisms can combine to provide support for the rapid creation and maintenance of highly structured machine vision applications. These applications comprise modules which can be considered as building blocks of CIM systems. Their integrated interoperation can be enabled by the emerging infrastructural tools which will be required to underpin the next generation of flexibly integrated manufacturing systems. The work described in this thesis concludes that the issues of machine vision applications and the issues of integration of these applications within manufacturing systems are entirely separate. This separation is reflected in the structure of the thesis. PART B details vision application issues while PAIIT C deals with integration. The criteria for next generation integrated machine vision systems, derived in PART A of the thesis, are extensive. In order to address these criteria and propose a complete architecture, a "thin slice" is taken through the areas of vision application, and integration at the lifecycle stages of design, implementation, runtime and maintenance. The thesis describes the reference architecture, demonstrates its use though a proof of concept implementation and evaluates the support offered by the architecture for easing the problems of software change