an open and modular hardware node for wireless sensor and body area networks

Health monitoring is nowadays one of the hottest markets due to the increasing interest in prevention and treatment of physical problems. In this context the development of wearable, wireless, open-source, and nonintrusive sensing solutions is still an open problem. Indeed, most of the existing commercial architectures are closed and provide little flexibility. In this paper, an open hardware architecture for designing a modular wireless sensor node for health monitoring is proposed. By separating the connection and sensing functions in two separate boards, compliant with the IEEE1451 standard, we add plug and play capabilities to analog transducers, while granting at the same time a high level of customization. As an additional contribution of the work, we developed a cosimulation tool which simplifies the physical connection with the hardware devices and provides support for complex systems. Finally, a wireless body area network for fall detection and health monitoring, based on wireless node prototypes realized according to the proposed architecture, is presented as an application scenario

A Semantic Interoperability Model Based on the IEEE 1451 Family of Standards Applied to the Industry 4.0

The Internet of Things (IoT) has been growing recently. It is a concept for connecting billions of smart devices through the Internet in different scenarios. One area being developed inside the IoT in industrial automation, which covers Machine-to-Machine (M2M) and industrial communications with an automatic process, emerging the Industrial Internet of Things (IIoT) concept. Inside the IIoT is developing the concept of Industry 4.0 (I4.0). That represents the fourth industrial revolution and addresses the use of Internet technologies to improve the production efficiency of intelligent services in smart factories. I4.0 is composed of a combination of objects from the physical world and the digital world that offers dedicated functionality and flexibility inside and outside of an I4.0 network. The I4.0 is composed mainly of Cyber-Physical Systems (CPS). The CPS is the integration of the physical world and its digital world, i.e., the Digital Twin (DT). It is responsible for realising the intelligent cross-link application, which operates in a self-organised and decentralised manner, used by smart factories for value creation. An area where the CPS can be implemented in manufacturing production is developing the Cyber-Physical Production System (CPPS) concept. CPPS is the implementation of Industry 4.0 and CPS in manufacturing and production, crossing all levels of production between the autonomous and cooperative elements and sub-systems. It is responsible for connecting the virtual space with the physical world, allowing the smart factories to be more intelligent, resulting in better and smart production conditions, increasing productivity, production efficiency, and product quality. The big issue is connecting smart devices with different standards and protocols. About 40% of the benefits of the IoT cannot be achieved without interoperability. This thesis is focused on promoting the interoperability of smart devices (sensors and actuators) inside the IIoT under the I4.0 context. The IEEE 1451 is a family of standards developed to manage transducers. This standard reaches the syntactic level of interoperability inside Industry 4.0. However, Industry 4.0 requires a semantic level of communication not to exchange data ambiguously. A new semantic layer is proposed in this thesis allowing the IEEE 1451 standard to be a complete framework for communication inside the Industry 4.0 to provide an interoperable network interface with users and applications to collect and share the data from the industry field.A Internet das Coisas tem vindo a crescer recentemente. É um conceito que permite conectar bilhões de dispositivos inteligentes através da Internet em diferentes cenários. Uma área que está sendo desenvolvida dentro da Internet das Coisas é a automação industrial, que abrange a comunicação máquina com máquina no processo industrial de forma automática. Essa interligação, representa o conceito da Internet das Coisas Industrial. Dentro da Internet das Coisas Industrial está a desenvolver o conceito de Indústria 4.0 (I4.0). Isso representa a quarta revolução industrial que aborda o uso de tecnologias utilizadas na Internet para melhorar a eficiência da produção de serviços em fábricas inteligentes. A Indústria 4.0 é composta por uma combinação de objetos do mundo físico e do mundo da digital que oferece funcionalidade dedicada e flexibilidade dentro e fora de uma rede da Indústria 4.0. O I4.0 é composto principalmente por Sistemas Ciberfísicos. Os Sistemas Ciberfísicos permitem a integração do mundo físico com seu representante no mundo digital, por meio do Gémeo Digital. Sistemas Ciberfísicos são responsáveis por realizar a aplicação inteligente da ligação cruzada, que opera de forma auto-organizada e descentralizada, utilizada por fábricas inteligentes para criação de valor. Uma área em que o Sistema Ciberfísicos pode ser implementado na produção manufatureira, isso representa o desenvolvimento do conceito Sistemas de Produção Ciberfísicos. Esse sistema é a implementação da Indústria 4.0 e Sistema Ciberfísicos na fabricação e produção. A cruzar todos os níveis desde a produção entre os elementos e subsistemas autónomos e cooperativos. Ele é responsável por conectar o espaço virtual com o mundo físico, permitindo que as fábricas inteligentes sejam mais inteligentes, resultando em condições de produção melhores e inteligentes, aumentando a produtividade, a eficiência da produção e a qualidade do produto. A grande questão é como conectar dispositivos inteligentes com diferentes normas e protocolos. Cerca de 40% dos benefícios da Internet das Coisas não podem ser alcançados sem interoperabilidade. Esta tese está focada em promover a interoperabilidade de dispositivos inteligentes (sensores e atuadores) dentro da Internet das Coisas Industrial no contexto da Indústria 4.0. O IEEE 1451 é uma família de normas desenvolvidos para gerenciar transdutores. Esta norma alcança o nível sintático de interoperabilidade dentro de uma indústria 4.0. No entanto, a Indústria 4.0 requer um nível semântico de comunicação para não haver a trocar dados de forma ambígua. Uma nova camada semântica é proposta nesta tese permitindo que a família de normas IEEE 1451 seja um framework completo para comunicação dentro da Indústria 4.0. Permitindo fornecer uma interface de rede interoperável com utilizadores e aplicações para recolher e compartilhar os dados dentro de um ambiente industrial.This thesis was developed at the Measurement and Instrumentation Laboratory (IML) in the University of Beira Interior and supported by the portuguese project INDTECH 4.0 – Novas tecnologias para fabricação, que tem como objetivo geral a conceção e desenvolvimento de tecnologias inovadoras no contexto da Indústria 4.0/Factories of the Future (FoF), under the number POCI-01-0247-FEDER-026653

Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS

Implementation and analysis of the ISO/IEC/IEEE P21451-1 draft standard for a smart transducer interface common network services and its applications in the Internet of Things

The Internet of Things (IoT) has rapidly become the paradigm for the creation and improvement of new and old Cyber Physical Systems (CPS), but how much longer can this development of IoT devices, networks, and services be sustained? The past decade has seen incredible growth in internet connected devices, with current estimates placing the number of such devices at about 20 billion in 2017, not including personal computers, smart phones, and tablets. This has created a massive market for these devices, with each company making their own applications, protocols, and services. Since these markets are competitive, there originally was no incentive to design systems, which were built to have a common protocol to enable interoperability between systems. This can pose a large integration effort if two or more of these systems need to communicate together as part of a larger system. The problem is compounded if these systems utilize two different physical layers or talk using two different protocols. The revitalization of the IEEE 1451 family of standards can solve this problem. The work in this thesis proposes to solve the integration problem by providing a common set of services and protocols for devices. This work provides the basis for a common architectural foundation for future IoT development. The contributions of this thesis include a renewal of the language and intent of the IEEE P21451-1 draft standard, development of example implementations to be included in the standard, and the development of Open Source hardware and software aimed at lowering the cost of adopting this standard

Internet of Things-aided Smart Grid: Technologies, Architectures, Applications, Prototypes, and Future Research Directions

Traditional power grids are being transformed into Smart Grids (SGs) to address the issues in existing power system due to uni-directional information flow, energy wastage, growing energy demand, reliability and security. SGs offer bi-directional energy flow between service providers and consumers, involving power generation, transmission, distribution and utilization systems. SGs employ various devices for the monitoring, analysis and control of the grid, deployed at power plants, distribution centers and in consumers' premises in a very large number. Hence, an SG requires connectivity, automation and the tracking of such devices. This is achieved with the help of Internet of Things (IoT). IoT helps SG systems to support various network functions throughout the generation, transmission, distribution and consumption of energy by incorporating IoT devices (such as sensors, actuators and smart meters), as well as by providing the connectivity, automation and tracking for such devices. In this paper, we provide a comprehensive survey on IoT-aided SG systems, which includes the existing architectures, applications and prototypes of IoT-aided SG systems. This survey also highlights the open issues, challenges and future research directions for IoT-aided SG systems

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.Comment: 32 page

Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments