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

Optical Wireless Data Center Networks

Bandwidth and computation-intensive Big Data applications in disciplines like social media, bio- and nano-informatics, Internet-of-Things (IoT), and real-time analytics, are pushing existing access and core (backbone) networks as well as Data Center Networks (DCNs) to their limits. Next generation DCNs must support continuously increasing network traffic while satisfying minimum performance requirements of latency, reliability, flexibility and scalability. Therefore, a larger number of cables (i.e., copper-cables and fiber optics) may be required in conventional wired DCNs. In addition to limiting the possible topologies, large number of cables may result into design and development problems related to wire ducting and maintenance, heat dissipation, and power consumption. To address the cabling complexity in wired DCNs, we propose OWCells, a class of optical wireless cellular data center network architectures in which fixed line of sight (LOS) optical wireless communication (OWC) links are used to connect the racks arranged in regular polygonal topologies. We present the OWCell DCN architecture, develop its theoretical underpinnings, and investigate routing protocols and OWC transceiver design. To realize a fully wireless DCN, servers in racks must also be connected using OWC links. There is, however, a difficulty of connecting multiple adjacent network components, such as servers in a rack, using point-to-point LOS links. To overcome this problem, we propose and validate the feasibility of an FSO-Bus to connect multiple adjacent network components using NLOS point-to-point OWC links. Finally, to complete the design of the OWC transceiver, we develop a new class of strictly and rearrangeably non-blocking multicast optical switches in which multicast is performed efficiently at the physical optical (lower) layer rather than upper layers (e.g., application layer). Advisors: Jitender S. Deogun and Dennis R. Alexande

A patient agent controlled customized blockchain based framework for internet of things

Although Blockchain implementations have emerged as revolutionary technologies for various industrial applications including cryptocurrencies, they have not been widely deployed to store data streaming from sensors to remote servers in architectures known as Internet of Things. New Blockchain for the Internet of Things models promise secure solutions for eHealth, smart cities, and other applications. These models pave the way for continuous monitoring of patient’s physiological signs with wearable sensors to augment traditional medical practice without recourse to storing data with a trusted authority. However, existing Blockchain algorithms cannot accommodate the huge volumes, security, and privacy requirements of health data. In this thesis, our first contribution is an End-to-End secure eHealth architecture that introduces an intelligent Patient Centric Agent. The Patient Centric Agent executing on dedicated hardware manages the storage and access of streams of sensors generated health data, into a customized Blockchain and other less secure repositories. As IoT devices cannot host Blockchain technology due to their limited memory, power, and computational resources, the Patient Centric Agent coordinates and communicates with a private customized Blockchain on behalf of the wearable devices. While the adoption of a Patient Centric Agent offers solutions for addressing continuous monitoring of patients’ health, dealing with storage, data privacy and network security issues, the architecture is vulnerable to Denial of Services(DoS) and single point of failure attacks. To address this issue, we advance a second contribution; a decentralised eHealth system in which the Patient Centric Agent is replicated at three levels: Sensing Layer, NEAR Processing Layer and FAR Processing Layer. The functionalities of the Patient Centric Agent are customized to manage the tasks of the three levels. Simulations confirm protection of the architecture against DoS attacks. Few patients require all their health data to be stored in Blockchain repositories but instead need to select an appropriate storage medium for each chunk of data by matching their personal needs and preferences with features of candidate storage mediums. Motivated by this context, we advance third contribution; a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The mapping between health data features and characteristics of each repository is learned using machine learning. The Blockchain’s capacity to make transactions and store records without central oversight enables its application for IoT networks outside health such as underwater IoT networks where the unattended nature of the nodes threatens their security and privacy. However, underwater IoT differs from ground IoT as acoustics signals are the communication media leading to high propagation delays, high error rates exacerbated by turbulent water currents. Our fourth contribution is a customized Blockchain leveraged framework with the model of Patient-Centric Agent renamed as Smart Agent for securely monitoring underwater IoT. Finally, the smart Agent has been investigated in developing an IoT smart home or cities monitoring framework. The key algorithms underpinning to each contribution have been implemented and analysed using simulators.Doctor of Philosoph

100% Renewable Energy Transition: Pathways and Implementation

Energy markets are already undergoing considerable transitions to accommodate new (renewable) energy forms, new (decentral) energy players, and new system requirements, e.g. flexibility and resilience. Traditional energy markets for fossil fuels are therefore under pressure, while not-yet-mature (renewable) energy markets are emerging. As a consequence, investments in large-scale and capital intensive (traditional) energy production projects are surrounded by high uncertainty, and are difficult to hedge by private entities. Traditional energy production companies are transforming into energy service suppliers and companies aggregating numerous potential market players are emerging, while regulation and system management are playing an increasing role. To address these increasing uncertainties and complexities, economic analysis, forecasting, modeling and investment assessment require fresh approaches and views. Novel research is thus required to simulate multiple actor interplays and idiosyncratic behavior. The required approaches cannot deal only with energy supply, but need to include active demand and cover systemic aspects. Energy market transitions challenge policy-making. Market coordination failure, the removal of barriers hindering restructuring and the combination of market signals with command-and-control policy measures are some of the new aims of policies.The aim of this Special Issue is to collect research papers that address the above issues using novel methods from any adequate perspective, including economic analysis, modeling of systems, behavioral forecasting, and policy assessment.The issue will include, but is not be limited to: Local control schemes and algorithms for distributed generation systems; Centralized and decentralized sustainable energy management strategies; Communication architectures, protocols and properties of practical applications; Topologies of distributed generation systems improving flexibility, efficiency and power quality; Practical issues in the control design and implementation of distributed generation systems; Energy transition studies for optimized pathway options aiming for high levels of sustainabilit

Real-time acoustic event classification in urban environments using low-cost devices

En la societat moderna i en constant evolució, la presència de soroll s'ha convertit en un perill diari per a una quantitat preocupant de la població. Estar sobreexposats a alts nivells de soroll pot interferir en activitats quotidianes i podria causar greus efectes secundaris en termes de salut com mal humor, deteriorament cognitiu en nens o malalties cardiovasculars. Hi ha estudis que assenyalen que no només afecta el nivell de soroll al qual estan exposats els ciutadans, sinó que també és important el tipus de so. Així doncs, no tots els esdeveniments acústics tenen el mateix impacte en la població. Amb les tecnologies que es fan servir actualment per a monitorar la contaminació acústica, és difícil identificar automàticament quins sorolls estan més presents en les zones més contaminades. De fet, per avaluar les queixes dels ciutadans, normalment s'envien tècnics a la zona on s'hi ha produït la queixa per avaluar si aquesta és rellevant. A causa de l'elevat nombre de queixes que es generen diàriament (especialment en zones molt poblades), el desenvolupament de Xarxes de Sensors Acústics Sense Fils (WASN) que monitorin automàticament la contaminació acústica d'una zona s'ha convertit en una tendència d'investigació. En l'actualitat, la majoria de les xarxes desplegades en entorns urbans només mesuren el nivell de soroll equivalent fent servir equipaments cars i precisos però no permeten d'identificar les fonts de soroll presents a cada lloc. Donat l'elevat cost dels sensors, aquests solen col·locar-se en llocs estratègics, però no monitoren zones àmplies. L'objectiu d'aquesta tesi és abordar un important repte que encara està latent en aquest camp: monitorar acústicament zones de gran envergadura en temps real i de forma escalable i econòmica. En aquest sentit, s'ha seleccionat el centre de la ciutat de Barcelona com a cas d'ús de referència per a dur a terme aquesta investigació. En primer lloc, aquesta tesi parteix d'una anàlisi precís d'un conjunt de 6 hores de dades anotades corresponents al paisatge sonor d'una zona concreta de la ciutat (l'Eixample). Després, es presenta una arquitectura distribuïda escalable que fa servir dispositius de baix cost per a reconèixer esdeveniments acústics. Per validar la viabilitat d'aquest enfocament, s'ha implementat un algorisme d'aprenentatge profund que s'executa sobre aquesta arquitectura per a classificar 10 categories acústiques diferents. Com que els nodes del sistema proposats estan disposats en una topologia amb redundància física (més d'un node pot escoltar el mateix esdeveniment acústic simultàniament), s'han recollit dades en quatre punts del centre de Barcelona respectant l'arquitectura dels sensors. Per últim, donat que els esdeveniments del món real tendeixen a produir-se simultàniament, s'ha millorat l'algorisme d'aprenentatge profund perquè suporti la classificació multietiqueta (és a dir, polifònica). Els resultats mostren que, amb l'arquitectura del sistema proposat, és possible classificat esdeveniments acústic en temps real. En general, les contribucions d'aquesta investigació són les següents: (1) el disseny d'una WASN de baix cost i escalable, que pugui monitorar àrees a gran escala i (2) el desenvolupament d'un algorisme de classificació en temps real executat sobre els nodes de detecció dissenyats.En la sociedad moderna y en constante evolución, la presencia de ruido se ha convertido en una amenaza diaria para una cantidad preocupante de la población. Estar sobreexpuesto a altos niveles de ruido puede interferir en las actividades cotidianas y podría acarrear graves efectos secundarios en términos de salud como mal humor, deterioro cognitivo en niños o enfermedades cardiovasculares. Hay estudios que señalan que no solo afecta el nivel de ruido al que están expuestos los ciudadanos: también es importante el tipo de sonido. Es decir, no todos los eventos acústicos tienen el mismo impacto en la población. Con las tecnologías que se utilizan actualmente para monitorizar la contaminación acústica, es difícil identificar automáticamente qué sonidos están más presentes en las zonas más contaminadas. De hecho, para evaluar las quejas de los ciudadanos, normalmente se envían técnicos a la zona donde se ha realizado la queja para evaluar si ésta es relevante. Debido al elevado número de quejas que se generan diariamente (especialmente en zonas muy pobladas), el desarrollo de Redes de Sensores Acústicos Inalámbricos (WASN) que monitoricen automáticamente la contaminación acústica se ha convertido en una tendencia de investigación. Actualmente, la mayoría de redes desplegadas en entornos urbanos solo miden el nivel de ruido equivalente mediante equipos caros y precisos, pero no son capaces de identificar las fuentes de ruido presentes en cada lugar. Dado el elevado precio de estos sensores, los nodos suelen colocarse en lugares estratégicos, pero no monitorizan zonas amplias. El objetivo de esta tesis es abordar un importante reto aún latente en este campo: monitorizar acústicamente zonas de gran tamaño en tiempo real y de forma escalable y económica. En este sentido, se ha seleccionado la ciudad de Barcelona como caso de uso para llevar a cabo esta investigación. Primeramente, esta tesis parte de un análisis preciso de un conjunto de 6 horas de datos anotados correspondientes al paisaje sonoro de una zona concreta de la ciudad (l'Eixample). Después, se presenta una arquitectura distribuida escalable que utiliza dispositivos de bajo coste para reconocer eventos acústicos. Para validar la viabilidad del enfoque, se ha implementado un algoritmo de aprendizaje profundo ejecutado sobre esta arquitectura para clasificar 10 categorías acústicas diferentes. Como los nodos del sistema propuesto están dispuestos en una topología con redundancia física (más de un nodo puede escuchar el mismo evento acústico a la vez), se han recogido datos en cuatro puntos del centro de Barcelona respetando la arquitectura de los sensores. Por último, dado que los eventos del mundo real tienden a producirse simultáneamente, se ha mejorado el algoritmo de aprendizaje profundo para que soporte la clasificación multietiqueta (polifónica). Los resultados muestran que, con la arquitectura del sistema propuesto, es posible clasificar eventos acústicos en tiempo real. En general, las contribuciones de esta investigación son las siguientes (1) diseño de una WASN de bajo coste y escalable, capaz de monitorizar áreas a gran escala y (2) desarrollo de un algoritmo de clasificación en tiempo real ejecutado sobre los nodos de detección diseñados.In the modern and ever-evolving society, the presence of noise has become a daily threat to a worrying amount of the population. Being overexposed to high levels of noise may interfere with day-to-day activities and, thus, could potentially bring severe side-effects in terms of health such as annoyance, cognitive impairment in children or cardiovascular diseases. Some studies point out that it is not only the level of noise that matters but also the type of sound that the citizens are exposed to. That is, not all the acoustic events have the same impact on the population. With current technologies used to track noise levels, for both private and public administrations, it is hard to automatically identify which sounds are more present in most polluted areas. Actually, to assess citizen complaints, technicians are typically sent to the area to be surveyed to evaluate if the complaint is relevant. Due to the high number of complaints that are generated every day (specially in highly populated areas), the development of Wireless Acoustic Sensor Networks (WASN) that would automatically monitor the noise pollution of a certain area have become a research trend. Currently, most of the networks that are deployed in cities measure only the equivalent noise level by means of expensive but highly accurate hardware but cannot identify the noise sources that are present in each spot. Given the elevated price of these sensors, nodes are typically placed in specific locations, but do not monitor wide areas. The purpose of this thesis is to address an important challenge still latent in this field: to acoustically monitor large-scale areas in real-time and in a scalable and cost efficient way. In this regard, the city centre of Barcelona has been selected as a reference use-case scenario to conduct this research. First, this dissertation starts with an accurate analysis of an annotated dataset of 6 hours corresponding to the soundscape of a specific area of the city (l’Eixample). Next, a scalable distributed architecture using low-cost computing devices to recognize acoustic events is presented. To validate the feasibility of this approach, a deep learning algorithm running on top of this architecture has been implemented to classify 10 different acoustic categories. As the sensing nodes of the proposed system are arranged in such a way that it is possible to take advantage of physical redundancy (that is, more than one node may hear the same acoustic event), data has been gathered in four spots of the city centre of Barcelona respecting the sensors topology. Finally, as real-world events tend to occur simultaneously, the deep learning algorithm has been enhanced to support multilabel (i.e., polyphonic) classification. Results show that, with the proposed system architecture, it is possible to classify acoustic events in real-time. Overall, the contributions of this research are the following: (1) the design of a low-cost, scalable WASN able to monitor large-scale areas and (2) the development of a real-time classification algorithm able to run over the designed sensing nodes