IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

Transport Layer solution for bulk data transfers over Heterogeneous Long Fat Networks in Next Generation Networks

Aquesta tesi per compendi centra les seves contribucions en l'aprenentatge i innovació de les Xarxes de Nova Generació. És per això que es proposen diferents contribucions en diferents àmbits (Smart Cities, Smart Grids, Smart Campus, Smart Learning, Mitjana, eHealth, Indústria 4.0 entre d'altres) mitjançant l'aplicació i combinació de diferents disciplines (Internet of Things, Building Information Modeling, Cloud Storage, Ciberseguretat, Big Data, Internet de el Futur, Transformació Digital). Concretament, es detalla el monitoratge sostenible del confort a l'Smart Campus, la que potser es la meva aportació més representativa dins de la conceptualització de Xarxes de Nova Generació. Dins d'aquest innovador concepte de monitorització s'integren diferents disciplines, per poder oferir informació sobre el nivell de confort de les persones. Aquesta investigació demostra el llarg recorregut que hi ha en la transformació digital dels sectors tradicionals i les NGNs. Durant aquest llarg aprenentatge sobre les NGN a través de les diferents investigacions, es va poder observar una problemàtica que afectava de manera transversal als diferents camps d'aplicació de les NGNs i que aquesta podia tenir una afectació en aquests sectors. Aquesta problemàtica consisteix en el baix rendiment durant l'intercanvi de grans volums de dades sobre xarxes amb gran capacitat d'ample de banda i remotament separades geogràficament, conegudes com a xarxes elefant. Concretament, això afecta al cas d'ús d'intercanvi massiu de dades entre regions Cloud (Cloud Data Sharing use case). És per això que es va estudiar aquest cas d'ús i les diferents alternatives a nivell de protocols de transport,. S'estudien les diferents problemàtiques que pateixen els protocols i s'observa per què aquests no són capaços d'arribar a rendiments òptims. Deguda a aquesta situació, s'hipotetiza que la introducció de mecanismes que analitzen les mètriques de la xarxa i que exploten eficientment la capacitat de la mateixa milloren el rendiment dels protocols de transport sobre xarxes elefant heterogènies durant l'enviament massiu de dades. Primerament, es dissenya l’Adaptative and Aggressive Transport Protocol (AATP), un protocol de transport adaptatiu i eficient amb l'objectiu de millorar el rendiment sobre aquest tipus de xarxes elefant. El protocol AATP s'implementa i es prova en un simulador de xarxes i un testbed sota diferents situacions i condicions per la seva validació. Implementat i provat amb èxit el protocol AATP, es decideix millorar el propi protocol, Enhanced-AATP, sobre xarxes elefant heterogènies. Per això, es dissenya un mecanisme basat en el Jitter Ràtio que permet fer aquesta diferenciació. A més, per tal de millorar el comportament del protocol, s’adapta el seu sistema de fairness per al repartiment just dels recursos amb altres fluxos Enhanced-AATP. Aquesta evolució s'implementa en el simulador de xarxes i es realitzen una sèrie de proves. A l'acabar aquesta tesi, es conclou que les Xarxes de Nova Generació tenen molt recorregut i moltes coses a millorar causa de la transformació digital de la societat i de l'aparició de nova tecnologia disruptiva. A més, es confirma que la introducció de mecanismes específics en la concepció i operació dels protocols de transport millora el rendiment d'aquests sobre xarxes elefant heterogènies.Esta tesis por compendio centra sus contribuciones en el aprendizaje e innovación de las Redes de Nueva Generación. Es por ello que se proponen distintas contribuciones en diferentes ámbitos (Smart Cities, Smart Grids, Smart Campus, Smart Learning, Media, eHealth, Industria 4.0 entre otros) mediante la aplicación y combinación de diferentes disciplinas (Internet of Things, Building Information Modeling, Cloud Storage, Ciberseguridad, Big Data, Internet del Futuro, Transformación Digital). Concretamente, se detalla la monitorización sostenible del confort en el Smart Campus, la que se podría considerar mi aportación más representativa dentro de la conceptualización de Redes de Nueva Generación. Dentro de este innovador concepto de monitorización se integran diferentes disciplinas, para poder ofrecer información sobre el nivel de confort de las personas. Esta investigación demuestra el recorrido que existe en la transformación digital de los sectores tradicionales y las NGNs. Durante este largo aprendizaje sobre las NGN a través de las diferentes investigaciones, se pudo observar una problemática que afectaba de manera transversal a los diferentes campos de aplicación de las NGNs y que ésta podía tener una afectación en estos sectores. Esta problemática consiste en el bajo rendimiento durante el intercambio de grandes volúmenes de datos sobre redes con gran capacidad de ancho de banda y remotamente separadas geográficamente, conocidas como redes elefante, o Long Fat Networks (LFNs). Concretamente, esto afecta al caso de uso de intercambio de datos entre regiones Cloud (Cloud Data Data use case). Es por ello que se estudió este caso de uso y las diferentes alternativas a nivel de protocolos de transporte. Se estudian las diferentes problemáticas que sufren los protocolos y se observa por qué no son capaces de alcanzar rendimientos óptimos. Debida a esta situación, se hipotetiza que la introducción de mecanismos que analizan las métricas de la red y que explotan eficientemente la capacidad de la misma mejoran el rendimiento de los protocolos de transporte sobre redes elefante heterogéneas durante el envío masivo de datos. Primeramente, se diseña el Adaptative and Aggressive Transport Protocol (AATP), un protocolo de transporte adaptativo y eficiente con el objetivo maximizar el rendimiento sobre este tipo de redes elefante. El protocolo AATP se implementa y se prueba en un simulador de redes y un testbed bajo diferentes situaciones y condiciones para su validación. Implementado y probado con éxito el protocolo AATP, se decide mejorar el propio protocolo, Enhanced-AATP, sobre redes elefante heterogéneas. Además, con tal de mejorar el comportamiento del protocolo, se mejora su sistema de fairness para el reparto justo de los recursos con otros flujos Enhanced-AATP. Esta evolución se implementa en el simulador de redes y se realizan una serie de pruebas. Al finalizar esta tesis, se concluye que las Redes de Nueva Generación tienen mucho recorrido y muchas cosas a mejorar debido a la transformación digital de la sociedad y a la aparición de nueva tecnología disruptiva. Se confirma que la introducción de mecanismos específicos en la concepción y operación de los protocolos de transporte mejora el rendimiento de estos sobre redes elefante heterogéneas.This compendium thesis focuses its contributions on the learning and innovation of the New Generation Networks. That is why different contributions are proposed in different areas (Smart Cities, Smart Grids, Smart Campus, Smart Learning, Media, eHealth, Industry 4.0, among others) through the application and combination of different disciplines (Internet of Things, Building Information Modeling, Cloud Storage, Cybersecurity, Big Data, Future Internet, Digital Transformation). Specifically, the sustainable comfort monitoring in the Smart Campus is detailed, which can be considered my most representative contribution within the conceptualization of New Generation Networks. Within this innovative monitoring concept, different disciplines are integrated in order to offer information on people's comfort levels. . This research demonstrates the long journey that exists in the digital transformation of traditional sectors and New Generation Networks. During this long learning about the NGNs through the different investigations, it was possible to observe a problematic that affected the different application fields of the NGNs in a transversal way and that, depending on the service and its requirements, it could have a critical impact on any of these sectors. This issue consists of a low performance operation during the exchange of large volumes of data on networks with high bandwidth capacity and remotely geographically separated, also known as Elephant networks, or Long Fat Networks (LFNs). Specifically, this critically affects the Cloud Data Sharing use case. That is why this use case and the different alternatives at the transport protocol level were studied. For this reason, the performance and operation problems suffered by layer 4 protocols are studied and it is observed why these traditional protocols are not capable of achieving optimal performance. Due to this situation, it is hypothesized that the introduction of mechanisms that analyze network metrics and efficiently exploit network’s capacity meliorates the performance of Transport Layer protocols over Heterogeneous Long Fat Networks during bulk data transfers. First, the Adaptive and Aggressive Transport Protocol (AATP) is designed. An adaptive and efficient transport protocol with the aim of maximizing its performance over this type of elephant network.. The AATP protocol is implemented and tested in a network simulator and a testbed under different situations and conditions for its validation. Once the AATP protocol was designed, implemented and tested successfully, it was decided to improve the protocol itself, Enhanced-AATP, to improve its performance over heterogeneous elephant networks. In addition, in order to upgrade the behavior of the protocol, its fairness system is improved for the fair distribution of resources among other Enhanced-AATP flows. Finally, this evolution is implemented in the network simulator and a set of tests are carried out. At the end of this thesis, it is concluded that the New Generation Networks have a long way to go and many things to improve due to the digital transformation of society and the appearance of brand-new disruptive technology. Furthermore, it is confirmed that the introduction of specific mechanisms in the conception and operation of transport protocols improves their performance on Heterogeneous Long Fat Networks

Development of a new robust hybrid automata algorithm based on surface electromyography (SEMG) signal for instrumented wheelchair control

Instrumented wheelchair operates based on surface electromyography (sEMG) is one of alternative to assist impairment person for mobility. SEMG is chosen due to good in accuracy and easier preparation to place the electrodes. Motor neuron transmit electrical potential to muscle fibre to perform isometric, concentric or eccentric contraction. These electrical changes that is called Motor Unit Action Potential (MUAP) can be acquired and amplified by electrodes located on targeted muscles changes can be recorded and analysed using sEMG devices. But, sEMG device cost up to USD 2,100 for a sEMG data acquisition device that available on market is one of the drawback to be used by impairment person that most of them has financial problem due to unable to work like before. In addition, it is a closed source system that cannot be modified to improve the accuracy and adding more features. Open source system such as Arduino has limitation of specifications that makes able to apply nonpattern recognition control methods which is simpler and easier compared to pattern recognition. However, classification accuracy is lower than pattern recognition and it cannot be applied to higher number participants from different background and gender. This research aims are to develop an open-source Arduino based sEMG data acquisition device by formulating hybrid automata algorithm to differentiate MUAP activity during wheelchair propulsion. Addition of hybrid automata algorithm to run pattern and non-pattern recognition based control methods is an advantage to increase accuracy in differentiating forward stroke or hand return activity. Electrodes are placed on Biceps (BIC), Triceps (TRI), Extensor (EXT), Flexor (FIX) and MUAP activity recorded for 30 healthy persons. Then, experiment result was validated with simulation result using OpenSim biomedical modelling software. Mean, standard deviation (SD), confidence interval (CI) and maximum point different (MPD) of MUAP were calculated and to be used as thresholds for non-pattern recognition control method in method selection experiment. Meanwhile, pattern recognition is using Probability Density Function (PDF) to determine MUAP according to type of activities. Total of ten control methods determined from population and individual data were tested against another 10 healthy persons to evaluate the algorithm performance. Assessment of each control method done by misclassification matrix looking at True Positive (TP) and False Negative (FN) of power assist system activation period. Developed sEMG data acquisition device that is operated by Arduino MEGA 2560 and Myoware muscle sensors with sampling rate of above 400Hz successfully recorded MUAP from four arm muscles. Furthermore, 2.5 ms of average data latency for device to record, analyse, validate and creating commands to activate the power assist system. Data obtained from the device shows that most active muscle during wheelchair propulsion is TRI, followed by BIC and matched to OpenSim simulation result. In method selection experiment, 96.28% of average accuracy was achieved and different control methods were selected by misclassification matrix for each of persons. This method would be a control method to activate power assist system and selected based on conditions set in the algorithm. These findings indicated that open source Arduino board is capable of running real time pattern, non-pattern recognition based control methods by producing classification accuracy up to 99.48% even though it is known as just a microcontroller that has limitation to run complex classifiers. At the same time, a device that cost less than USD200 has 400Hz of sampling rate is as good as closed source device that is come with expensive price tag to own it. Based on algorithm evaluation, it shows that one control method couldn’t fit to all persons as per proven in method selection experiment. Different person has different control method that suit them the most. Lastly, BIC and TRI can be reference muscles to activate assistive device in instrumented wheelchair that is using propulsion as indication

Factories of the Future

Engineering; Industrial engineering; Production engineerin

Tecnología open-hardware para la parametrización ambiental en aplicaciones de Ingeniería

El desarrollo del open-hardware ha proporcionado una nueva herramienta para mejorar el sistema de monitorización de otros sistemas. En la actualidad la sociedad está inmersa en una implementación de sistemas capaces de proporcionar una gran cantidad de datos del entorno, los cuales nos ayudan al estudio y entendimiento de este. En esta tesis doctoral se estudia la capacidad de estas plataformas libres en tres campos como son el Agronómico , Conservación Cultural e Industrial. Se ha procedido a una comprobación y una validación posterior en cada uno de los campos anteriormente citados. Los resultados obtenidos han sido positivos, siendo similares a los obtenidos por equipos comerciales. Esto nos lleva a la conclusión de que estas nuevas plataformas son el futuro, no sólo por su menor precio, sino por su gran capacidad de modificación y adaptación que las plataformas comerciales cerradas no permiten.The development of open hardware has provided new tool to improve system to monitor other system. Nowadays the society is immersed in an implementation of systems provide a large amount of data of the environment, which help us to study and understand it. This doctoral thesis studies the capacity of these free platforms in three differents scenarios as Agronomic, Cultural Heritage and Industrial. A pre-selection of the components and a subsequent verification and validation has been carried out in each of the aforementioned fields. The result obtained have been positives, being similar to those obtained by commercial equipment. This leads us to the conclusion that these new platforms are the future, not only for its lower price, but for its great capacity of modification and adaptation that the closed commercial platforms do not allow

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Computer Science & Technology Series : XXI Argentine Congress of Computer Science. Selected papers

CACIC’15 was the 21thCongress in the CACIC series. It was organized by the School of Technology at the UNNOBA (North-West of Buenos Aires National University) in Junín, Buenos Aires. The Congress included 13 Workshops with 131 accepted papers, 4 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 6 courses. CACIC 2015 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 202 submissions. An average of 2.5 review reports werecollected for each paper, for a grand total of 495 review reports that involved about 191 different reviewers. A total of 131 full papers, involving 404 authors and 75 Universities, were accepted and 24 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI