Sensae Console - Platforma de support para serviços baseados em IoT

Today there are more smart devices than people. The number of devices worldwide is forecast to almost triple from 8.74 billion in 2020 to more than 25.4 billion devices in 2030. The Internet of Things (IoT) is the connection of millions of smart devices and sensors connected to the Internet. These connected devices and sensors collect and share data for use and analysis by many organizations. Some examples of intelligent connected sensors are: GPS asset tracking, parking spots, refrigerator thermostats, soil condition and many others. The limit of different objects that can become intelligent sensors is limited only by our imagination. But these devices are mostly useless without a platform to analyze, store and present the aggregated data into business-oriented information. Recently, several platforms have emerged to address this need and help companies/governments to increase efficiency, cut on operational costs and improve safety. Sadly, most of these platforms are tailor made for the devices that the company offers. This dissertation presents the (Sensae Console) platform that enables and promotes the development of IoT-based business-oriented applications. This platform attempts to be device-neutral, IoT middleware-neutral and provide flexible upstream integration and hosting options while providing a simple and concise data streaming Application Programming Interface (API). Three IoT-based business-oriented applications built on top of the Sensae Console platform are presented as Proof of Concept (PoC) of its capabilities.Atualmente, existem mais sensores inteligentes do que pessoas. O número de sensores em todo o mundo deve quase triplicar de 8,74 bilhões em 2020 para mais de 25,4 bilhões em 2030. O conceito de IoT está relacionado com a interação entre milhões de dispositivos inteligentes através da Internet. Estes dispositivos e sensores conectados recolhem e disponibilizam dados para uso e análise por parte de muitas organizações. Alguns exemplos de sensores inteligentes e seus usos são: dispositivos GPS para rastreamento de ativos, monitorização de vagas de estacionamento, termostatos em arcas frigoríficas, condição do solo e muitos outros. O número de diferentes objetos que podem vir-se a tornar sensores inteligentes é limitado apenas pela nossa imaginação. Mas estes dispositivos são praticamente inúteis sem uma plataforma para analisar, armazenar e apresentar os dados agregados em informação relevante para o negócio em questão. Recentemente, várias plataformas surgiram para responder a essa necessidade e ajudar empresas/governos a aumentar a sua eficiência, reduzir custos operacionais e melhorar a segurança dos espaços e negócios. Infelizmente, a maioria dessas plataformas é feita à medida para os dispositivos que a empresa em questão oferece. Esta tese apresenta uma plataforma (Sensae Console) focada em que propiciar a criação de aplicações relacionados com IoT para negócios específicos. Esta plataforma procura ser agnóstica em relação aos dispositivos inteligentes e middleware de IoT usados por terceiros, oferece variadas e flexíveis opções de integração e hosting como também uma API de streaming simples e concisa. Três aplicações relacionadas com IoT, orientadas ao seu negócio e construídas com base na plataforma Sensae Console são apresentadas como provas de conceito das capacidades da plataforma

Consuming data sources to generate actionable items

Plataforma que consumeixi sensors IoT i sistemes d'alertes per a generar accions de resposta relacionades amb els sistemes d'alerta. Per a demostrar els casos d'ús possibles s'incorporaran funcions requerides per Projectes Europeus, solucions comercials i solucions compatibles amb estàndards

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

Video, as a key driver in the global explosion of digital information, can create tremendous benefits for human society. Governments and enterprises are deploying innumerable cameras for a variety of applications, e.g., law enforcement, emergency management, traffic control, and security surveillance, all facilitated by video analytics (VA). This trend is spurred by the rapid advancement of deep learning (DL), which enables more precise models for object classification, detection, and tracking. Meanwhile, with the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud. Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution. The resulting new intersection, edge video analytics (EVA), begins to attract widespread attention. Nevertheless, only a few loosely-related surveys exist on this topic. The basic concepts of EVA (e.g., definition, architectures) were not fully elucidated due to the rapid development of this domain. To fill these gaps, we provide a comprehensive survey of the recent efforts on EVA. In this paper, we first review the fundamentals of edge computing, followed by an overview of VA. The EVA system and its enabling techniques are discussed next. In addition, we introduce prevalent frameworks and datasets to aid future researchers in the development of EVA systems. Finally, we discuss existing challenges and foresee future research directions. We believe this survey will help readers comprehend the relationship between VA and edge computing, and spark new ideas on EVA.Comment: 31 pages, 13 figure

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

A Smart Charging Assistant for Electric Vehicles Considering Battery Degradation, Power Grid and User Constraints

Der Anstieg intermittierender Stromerzeugung aus erneuerbaren Energiequellen erschwert zunehmend einen effizienten und zuverlässigen Betrieb der Versorgungsnetze. Gleichzeitig steigt die Zahl der Elektrofahrzeuge, die zum Aufladen erhebliche Mengen an elektrischer Energie benötigen, rapide an. Energie- und Mobilitätssektor sind somit unweigerlich miteinander verbunden, was zur Folge hat, dass zuverlässige Elektromobilität von einer robusten Stromversorgung abhängt. Darüber hinaus empfinden Fahrzeugnutzer ihre individuelle Mobilität als eingeschränkt, da Elektrofahrzeuge im Vergleich zu Fahrzeugen mit Verbrennungsmotor derzeit eine geringere Reichweite aufweisen und mehr Zeit zum Aufladen benötigen. In der vorliegenden Arbeit wird daher ein neuartiges Konzept sowie eine Softwareanwendung (Ladeassistent) vorgestellt, die den Nutzer beim Laden seines Elektrofahrzeuges unterstützt und dabei die Interessen aller beteiligten Akteure berücksichtigt. Dafür werden zunächst Gestaltungsmerkmale möglicher Softwarearchitekturen verglichen, um eine geeignete Struktur von Modulen und deren Verknüpfung zu definieren. Anschließend werden anhand realer Daten sowohl Energieverbrauchs- als auch Batteriemodelle entwickelt, verbessert und validiert, welche die Fahr- und Ladeeigenschaften von Elektrofahrzeugen abbilden. Die wichtigsten Beiträge dieser Arbeit resultieren aus der Entwicklung und Validierung der folgenden drei Kernkomponenten des Ladeassistenten. Als Erstes wird das individuelle Mobilitätsverhalten der Nutzer modelliert und anhand von aufgezeichneten und halbsynthetischen Fahrdaten von Elektrofahrzeugen ausgewertet. Insbesondere wird ein neuartiger, zweistufiger Clustering-Algorithmus entwickelt, um häufig besuchte Orte der Nutzer zu ermitteln. Anschließend werden Ensembles von Random-Forest-Modellen verwendet, um die nächsten Aufenthaltsorte und die dort typischen Parkzeiten vorherzusagen. Als Zweites wird gemischt-ganzzahlige stochastische Optimierung angewandt, um Ladestopps in einem zukünftigen Zeithorizont möglichst komfortabel und kostengünstig zu planen. Dabei wird ein graphenbasierter Algorithmus eingesetzt, um den Energiebedarf und die Eintrittswahrscheinlichkeit von Mobilitätsszenarien eines Elektrofahrzeugnutzers zu quantifizieren. Zur Validierung werden zwei alternative Ladestrategien definiert und mit dem vorgeschlagenen System verglichen. Als Drittes wird ein nichtlineares Optimierungsschema entwickelt, um vorhandene Zeit- und Energieflexibilität in Ladevorgängen von Elektrofahrzeugen zu nutzen. Die Integration eines detaillierten Batteriemodells ermöglicht eine genaue Quantifizierung der Kosteneinsparungen aufgrund einer geringeren Batteriealterung und dynamischer Stromtarife. Anhand von Daten aus realen Ladevorgängen von Elektrofahrzeugen können Einflüsse auf die Rentabilität von Vehicle-to-Grid-Anwendungen herausgearbeitet werden. Aus der Umsetzung des vorgestellten Ansatzes in einer realistischen Umgebung geht ein Architekturentwurf und ein Kommunikationskonzept für optimierungsbasierte intelligente Ladesysteme hervor. Dabei werden weitere Herausforderungen im Zusammenhang mit standardisierter Ladekommunikation, Eingriffen der Energieversorger und Nutzerakzeptanz aufgedeckt

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical