MONICA in Hamburg: Towards Large-Scale IoT Deployments in a Smart City

Modern cities and metropolitan areas all over the world face new management challenges in the 21st century primarily due to increasing demands on living standards by the urban population. These challenges range from climate change, pollution, transportation, and citizen engagement, to urban planning, and security threats. The primary goal of a Smart City is to counteract these problems and mitigate their effects by means of modern ICT to improve urban administration and infrastructure. Key ideas are to utilise network communication to inter-connect public authorities; but also to deploy and integrate numerous sensors and actuators throughout the city infrastructure - which is also widely known as the Internet of Things (IoT). Thus, IoT technologies will be an integral part and key enabler to achieve many objectives of the Smart City vision. The contributions of this paper are as follows. We first examine a number of IoT platforms, technologies and network standards that can help to foster a Smart City environment. Second, we introduce the EU project MONICA which aims for demonstration of large-scale IoT deployments at public, inner-city events and give an overview on its IoT platform architecture. And third, we provide a case-study report on SmartCity activities by the City of Hamburg and provide insights on recent (on-going) field tests of a vertically integrated, end-to-end IoT sensor application.Comment: 6 page

A context -and template- based data compression approach to improve resource-constrained IoT systems interoperability.

170 p.El objetivo del Internet de las Cosas (the Internet of Things, IoT) es el de interconectar todo tipo de cosas, desde dispositivos simples, como una bombilla o un termostato, a elementos más complejos y abstractoscomo una máquina o una casa. Estos dispositivos o elementos varían enormemente entre sí, especialmente en las capacidades que poseen y el tipo de tecnologías que utilizan. Esta heterogeneidad produce una gran complejidad en los procesos integración en lo que a la interoperabilidad se refiere.Un enfoque común para abordar la interoperabilidad a nivel de representación de datos en sistemas IoT es el de estructurar los datos siguiendo un modelo de datos estándar, así como formatos de datos basados en texto (e.g., XML). Sin embargo, el tipo de dispositivos que se utiliza normalmente en sistemas IoT tiene capacidades limitadas, así como recursos de procesamiento y de comunicación escasos. Debido a estas limitaciones no es posible integrar formatos de datos basados en texto de manera sencilla y e1ciente en dispositivos y redes con recursos restringidos. En esta Tesis, presentamos una novedosa solución de compresión de datos para formatos de datos basados en texto, que está especialmente diseñada teniendo en cuenta las limitaciones de dispositivos y redes con recursos restringidos. Denominamos a esta solución Context- and Template-based Compression (CTC). CTC mejora la interoperabilidad a nivel de los datos de los sistemas IoT a la vez que requiere muy pocos recursos en cuanto a ancho de banda de las comunicaciones, tamaño de memoria y potencia de procesamiento

A context -and template- based data compression approach to improve resource-constrained IoT systems interoperability.

170 p.El objetivo del Internet de las Cosas (the Internet of Things, IoT) es el de interconectar todo tipo de cosas, desde dispositivos simples, como una bombilla o un termostato, a elementos más complejos y abstractoscomo una máquina o una casa. Estos dispositivos o elementos varían enormemente entre sí, especialmente en las capacidades que poseen y el tipo de tecnologías que utilizan. Esta heterogeneidad produce una gran complejidad en los procesos integración en lo que a la interoperabilidad se refiere.Un enfoque común para abordar la interoperabilidad a nivel de representación de datos en sistemas IoT es el de estructurar los datos siguiendo un modelo de datos estándar, así como formatos de datos basados en texto (e.g., XML). Sin embargo, el tipo de dispositivos que se utiliza normalmente en sistemas IoT tiene capacidades limitadas, así como recursos de procesamiento y de comunicación escasos. Debido a estas limitaciones no es posible integrar formatos de datos basados en texto de manera sencilla y e1ciente en dispositivos y redes con recursos restringidos. En esta Tesis, presentamos una novedosa solución de compresión de datos para formatos de datos basados en texto, que está especialmente diseñada teniendo en cuenta las limitaciones de dispositivos y redes con recursos restringidos. Denominamos a esta solución Context- and Template-based Compression (CTC). CTC mejora la interoperabilidad a nivel de los datos de los sistemas IoT a la vez que requiere muy pocos recursos en cuanto a ancho de banda de las comunicaciones, tamaño de memoria y potencia de procesamiento

Enabling the orchestration of IoT slices through edge and cloud microservice platforms

This article addresses one of the main challenges related to the practical deployment of Internet of Things (IoT) solutions: the coordinated operation of entities at different infrastructures to support the automated orchestration of end-to-end Internet of Things services. This idea is referred to as "Internet of Things slicing" and is based on the network slicing concept already defined for the Fifth Generation (5G) of mobile networks. In this context, we present the architectural design of a slice orchestrator addressing the aforementioned challenge, based on well-known standard technologies and protocols. The proposed solution is able to integrate existing technologies, like cloud computing, with other more recent technologies like edge computing and network slicing. In addition, a functional prototype of the proposed orchestrator has been implemented, using open-source software and microservice platforms. As a first step to prove the practical feasibility of our solution, the implementation of the orchestrator considers cloud and edge domains. The validation results obtained from the prototype prove the feasibility of the solution from a functional perspective, verifying its capacity to deploy Internet of Things related functions even on resource constrained platforms. This approach enables new application models where these Internet of Things related functions can be onboarded on small unmanned aerial vehicles, offering a flexible and cost-effective solution to deploy these functions at the network edge. In addition, this proposal can also be used on commercial cloud platforms, like the Google Compute Engine, showing that it can take advantage of the benefits of edge and cloud computing respectivelyThe work of Ivan Vidal and Francisco Valera was partially supported by the European H2020 5GinFIRE project (grant agreement 732497), and by the 5GCity project (TEC2016-76795-C6-3-R) funded by the Spanish Ministry of Economy and Competitiveness

Context- and Template-Based Compression for Efficient Management of Data Models in Resource-Constrained Systems

The Cyber Physical Systems (CPS) paradigm is based on the deployment of interconnected heterogeneous devices and systems, so interoperability is at the heart of any CPS architecture design. In this sense, the adoption of standard and generic data formats for data representation and communication, e.g., XML or JSON, effectively addresses the interoperability problem among heterogeneous systems. Nevertheless, the verbosity of those standard data formats usually demands system resources that might suppose an overload for the resource-constrained devices that are typically deployed in CPS. In this work we present Context-and Template-based Compression (CTC), a data compression approach targeted to resource-constrained devices, which allows reducing the resources needed to transmit, store and process data models. Additionally, we provide a benchmark evaluation and comparison with current implementations of the Efficient XML Interchange (EXI) processor, which is promoted by the World Wide Web Consortium (W3C), and it is the most prominent XML compression mechanism nowadays. Interestingly, the results from the evaluation show that CTC outperforms EXI implementations in terms of memory usage and speed, keeping similar compression rates. As a conclusion, CTC is shown to be a good candidate for managing standard data model representation formats in CPS composed of resource-constrained devices.Research partially supported by the European Union Horizon 2020 Programme under Grant Agreement Number H2020-EeB-2015/680708 - HIT2GAP, Highly Innovative building control Tools Tackling the energy performance GAP. Also partially supported by the Department of Education, Universities and Research of the Basque Government under Grant IT980-16 and the Spanish Research Council, under grant TIN2016-79897-P

Tool Wear Prediction Upgrade Kit for Legacy CNC Milling Machines in the Shop Floor

The operation of CNC milling is expensive because of the cost-intensive use of cutting tools. The wear and tear of CNC tools influence the tool lifetime. Today’s machines are not capable of accurately estimating the tool abrasion during the machining process. Therefore, manufacturers rely on reactive maintenance, a tool change after breakage, or a preventive maintenance approach, a tool change according to predefined tool specifications. In either case, maintenance costs are high due to a loss of machine utilization or premature tool change. To find the optimal point of tool change, it is necessary to monitor CNC process parameters during machining and use advanced data analytics to predict the tool abrasion. However, data science expertise is limited in small-medium sized manufacturing companies. The long operating life of machines often does not justify investments in new machines before the end of operating life. The publication describes a cost-efficient approach to upgrade legacy CNC machines with a Tool Wear Prediction Upgrade Kit. A practical solution is presented with a holistic hardware/software setup, including edge device, and multiple sensors. The prediction of tool wear is based on machine learning. The user interface visualizes the machine condition for the maintenance personnel in the shop floor. The approach is conceptualized and discussed based on industry requirements. Future work is outlined

Concevoir des applications internet des objets sémantiques

According to Cisco's predictions, there will be more than 50 billions of devices connected to the Internet by 2020.The devices and produced data are mainly exploited to build domain-specific Internet of Things (IoT) applications. From a data-centric perspective, these applications are not interoperable with each other.To assist users or even machines in building promising inter-domain IoT applications, main challenges are to exploit, reuse, interpret and combine sensor data.To overcome interoperability issues, we designed the Machine-to-Machine Measurement (M3) framework consisting in:(1) generating templates to easily build Semantic Web of Things applications, (2) semantically annotating IoT data to infer high-level knowledge by reusing as much as possible the domain knowledge expertise, and (3) a semantic-based security application to assist users in designing secure IoT applications.Regarding the reasoning part, stemming from the 'Linked Open Data', we propose an innovative idea called the 'Linked Open Rules' to easily share and reuse rules to infer high-level abstractions from sensor data.The M3 framework has been suggested to standardizations and working groups such as ETSI M2M, oneM2M, W3C SSN ontology and W3C Web of Things. Proof-of-concepts of the flexible M3 framework have been developed on the cloud (http://www.sensormeasurement.appspot.com/) and embedded on Android-based constrained devices.Selon les prévisions de Cisco , il y aura plus de 50 milliards d'appareils connectés à Internet d'ici 2020. Les appareils et les données produites sont principalement exploitées pour construire des applications « Internet des Objets (IdO) ». D'un point de vue des données, ces applications ne sont pas interopérables les unes avec les autres. Pour aider les utilisateurs ou même les machines à construire des applications 'Internet des Objets' inter-domaines innovantes, les principaux défis sont l'exploitation, la réutilisation, l'interprétation et la combinaison de ces données produites par les capteurs. Pour surmonter les problèmes d'interopérabilité, nous avons conçu le système Machine-to-Machine Measurement (M3) consistant à: (1) enrichir les données de capteurs avec les technologies du web sémantique pour décrire explicitement leur sens selon le contexte, (2) interpréter les données des capteurs pour en déduire des connaissances supplémentaires en réutilisant autant que possible la connaissance du domaine définie par des experts, et (3) une base de connaissances de sécurité pour assurer la sécurité dès la conception lors de la construction des applications IdO. Concernant la partie raisonnement, inspiré par le « Web de données », nous proposons une idée novatrice appelée le « Web des règles » afin de partager et réutiliser facilement les règles pour interpréter et raisonner sur les données de capteurs. Le système M3 a été suggéré à des normalisations et groupes de travail tels que l'ETSI M2M, oneM2M, W3C SSN et W3C Web of Things. Une preuve de concept de M3 a été implémentée et est disponible sur le web (http://www.sensormeasurement.appspot.com/) mais aussi embarqu

Sensor inteligente para monitorización de animales con técnicas de bajo consumo

El estudio del comportamiento animal permite conocer los mecanismos de supervivencia de las especies, analizar su bienestar e incluso detectar enfermedades. Tradicionalmente, este estudio ha sido realizado por parte del personal Veterinario, a través de la observación del animal. Sin embargo, esta tarea supone una gran inversión de tiempo.En este Trabajo Fin de Máster se ha desarrollado un sensor inteligente adherido al cuerpo del animal que permite obtener una descripción precisa y constante de sus movimientos. Este tipo de dispositivos se conocen habitualmente como wearables, y suelen presentar un gran inconveniente en términos de duración de la batería, que limita muchas veces su implementación. Como respuesta a este problema, en este proyecto se utilizan diversas técnicas y tecnologías que permiten minimizar el consumo energético, como por ejemplo realizar la lectura del movimiento del animal mientras los procesadores del sensor están dormidos.La información inercial recabada por el sensor se envía a un servidor externo situado en Internet, lo que se conoce habitualmente como la “nube”. Con la finalidad de monitorizar a medio plazo un número elevado de animales en un espacio reducido, se ha seleccionado la tecnología WiFi para el envío.Como protocolo de nivel de aplicación y formato de los datos enviados se han utilizado MQTT y SenML, respectivamente. Ambos son estándares de IoT y permiten, entre otras cosas, reducir el consumo y asegurar la interoperabilidad con otras aplicaciones. El almacenamiento de la información enviada se produce en una base de datos de series temporales. A través de Internet, es posible acceder a ella para su representación y futuro procesado.Para la validación del sistema propuesto al completo se han diseñado dos prototipos del sensor inteligente: uno inicial para la experimentación en un escenario controlado con el movimiento del cuerpo humano, y otro más compacto en forma de placa de circuito impreso para la monitorización de las ovejas de la Facultad de Veterinaria de la Universidad de Zaragoza.<br /

Softwarization of Large-Scale IoT-based Disasters Management Systems

The Internet of Things (IoT) enables objects to interact and cooperate with each other for reaching common objectives. It is very useful in large-scale disaster management systems where humans are likely to fail when they attempt to perform search and rescue operations in high-risk sites. IoT can indeed play a critical role in all phases of large-scale disasters (i.e. preparedness, relief, and recovery). Network softwarization aims at designing, architecting, deploying, and managing network components primarily based on software programmability properties. It relies on key technologies, such as cloud computing, Network Functions Virtualization (NFV), and Software Defined Networking (SDN). The key benefits are agility and cost efficiency. This thesis proposes softwarization approaches to tackle the key challenges related to large-scale IoT based disaster management systems. A first challenge faced by large-scale IoT disaster management systems is the dynamic formation of an optimal coalition of IoT devices for the tasks at hand. Meeting this challenge is critical for cost efficiency. A second challenge is an interoperability. IoT environments remain highly heterogeneous. However, the IoT devices need to interact. Yet another challenge is Quality of Service (QoS). Disaster management applications are known to be very QoS sensitive, especially when it comes to delay. To tackle the first challenge, we propose a cloud-based architecture that enables the formation of efficient coalitions of IoT devices for search and rescue tasks. The proposed architecture enables the publication and discovery of IoT devices belonging to different cloud providers. It also comes with a coalition formation algorithm. For the second challenge, we propose an NFV and SDN based - architecture for on-the-fly IoT gateway provisioning. The gateway functions are provisioned as Virtual Network Functions (VNFs) that are chained on-the-fly in the IoT domain using SDN. When it comes to the third challenge, we rely on fog computing to meet the QoS and propose algorithms that provision IoT applications components in hybrid NFV based - cloud/fogs. Both stationary and mobile fog nodes are considered. In the case of mobile fog nodes, a Tabu Search-based heuristic is proposed. It finds a near-optimal solution and we numerically show that it is faster than the Integer Linear Programming (ILP) solution by several orders of magnitude