Exploring Computing Continuum in IoT Systems: Sensing, Communicating and Processing at the Network Edge

As Internet of Things (IoT), originally comprising of only a few simple sensing devices, reaches 34 billion units by the end of 2020, they cannot be defined as merely monitoring sensors anymore. IoT capabilities have been improved in recent years as relatively large internal computation and storage capacity are becoming a commodity. In the early days of IoT, processing and storage were typically performed in cloud. New IoT architectures are able to perform complex tasks directly on-device, thus enabling the concept of an extended computational continuum. Real-time critical scenarios e.g. autonomous vehicles sensing, area surveying or disaster rescue and recovery require all the actors involved to be coordinated and collaborate without human interaction to a common goal, sharing data and resources, even in intermittent networks covered areas. This poses new problems in distributed systems, resource management, device orchestration,as well as data processing. This work proposes a new orchestration and communication framework, namely CContinuum, designed to manage resources in heterogeneous IoT architectures across multiple application scenarios. This work focuses on two key sustainability macroscenarios: (a) environmental sensing and awareness, and (b) electric mobility support. In the first case a mechanism to measure air quality over a long period of time for different applications at global scale (3 continents 4 countries) is introduced. The system has been developed in-house from the sensor design to the mist-computing operations performed by the nodes. In the second scenario, a technique to transmit large amounts of fine-time granularity battery data from a moving vehicle to a control center is proposed jointly with the ability of allocating tasks on demand within the computing continuum

Named Data Networking in IoT based sensor devices

In a world running on a “smart” vision, the Internet of Things (IoT) progress is going faster than ever. The term “things” is not just about computer, people and smartphone, but also sensors, refrigerators, vehicles, clothing, food and so on. Internet of Things is the possibility to provide an IP address for every item, so it will have an interface on the Internet network. The household devices will not just being commanded and monitored remotely then, but they will have an active main character role, establishing a communication network between them. The thesis will begin describing a general overview, the state of art, of the IoT world and of sensors networks, checking its potential and any restrictions, if present. Then, every engineering aspect of the realized project, will been described in detail. This thesis will also prove that nowadays we have the right items and components for the realization of reliable low-cost sensors. The ultimate purpose is to verify the introduction of new network protocols like NDN (Named Data Networking) to evaluate their performances and efficiency. Finally I will propose the simulations output obtained by NS3 (Network Simulator): a scenario simulation using NDNSim and ChronoSync application will be present

Race Against the Machine: a Fully-annotated, Open-design Dataset of Autonomous and Piloted High-speed Flight

Unmanned aerial vehicles, and multi-rotors in particular, can now perform dexterous tasks in impervious environments, from infrastructure monitoring to emergency deliveries. Autonomous drone racing has emerged as an ideal benchmark to develop and evaluate these capabilities. Its challenges include accurate and robust visual-inertial odometry during aggressive maneuvers, complex aerodynamics, and constrained computational resources. As researchers increasingly channel their efforts into it, they also need the tools to timely and equitably compare their results and advances. With this dataset, we want to (i) support the development of new methods and (ii) establish quantitative comparisons for approaches originating from the broader robotics and artificial intelligence communities. We want to provide a one-stop resource that is comprehensive of (i) aggressive autonomous and piloted flight, (ii) high-resolution, high-frequency visual, inertial, and motion capture data, (iii) commands and control inputs, (iv) multiple light settings, and (v) corner-level labeling of drone racing gates. We also release the complete specifications to recreate our flight platform, using commercial off-the-shelf components and the open-source flight controller Betaflight, to democratize drone racing research. Our dataset, open-source scripts, and drone design are available at: https://github.com/tii-racing/drone-racing-datasetComment: 8 pages, 7 figure

Una libreria OpenGL per la selezione e editing di mesh poligonali

Nel mondo Open Source, la libreria grafica OpenGL è oggi ampiamente utilizzata in svariati settori come l'animazione 2D/3D, la modellazione CAD o nello sviluppo di videogiochi. A causa dei suoi innumerevoli usi e dell'astrazione che OpenGL permette di ottenere su diversi ambienti grafici, lo sviluppatore - che la utilizza - è vincolato a cercare librerie di supporto al fine di sfruttarne al meglio le potenzialità. Questa tesi si configura su questi presupposti, presentando una libreria di selezione e editing di mesh 3D basata su OpenGL. La libreria, chiamata libEditMesh, sfrutta il meccanismo geometrico del RayPicking permettendo all'utilizzatore di identificare col mouse punti, facce e lati di solidi in scena. La tesi si articola sostanzialmente in due parti: nella prima vengono proposte alcune soluzioni ad-hoc sviluppate su applicazioni già esistenti nel panorama openSource, e non; nella seconda vengono esposti gli algoritmi e funzioni implementate in libEditMesh

Explorer le computing continuum dans l'internet des objets : détection, communication et traitement dans le Network Edge

As Internet of Things (IoT), originally comprising of only a few simple sensing devices, reaches 34 billion units by the end of 2020, they cannot be defined as merely monitoring sensors anymore. IoT capabilities have been improved in recent years as relatively large internal computation and storage capacity are becoming a commodity. In the early days of IoT, processing and storage were typically performed in cloud. New IoT architectures are able to perform complex tasks directly on-device, thus enabling the concept of an extended computational continuum. Real-time critical scenarios e.g. autonomous vehicles sensing, area surveying or disaster rescue and recovery require all the actors involved to be coordinated and collaborate without human interaction to a common goal, sharing data and resources, even in intermittent networks covered areas. This poses new problems in distributed systems, resource management, device orchestration,as well as data processing. This work proposes a new orchestration and communication framework, namely CContinuum, designed to manage resources in heterogeneous IoT architectures across multiple application scenarios. This work focuses on two key sustainability macroscenarios: (a) environmental sensing and awareness, and (b) electric mobility support. In the first case a mechanism to measure air quality over a long period of time for different applications at global scale (3 continents 4 countries) is introduced. The system has been developed in-house from the sensor design to the mist-computing operations performed by the nodes. In the second scenario, a technique to transmit large amounts of fine-time granularity battery data from a moving vehicle to a control center is proposed jointly with the ability of allocating tasks on demand within the computing continuum.L'Internet des objets (IoT), ne comprenant à l'origine que quelques dispositifs de détection simple, atteint aujourd’hui 34 milliards d’objets connectés d'ici fin 2020. Ces objets ne peuvent plus être définis comme de simples capteurs de surveillance. Les capacités de l'IoT ont été améliorées ces dernières années tandis-que que les capacités de calcul et de stockage de masse sont devenus des marchandises. Aux débuts de l'IoT, le traitement et le stockage étaient généralement effectués dans le cloud. Les nouvelles architectures IoT sont capables d'exécuter des tâches complexes directement sur l'appareil, permettant ainsi le concept d'un continuum de calcul étendu. Les scénarios critiques et temps réel, comme par exemple la détection de véhicules autonomes, la surveillance de zone ou le sauvetage en cas de catastrophe, nécessitent que l’ensemble des acteurs impliqués soient coordonnés et collaborent sans interaction humaine vers un objectif commun, partageant des données et des ressources, même dans les zones couvertes par des réseaux intermittents. Cela pose de nouveaux problèmes dans les systèmes distribués, la gestion des ressources, l'orchestration des appareils et le traitement des données. Ce travail propose un nouveau cadre de communication et d'orchestration, à savoir le C-Continuum, conçu dans des architectures IoT hétérogènes à travers plusieurs scénarios d'application. Ce travail se concentre pour gérer les ressources sur deux macro-scénarios clés de durabilité : (a) la détection et la sensibilisation à l'environnement, et (b) le soutien à la mobilité électrique. Dans le premier cas, un mécanisme de mesure de la qualité de l'air sur une longue période avec différentes applications à l'échelle mondiale (3 continents et 4 pays) est introduit. Le système a été développé en interne depuis la conception du capteur jusqu'aux opérations de mist-computing effectuées par les nœuds. Dans le deuxième scénario une technique pour transmettre de grandes quantités de données, entre un véhicule en mouvement et un centre de contrôle est proposé. Ces données sont de haute granularité temporelle relatives et permettent conjointement d'allouer des tâches sur demande dans le continuum de calcul

Smart Mobility and Sensing: Case Studies Based on a Bike Information Gathering Architecture

Mapping services and travel planner applications are experiencing a great success in supporting people while they plan a route or while they move across the city, playing a key role in the smart mobility scenario. Nevertheless, they are based on the same algorithms, on the same elements (in terms of time, distance, means of transports, etc.), providing a limited set of personalization. To fill this gap, we propose PUMA, a Personal Urban Mobility Assistant that aims to let the user add different factors of personalization, such as sustainability, street and personal safety, wellness and health, etc. In this paper we focus on the use of smart bikes (equipped with specific sensors) as means of transports and as a mean to collect data about the urban environment. We describe a cloud based architecture, personas and travel scenario to prove the feasibility of our approach

Revisiting WiFi offloading in the wild for V2I applications

International audienceThis paper revisits the opportunities of using WiFi offloading for Vehicle to Internet (V2I) communication, and how this has changed over the last decade. With the rollouts of provider-managed WiFi networks that are more structured and operate under authenticated regimes, WiFi offloading, or use of available (roadside) WiFi networks for V2I data communication, has different opportunities and challenges. To study the current landscape,we develop a system (X-Fi), which efficiently selects, associates to, authenticates with, and performs WiFi offloading for V2I communication with these networks, and a tool (X-Perf), which illustrates opportunities of WiFi offloading available today in these networks, with measurements and experiments across four metro areas across three continents over 22 months. Our results indicate the feasibility of achieving 1 GB/hour application goodput, an order of magnitude higher than the number provided by open WiFi networks in the past, which can take a significant load away from alternative communication paths for V2I systems. Moreover, we provide several implications on transport protocols and WiFi deployments to shed light on the use of such WiFi networks for V2I communication

Air Quality and Comfort Characterisation within an Electric Vehicle Cabin in Heating and Cooling Operations

This work is aimed at the experimental characterisation of air quality and thermal profile within an electric vehicle cabin, measuring at the same time the HVAC system energy consumption. Pollutant concentrations in the vehicle cabin are measured by means of a low-cost system of sensors. The effects of the HVAC system configuration, such as fresh-air and recirculation mode, on cabin air quality, are discussed. It is shown that the PM concentrations observed in recirculation mode are lower than those in fresh-air mode, while VOC concentrations are generally higher in recirculation than in fresh-air mode. The energy consumption is compared in different configurations of the HVAC system. The novelty of this work is the combined measurement of important comfort parameters such as air temperature distribution and air quality within the vehicle, together with the real time energy consumption of the HVAC system. A wider concept of comfort is enabled, based on the use of low-cost sensors in the automotive field

On assessing the accuracy of air pollution models exploiting a strategic sensors deployment

This paper presents a preliminary experiment done to identify potential problems and issues in setting up a testbed for air pollution measurement and modeling. Our final testbed, part of a joint research activity between the University of Bologna and the Macao Polytechnic Institute, will be composed of three lines of the air pollution sensors Canarin II and it will be used to produce spatio-temporal open data to test third-party air pollution models. Here, we present a preliminary experiment based on a single line of sensors, showing interesting insights into the actual open challenge of air pollution modeling techniques validation, taking into account the effects of air pollutant emissions sources, meteorology, atmospheric concentrations and urban vegetation