Information Centric Networking in the IoT: Experiments with NDN in the Wild

This paper explores the feasibility, advantages, and challenges of an ICN-based approach in the Internet of Things. We report on the first NDN experiments in a life-size IoT deployment, spread over tens of rooms on several floors of a building. Based on the insights gained with these experiments, the paper analyses the shortcomings of CCN applied to IoT. Several interoperable CCN enhancements are then proposed and evaluated. We significantly decreased control traffic (i.e., interest messages) and leverage data path and caching to match IoT requirements in terms of energy and bandwidth constraints. Our optimizations increase content availability in case of IoT nodes with intermittent activity. This paper also provides the first experimental comparison of CCN with the common IoT standards 6LoWPAN/RPL/UDP.Comment: 10 pages, 10 figures and tables, ACM ICN-2014 conferenc

Data-centric Design and Training of Deep Neural Networks with Multiple Data Modalities for Vision-based Perception Systems

224 p.Los avances en visión artificial y aprendizaje automático han revolucionado la capacidad de construir sistemas que procesen e interpreten datos digitales, permitiéndoles imitar la percepción humana y abriendo el camino a un amplio rango de aplicaciones. En los últimos años, ambas disciplinas han logrado avances significativos,impulsadas por los progresos en las técnicas de aprendizaje profundo(deep learning). El aprendizaje profundo es una disciplina que utiliza redes neuronales profundas (DNNs, por sus siglas en inglés) para enseñar a las máquinas a reconocer patrones y hacer predicciones basadas en datos. Los sistemas de percepción basados en el aprendizaje profundo son cada vez más frecuentes en diversos campos, donde humanos y máquinas colaboran para combinar sus fortalezas.Estos campos incluyen la automoción, la industria o la medicina, donde mejorar la seguridad, apoyar el diagnóstico y automatizar tareas repetitivas son algunos de los objetivos perseguidos.Sin embargo, los datos son uno de los factores clave detrás del éxito de los algoritmos de aprendizaje profundo. La dependencia de datos limita fuertemente la creación y el éxito de nuevas DNN. La disponibilidad de datos de calidad para resolver un problema específico es esencial pero difícil de obtener, incluso impracticable,en la mayoría de los desarrollos. La inteligencia artificial centrada en datos enfatiza la importancia de usar datos de alta calidad que transmitan de manera efectiva lo que un modelo debe aprender. Motivada por los desafíos y la necesidad de los datos, esta tesis formula y valida cinco hipótesis sobre la adquisición y el impacto de los datos en el diseño y entrenamiento de las DNNs.Específicamente, investigamos y proponemos diferentes metodologías para obtener datos adecuados para entrenar DNNs en problemas con acceso limitado a fuentes de datos de gran escala. Exploramos dos posibles soluciones para la obtención de datos de entrenamiento, basadas en la generación de datos sintéticos. En primer lugar, investigamos la generación de datos sintéticos utilizando gráficos 3D y el impacto de diferentes opciones de diseño en la precisión de los DNN obtenidos. Además, proponemos una metodología para automatizar el proceso de generación de datos y producir datos anotados variados, mediante la replicación de un entorno 3D personalizado a partir de un archivo de configuración de entrada. En segundo lugar, proponemos una red neuronal generativa(GAN) que genera imágenes anotadas utilizando conjuntos de datos anotados limitados y datos sin anotaciones capturados en entornos no controlados

Bluetooth Mesh under the Microscope: How much ICN is Inside?

Bluetooth (BT) mesh is a new mode of BT operation for low-energy devices that offers group-based publish-subscribe as a network service with additional caching capabilities. These features resemble concepts of information-centric networking (ICN), and the analogy to ICN has been repeatedly drawn in the BT community. In this paper, we compare BT mesh with ICN both conceptually and in real-world experiments. We contrast both architectures and their design decisions in detail. Experiments are performed on an IoT testbed using NDN/CCNx and BT mesh on constrained RIOT nodes. Our findings indicate significant differences both in concepts and in real-world performance. Supported by new insights, we identify synergies and sketch a design of a BT-ICN that benefits from both worlds

NDN, CoAP, and MQTT: A Comparative Measurement Study in the IoT

This paper takes a comprehensive view on the protocol stacks that are under debate for a future Internet of Things (IoT). It addresses the holistic question of which solution is beneficial for common IoT use cases. We deploy NDN and the two popular IP-based application protocols, CoAP and MQTT, in its different variants on a large-scale IoT testbed in single- and multi-hop scenarios. We analyze the use cases of scheduled periodic and unscheduled traffic under varying loads. Our findings indicate that (a) NDN admits the most resource-friendly deployment on nodes, and (b) shows superior robustness and resilience in multi-hop scenarios, while (c) the IP protocols operate at less overhead and higher speed in single-hop deployments. Most strikingly we find that NDN-based protocols are in significantly better flow balance than the UDP-based IP protocols and require less corrective actions

On Offline Evaluation of Vision-based Driving Models

Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. The supplementary video can be viewed at https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc

Active Learning Augmented Reality for STEAM Education—A Case Study

Immersive technologies are rapidly transforming the field of education. Amongst them, Augmented Reality (AR) has shown promise as a resource, particularly for education in Science, Technology, Engineering, Arts, and Mathematics (STEAM). There are, however, few teachers deploying this new medium in the classroom directly, and, consequently, only a few, elect students benefit from the AR-enriched offers. Curricula are already overloaded, and schools generally lack developmental resources, thus leaving no room for experimentation. This situation is further aggravated by the too few educational applications available with sufficient learning content. In this article, we investigate the method of Active Learning for the teaching of STEAM subjects, using a format where students are tasked with building an AR application as part of their learning. We evaluate the applicability of the Active Learning for STEAM subjects with a qualitative, case study approach, applying the workshop format as an extracurricular activity in our work with students from a range of secondary schools in Oxford. We discuss how the format works, so it can be embedded into regular curricula, not just as an extracurricular activity, also providing an overview on the involved teaching units and rationale. All teams in our preview audience of the case study succeeded in building working applications, several of impressive complexity. Students found that the lessons were enjoyable and AR technology can enhance their learning experience. The Active Learning method served as a catalyst for students’ skills development, with the case study providing evidence of learning to code, working with a physics simulation engine, ray-tracing, and geometry, learning how to manage teams and interact with other students/instructors, and engineering a working prototype of a game. We consequentially argue that combining the STEM subjects and the arts, using the proposed Active Learning format, is able to provide a more holistic and engaging education

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

ICN as Network Infrastructure for Multi-Sensory Devices: Local Domain Service Discovery for ICN-based IoT Environments

Information Centric Networking (ICN) is an emerging research topic aiming at shifting the Internet from its current host-centric paradigm towards an approach centred around content, which enables the direct retrieval of information objects in a secure, reliable, scalable, and efficient way. The exposure of ICN to scenarios other than static content distribution is a growing research topic, promising to extend the impact of ICN to a broader scale. In this context, particular attention has been given to the application of ICN in Internet of Things (IoT) environments. The current paper, by focusing on local domain IoT scenarios, such as multi-sensory Machine to Machine environments, discusses the challenges that ICN, particularly Interest-based solutions, impose to service discovery. This work proposes a service discovery mechanism for such scenarios, relying on an alternative forwarding pipeline for supporting its core operations. The proposed mechanism is validated through a proof-of-concept prototype, developed on top of the Named Data Networking ICN architecture, with results showcasing the benefits of our solution for discovering services within a collision domain. © 2017 Springer Science+Business Media New Yor