LRD2: Low Resource Device Description for Energy Efficient Device Discovery

International audienceThe success of the large scale deployment of sensor actuator devices lies in their plug\&play capabilities: they should be automatically discovered and ready to be used when they join to an environment. Self-description and discovery gain therefore a particular importance. Nevertheless, there is currently no largely adopted energy efficient device description and discovery standard. Existing protocols use proprietary device description models and discovery mechanisms that are incompatible between the two. This paper presents LRD2 (Low Resource Device Description), a generic description model capable of describing different kinds of device information. LRD2 implements a compression algorithm to reduce the size of description documents, thus saving energy by reducing the number of messages sent in the network. Experimental results about the performance of LRD2 are also presented

Autonomic Pervasive Applications Driven by Abstract Specifications

Conference in conjunction with ICAC 2012 (International Conference on Autonomic Computing)International audiencePervasive application architectures present stringent requirements that make their development especially hard. In particular, they need to be flexible in order to cope with dynamism in different forms (e.g. service and data providers and consumers). The current trend to build applications out of remote services makes the availability of constituent application components inherently dynamic. Developers can no longer assume that applications are static after development or at run time. Unfortunately, developing applications that are able to cope with dynamism is very complex.Existing development approaches do not provide explicit support for managing dynamism. In this paper we describe Rondo, a tool suite for designing pervasive applications. More specifically, we present our propositions in pervasive application specification, which borrows concepts from service-oriented component assembly, model-driven engineering (MDE) and continuous deployment, resulting in a more flexible approach than traditional application definitions. Then the capabilities of our application model are demonstrated with an example application scenario designed using our approach

FESTIVAL: towards an intercontinental federation approach

In the last years, in both Europe and Japan, several initiatives have been started with the aim of building and testing Internet of Things and Smart ICT architectures and platforms to address specific domain issues through designed solutions. FESTIVAL EU-Japan collaborative project aims at federating these testbeds, making them interoperable, allowing centralized data collection and analyzing societal issues in both cultures, all of it under a user privacy-preserving context. In this sense, FESTIVAL pursues a twofold approach: firstly, the intercontinental federation of testbeds in Japan and Europe using existing tools as well as developing new ones; and secondly, the creation of new services and experiments, to be performed on top of the FESTIVAL testbeds and experimentation facilities, associated to three different smart city domains: smart energy, smart building and smart shopping. Throughout this article the current status of the project (in its first year) is shown, describing the Experimentation as a Service federation approach to be implemented, with a first analysis of the platforms and testbeds that are included within the project. Furthermore, the paper also describes the services and use cases that will be conducted within FESTIVAL lifespan. Finally, next steps to be carried out in the coming years of the project are indicated.This work was funded in part by the European Union’s Horizon 2020 Programme of the FESTIVAL project (Federated Interoperable Smart ICT Services Development and Testing Platforms) under grant agreement 643275, and from the Japanese National Institute of Information and Communications Technolog

FESTIVAL: heterogeneous testbed federation across Europe and Japan

FESTIVAL is an H2020 EU-Japan collaborative project that aims to federate heterogeneous testbeds, making them interoperable and building an “Experimentation as a Service” (EaaS) model. Going beyond the traditional nature of experimental facilities, related to computational and networking large scale infrastructures, FESTIVAL testbeds have heterogeneous nature and in order to be federated they have been clustered in four categories: “Open Data” (i.e. open datasets), “IoT” (i.e. sensors and actuators), “IT” (i.e. computational resources) and “Living Labs” (i.e. people). Considering that every testbed category provides specific resources, the main challenge for FESTIVAL is to develop a platform that can allow experimenters to access very different assets in an homogeneous and transparent way, supporting them in the phases of the experiments. The FESTIVAL architecture, based on a multi-level federation approach, proposes a solution to this problem providing also a set of functionalities to manage and monitor the experiments. FESTIVAL tools, also, include the possibility to access FIWAREGeneric Enablers allowing to deploy predefined components to address specific needs in the experimentation (e.g. data analysis, big data management etc.). The FESTIVAL platform will be tested on three different smart city domains across Japan and Europe: smart energy, smart building and smart shopping.This work was funded in part by the European Union’s Horizon 2020 Programme of the FESTIVALproject (Federated Interoperable Smart ICT Services Development and Testing Platforms) under grant agreement no. 643275, and by the Japanese National Institute of Information and Communications Technology

FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes

Opération d'administration pour SStreaMWare

International audienceThis paper presents a novel research direction in the field of sensor data management. It concerns transactional support in heterogeneous large scale sensor systems. Besides well-known continuous queries on sensor data, system management queries should be supported in these systems. Indeed, with increasing capacity and diversity of sensors, new applications which require complex read-only and update queries are likely to appear. Those applications will require challenging properties such as ACID properties. This paper discusses the relevance of ACID properties in sensor data management context. It then focuses on the isolation property and proposes a concurrency control mechanism to support concurrent execution of continuous queries and update transactions on sensor properties

Indoor Occupancy Estimation via Location-Aware HMM: An IoT Approach

International audienceIndoor occupancy estimation is a critical analytical task for several applications (e.g., social isolation of elderlies). The proliferation of Internet of Things (IoT) devices enabled the occupancy estimation, as it provided access to a mass amount of data. Several works have been proposed exploiting the IoT Passive Inference (PIR) or environmental (e.g., $CO_2$) features. These works however are traditionally selecting the feature space at the learning phase and passively using it over time. Hence, they ignore the dynamics of indoor occupancy, such as the location of the occupant or his motion patterns, leading to a decreasing accuracy over time. In this paper, we study those dynamics and show that motion patterns, along with environmental features favor the occupancy estimation. We design a Location-Aware Hidden Markov Model (HMM), which dynamically adapts the feature space based on the occupant's location. Our experiments on real data show that Location-Aware HMM can reach up to 10% better accuracy than Conventional HMM

Wireless Sensor Network Technologies for Information Explosion Era

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Exploring Variability in IoT Data for Human Activity Recognition

International audienceHuman Activity Recognition (HAR) is a well-studied scientific area that has gained much traction with the rise of Internet of Things (IoT). Despite the interest in HAR for a wide spectrum of domains (technological, medical, etc.) only a few works exist, which study the variability in IoT data. To correctly perceive this variability, it is essential to dynamically model the evolving context of daily-life activities. Additionally, it is required to reduce the calculation cost of HAR, which is crucial for security and real-time applications. For the purpose of dynamically modeling, three context-aware approaches are formalized along with a context-free baseline. This study demonstrates improvements in terms of both of accuracy and calculation cost by considering variability in IoT data; our experimental study on real datasets reduced calculation cost by 20% while increasing accuracy by 20%

Gestion de données de capteurs

International audienceSince the emergence of sensor oriented applications, sensor data management has become a very active research domain. On the one hand, sensor networks provide distributed processing of queries on the sensors, on the other hand, data stream management systems are systems which deal with data stream comming from sensors. Recently, some hybrid approaches have also appeared which aim to integrate these two different kinds of solutions. This paper presents these three groups of proposals and gives their approach to deal with issues such as scalability of sensor systems, heterogeneity of sensors and processing of complex queries on data stream comming from sensors