Hybrid CoAP-based resource discovery for the Internet of Things

Enabling automatic, efficient and scalable discovery of the resources provided by constrained low-power sensor and actuator networks is an important element to empower the transformation towards the Internet of Things (IoT). To this end, many centralized and distributed resource discovery approaches have been investigated. Clearly, each approach has its own motivations, advantages and drawbacks. In this article, we present a hybrid centralized/distributed resource discovery solution aiming to get the most out of both approaches. The proposed architecture employs the well-known Constrained Application Protocol (CoAP) and features a number of interesting discovery characteristics including scalability, time and cost efficiency, and adaptability. Using such a solution, network nodes can automatically and rapidly detect the presence of Resource Directories (RDs), via a proactive RD discovery mechanism, and perform discovery tasks through them. Nodes may, alternatively, fall back automatically to efficient fully-distributed discovery operations achieved through Trickle-enabled, CoAP-based technics. The effectiveness of the proposed architecture has been demonstrated by formal analysis and experimental evaluations on dedicated IoT platforms

Making decisions about saving energy in compressed air systems using Ambient Intelligence and Artificial Intelligence

Compressed air systems are often the most expensive and inefficient industrial systems. For every 10 units of energy, less than 1 unit turns into useful compressed air. Air compressors tend to be kept fully on even if they are not (all) needed. The research proposed in this short paper will combinereal time ambient sensing with Artificial Intelligence andKnowledge Management to automatically improve efficiency in energy intensive manufacturing. The research will minimise energy use for air compressors based on real-time manufacturing conditions (and anticipated future requirements). Ambient datawill provide detailed information on performance. Artificial Intelligence will make sense of that data and automatically act. Knowledge Management will facilitate the processing of information to advise human operators on actions to reduce energy use and maintain productivity. The aim is to create new intelligent techniques to save energy in compressed air systems

Toward an Automatic Approach for Ubiquitous Robotic Services Composition

International audienceno abstrac

Robotique Ubiquitaire Sensible au Contexte. Objectifs et défis

International audienceno abstrac

Service-Oriented, User-Centered and Event-Aware Framework for Ambient Intelligence and Internet of Things

International audienceThe Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users' requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment

Approche basée sur la qualité de service pour la composition automatique des services en robotique ubiquitaire

International audienceno abstrac

Context‐aware Dynamic Service Composition in Ubiquitous Environment

International audienceThe service composition aims to provide a variety of high level services. Recent approaches cannot fully satisfy the requirement raised by ubiquitous environment. In this paper, we propose a layered design framework which aims at being flexible and robust to failure service composition. It adopts an abstract way of generating plan using rule-based techniques in order to adapt to the changes occurring on the services and the context of use. The approach optimizes the number of services and the recomposition time in large-scale environment by removing the phase of rediscovery. The framework for service composition and monitoring includes learning mechanism for the service selection, based on an estimation of the reputation for abstract services and the quality (QoS) for concrete services. The proposed approach is tested, under USARSim simulator, on a set of ubiquitous services for assisting elderly or dependant person in a residential environment. The obtained results show the feasibility and the scalability of the approach and a better reactivity to the dynamic and uncertain nature of the ubiquitous environment

Towards an Event-Aware Approach for Ubiquitous Computing based on Automatic Service Composition and Selection

International audienceno abstrac

QoS Based Framework for Ubiquitous Robotic Services Composition

International audienceno abstrac

Energy-centered and QoS-aware services selection for Internet of Things

International audienceAn important challenge to be addressed in the domain of Internet of Things (IoT) is the development of efficient services selection algorithms for an optimal management of both energy and Quality of Service (QoS) in the context of IoT services composition. This issue becomes crucial in the case of large-scale IoT environments composed of thousands of distributed entities. In this paper, an energy-centered and QoS-aware services selection algorithm (EQSA) is proposed for IoT services composition. The proposed selection approach consists of preselecting the services offering the QoS level required for user's satisfaction using a lexicographic optimization strategy and QoS constraints relaxation technique. In order to reduce the energy consumption of a composite service without affecting the user's satisfaction, the most suitable services among the preselected ones are then selected using the concept of relative dominance of services in the sense of Pareto. The relative dominance of a candidate service depends on its energy profile and QoS attributes, and user's preferences. The proposed algorithm has been evaluated through several simulation scenarios. The obtained results show clearly the good performances of the EQSA algorithm in terms of selection time, energy efficiency, composition lifetime, and optimality and its added value in comparison with algorithms dealing separately with QoS and energy consumption