Gestion de l'énergie dans un réseau de capteurs au niveau application

Energy is a key resource in Wireless Sensor Networks (WSNs), especially when sensor nodes are powered by batteries. This thesis is investigates how to save energy of the whole WSN, at the application level, thanks to control strategies, in real time and in a dynamic way. The first energy management strategy investigated is based on Model Predictive Control (MPC). The choice of MPC is motivated by the global objectives that are to reduce the energy consumption of the set of sensor nodes while ensuring a given service, named mission, for the sensor network. Moreover, a set of constraints on the binary control variables and on the sensor modes must be fulfilled. The second energy management strategy at the application level is based on a Hybrid Dynamical System (HDS) approach. This choice is motivated by the hybrid inherent nature of the WSN system when energy management is considered. The hybrid nature basically comes from the combination of continuous physical processes, namely, the charge / discharge of the node batteries; while the discrete part is related to the change in the functioning modes and the Unreachable condition of the nodes. The proposed strategies are evaluated and compared in simulation on a realistic test-case. Lastly, they have been implemented on a real test-bench and the results obtained have been discussed.L'énergie est une ressource clé dans les réseaux de capteurs sans fil (WSNs), en particulier lorsque les nœuds capteurs sont alimentés par des batteries. Cette thèse s'inscrit dans le contexte de la réduction de la consommation de l'énergie d'un réseau de capteurs au niveau application construite au-dessus de ce réseau, grâce à des stratégies de contrôle, en temps réel et de façon dynamique. La première stratégie de gestion de l'énergie considérée s'appuie sur le contrôle prédictif (MPC). Le choix de MPC est motivé par les objectifs globaux qui sont de réduire la consommation d'énergie de l'ensemble des nœuds capteurs tout en assurant un service donné, nommé mission, pour le réseau de capteurs. En outre, un ensemble de contraintes sur les variables de contrôle binaires et sur les nœuds capteur doit être rempli. La deuxième stratégie de gestion de l'énergie au niveau de l'application utilise une approche de contrôle hybride (HDS). Ce choix est motivé par la nature inhérente du système WSN qui est par essence hybride, en particulier lorsque l'on s'intéresse à la gestion de l'énergie. La nature hybride vient essentiellement de la combinaison de processus physiques continus tels la charge et décharge des batteries des nœuds; tandis que la partie discrète est liée à la modification des modes de fonctionnement et l'état Inaccessible des nœuds. Les stratégies proposées sont évaluées et comparées en simulation sur des différents scenarios réalistes. Elles ont aussi \'et\'e mises en œuvre sur un banc d'essai réel et les résultats obtenus ont été discutés

Design Framework for Reliable and Environment Aware Management of Smart Environment Devices

International audienceA smart environment is equipped with numerous devices (i.e., sensors, actuators) that are possibly distributed over different locations (e.g., rooms of a smart building). These devices are automatically controlled to achieve different objectives related, for instance, to comfort, security and energy savings. Controlling smart environment devices is not an easy task. This is due to: the heterogeneity of devices, the inconsistencies that can result from communication errors or devices failure, and the conflicting decisions including those caused by environment dependencies. This paper proposes a design framework for the reliable and environment aware management of smart environment devices. The framework is based on the combination of the rule based middleware LINC and the automata based language Heptagon/BZR (H/BZR). It consists of: an abstraction layer for the heterogeneity of devices, a transactional execution mechanism to avoid inconsistencies and a controller that, based on a generic model of the environment, makes appropriate decisions and avoids conflicts. A case study with concrete devices, in the field of building automation, is presented to illustrate the framework

A multi-modal smart sensing network for marine environmental monitoring

There is an imperative need for long-term, large-scale marine monitoring systems that will allow decisions to be made based on the analysis of collected data to avoid or limit negative impacts on the ecosystem. Modern marine environmental sensing technologies, such as autonomous wireless sensor networks (WSNs), provide the capability to meet the challenges of high spatial and temporal scales. However, the significant amount of data generated from WSNs is a significant challenge for manual analysis. These multitudinous data need to be automatically processed, indexed and catalogued in a smarter way that can be more easily understood, accessed and managed by operators, scientists and policy makers. Moreover, current research works show that WSNs have their own limitations, for example, reliability issues and the fact that they are passive systems and provide context-less data. Thus, it is becoming increasingly clear that in order to adequately monitor marine environments, they need to be characterised from multiple perspectives. Combining multiple technologies and sensing modalities in environmental monitoring programmes can provide not only advantages of reliability and robustness for sensing systems, but also enhanced understanding of environmental processes. In addition, considerable advances can be made if robust sensing technology can be combined with sophisticated methods of data analysis, classification and cataloguing. The aim of this work is to bridge the gap between current aquatic monitoring systems and futuristic ideal large scale multi-modality smart sensing networks for marine environmental monitoring. To illustrate this, a smart sensing system is proposed and two case studies are used to show data processing from in-situ measurements and from camera based visual sensing data automatically using machine learning techniques. Abnormal events detection results from an in-situ sensor and shipping traffic detection results from visual sensor are combined to illustrate the benefit of coupling multiple sensing modalities

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Double smart energy harvesting system for self-powered industrial IoT

312 p. 335 p. (confidencial)Future factories would be based on the Industry 4.0 paradigm. IndustrialInternet of Things (IIoT) represent a part of the solution in this field. Asautonomous systems, powering challenges could be solved using energy harvestingtechnology. The present thesis work combines two alternatives of energy input andmanagement on a single architecture. A mini-reactor and an indoor photovoltaiccell as energy harvesters and a double power manager with AC/DC and DC/DCconverters controlled by a low power single controller. Furthermore, theaforementioned energy management is improved with artificial intelligencetechniques, which allows a smart and optimal energy management. Besides, theharvested energy is going to be stored in a low power supercapacitor. The workconcludes with the integration of these solutions making IIoT self-powered devices.IK4 Teknike

A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

Implementation of an Energy Management Control Strategy for WSNs using the LINC Middleware

International audienceEnergy optimisation in Wireless Sensor Networks (WSNs) is traditionally done either at the sensor node level or at the network level. To obtain even more energy savings , the final application must be considered. Control theory offers promising solutions to tackle this challenge. This paper details an implementation of a control strategy to min-imise energy consumption of a WSN, while ensuring the application Quality of Service (QoS). The application QoS is expressed with the sampling period for sensor nodes and a minimum number of samples that must be available at the application level at each sampling time. With a Model Pre-dictive Control strategy, the lifetime of the WSN is doubled compared to the basic case. The control strategy has been implemented and evaluated on a real test-bed, formed of heterogeneous sensor nodes, thanks to the LINC coordination middleware