Stochastic Modeling and Analysis for Environmentally Powered Wireless Sensor Nodes

Environmental energy is becoming a feasible alternative for many low-power systems, such as wireless sensor nodes. Designing an environmentally powered device faces several challenges: choosing the exact type of the energy harvester, the energy storage elements and determining the duty cycle of the application. With harvesting, the design process becomes even more difficult because it also has to take into account the unpredictability of the energy source. The contribution of this paper is a methodology that facilitates the analysis of energy harvesting nodes. The existing modeling strategies for battery powered systems are not suitable because they do not capture the uncertainty of the power source. Also, the metrics of interest for battery powered devices are different, as opposed to the harvesting powered ones: in the former case we search to maximize the system lifetime, while in the latter case a more expressive goal is to increase the system availability

Modeling and Assessing an Energy-Aware Power-Supply for Wireless Sensor Nodes

[EN] Este trabajo presenta el modelado y evaluación de un sistema de alimentación para el sensor de la plataforma Iris[EN] Wireless sensors networks can be deployed in remote locations due to they do not need a fixed infrastructure. Therefore, energy scavenging systems are really important to provide the energy necessary to the sensor nodes and thus maximize its lifetime. This work presents the modeling and assessing of an energy-aware power-supply system for the Iris platform sensor. Theoretical models have been developed in order to estimate the energy in the energy storage supercapacitor depending on the incoming and outgoing energy. These models can be used to verify that the power-supply system provides enough energy to the sensor node under the most adverse weather conditions, and thus assuring the perpetual operation of the sensor nodes without human intervention. Also, these models will be implemented in a software module that makes possible the estimation of the sensor nodes¿ lifetime in function of their actual state of energy. The theoretical results given by these models have been compared with the results obtained with the real circuit. The comparison between both proves that the theoretical models are valid for the prediction of the future estate of energy based on the actual estate of energy.Álvarez Álvarez, J. (2009). Modeling and Assessing an Energy-Aware Power-Supply for Wireless Sensor Nodes. http://hdl.handle.net/10251/27229.Archivo delegad

Maximising microprocessor reliability through game theory and heuristics

PhD ThesisEmbedded Systems are becoming ever more pervasive in our society, with most routine daily tasks now involving their use in some form and the market predicted to be worth USD 220 billion, a rise of 300%, by 2018. Consumers expect more functionality with each design iteration, but for no detriment in perceived performance. These devices can range from simple low-cost chips to expensive and complex systems and are a major cost driver in the equipment design phase. For more than 35 years, designers have kept pace with Moore's Law, but as device size approaches the atomic limit, layouts are becoming so complicated that current scheduling techniques are also reaching their limit, meaning that more resource must be reserved to manage and deliver reliable operation. With the advent of many-core systems and further sources of unpredictability such as changeable power supplies and energy harvesting, this reservation of capability may become so large that systems will not be operating at their peak efficiency. These complex systems can be controlled through many techniques, with jobs scheduled either online prior to execution beginning or online at each time or event change. Increased processing power and job types means that current online scheduling methods that employ exhaustive search techniques will not be suitable to define schedules for such enigmatic task lists and that new techniques using statistic-based methods must be investigated to preserve Quality of Service. A new paradigm of scheduling through complex heuristics is one way to administer these next levels of processor effectively and allow the use of more simple devices in complex systems; thus reducing unit cost while retaining reliability a key goal identified by the International Technology Roadmap for Semi-conductors for Embedded Systems in Critical Environments. These changes would be beneficial in terms of cost reduction and system exibility within the next generation of device. This thesis investigates the use of heuristics and statistical methods in the operation of real-time systems, with the feasibility of Game Theory and Statistical Process Control for the successful supervision of high-load and critical jobs investigated. Heuristics are identified as an effective method of controlling complex real-time issues, with two-person non-cooperative games delivering Nash-optimal solutions where these exist. The simplified algorithms for creating and solving Game Theory events allow for its use within small embedded RISC devices and an increase in reliability for systems operating at the apex of their limits. Within this Thesis, Heuristic and Game Theoretic algorithms for a variety of real-time scenarios are postulated, investigated, refined and tested against existing schedule types; initially through MATLAB simulation before testing on an ARM Cortex M3 architecture functioning as a simplified automotive Electronic Control Unit.Doctoral Teaching Account from the EPSRC

コスト効率のよい農業用土壌モニタリングネットワークのシステム設計

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 瀬崎 薫, 東京大学准教授 川原 圭博, 国立情報学研究所教授 安達 淳, 東京大学准教授 落合 秀也, 東京大学教授 溝口 勝University of Tokyo(東京大学

Prédiction et gestion de l’énergie dans un réseau de capteurs sans fil récolteurs d’énergie vibratoire pour les applications industrielles de l’internet des objets

La question de l’autonomie énergétique des capteurs sans fil (WS pour Wireless Sensor), indispensables pour l’automatisation de nombreux procédés industriels, est aujourd’hui une limite fondamentale dans l’atteinte des objectifs de l’industrie 4.0. Pour surmonter cette limite, la piste de solution la plus prometteuse est celle de la récolte de l’énergie ambiante (EH pour Energy Harvesting). L’EH consiste à identifier une source d’énergie primaire (soleil, vibrations, ondes radiofréquences, chaleur, etc.), disponible dans l’environnement immédiat du capteur et de la transformer en énergie électrique pour son alimentation. Cette thèse est une contribution dans ce domaine de recherche en pleine expansion, pour des applications dans l’environnement industriel. Les vibrations qui abondent dans la plupart des procédés industriels sont considérées comme source d’alimentation des WS capables de remplacer les capteurs filés actuellement utilisés. Prenant en considération le caractère aléatoire de la quantité d’énergie récoltable, deux contributions majeures sont proposées dans cette thèse à savoir la conception d’un Prédicteur de l’Énergie Récoltable des vibrations (PERV) et la mise en place d’une solution permettant de gérer efficacement l’énergie récoltée à travers un Protocole Hiérarchique à Équilibrage d’Énergie (PHEE). La conception du PERV est basée sur des données de vibrations enregistrées à 12 emplacements différents, et ce pendant un mois, sur le processus de concassage des minerais par un broyeur semiautogène. La périodicité observée dans les signaux est exploitée pour minimiser la quantité de données devant être stockées pour l’estimation de la puissance à un instant donné. Les performances du PERV sont ensuite comparées à un prédicteur de l’état de l’art le EWMA (Exponentially Weighted Moving-Average qui utilise l’historique des données d’énergie pour estimer les quantités d’énergie récoltable dans le futur) et il est obtenu que l’erreur quadratique moyenne pour les 12 points de mesure subie des améliorations allant de 10 % à 90.5 % comparé au prédicteur EWMA. Le PERV permet ainsi d’augmenter la précision dans la prédiction tout en réduisant la quantité des données devant être stockées. Sous la base de l’énergie prédite, le PHEE est conçu avec pour objectif d’optimiser à la fois la Qualité de Service individuelle de chacun des noeuds, mais aussi celle du réseau en entier. De façon plus spécifique, sous la base de l’énergie prédite, les noeuds capteurs contrôlant le procédé sont capables d'opérer de façon perpétuelle lorsque le coût énergétique par cycle de mesure est inférieur à 160

Adaptive power management in energy harvesting systems

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal utility is obtained while respecting the limited and time-varying amount of available energy. Instead of solving the optimization problem on-line which may be prohibitively complex in terms of running time and energy consumption, we propose a parameterized specification and the computation of a corresponding optimal on-line controller. The efficiency of the new approach is demonstrated by experimental results and measurements on a sensor nod

ABSTRACT Adaptive Power Management in Energy Harvesting Systems

Recently, there has been a substantial interest in the design of systems that receive their energy from regenerative sources such as solar cells. In contrast to approaches that attempt to minimize the power consumption we are concerned with adapting parameters of the application such that a maximal utility is obtained while respecting the limited and time-varying amount of available energy. Instead of solving the optimization problem on-line which may be prohibitively complex in terms of running time and energy consumption, we propose a parameterized specification and the computation of a corresponding optimal on-line controller. The efficiency of the new approach is demonstrated by experimental results and measurements on a sensor node. 1