Os Wireless sensor networks (WSNs) evoluíram a partir da idéia de que sensores sem fio podem ser utilizados para coletar informações de ambientes nas mais diversas situações. Os primeiros trabalhos sobre WSNs foram desenvolvidos pelo Defense Advanced Research Projects Agency (DARPA)1, com o conceito de Smart Dust baseados em microelectromechanical systems (MEMS), dispositivos com capacidades de detectar luminosidade, temperatura, vibração, magnetismo ou elementos químicos, com processamento embarcado e capaz de transmitir dados via wireless. Atualmente tecnologias emergentes têm aproveitado a possibilidade de comunicação com a World Wide Web para ampliar o rol de aplicações desta tecnologia, dentre elas a Internet das Coisas (Internet of Things) IoT. Esta pesquisa estuda a implementação de um novo algoritmo e protocolo que possibilita o encaminhamento dos dados coletados nos microsensores em cenários de redes ad hoc com os sensores distribuídos aleatoriamente, em uma área adversa. Apesar de terem sido desenvolvidos vários dispositivos de hardware pela comunidade de pesquisa sobre WSN, existe um esforço liderado pela Internet Engineering Task Force (IETF)2, na implementação e padronização de protocolos que atendam a estes mecanismos, com limitações de recursos em energia e processamento. Este trabalho propõe a implementação de novos algoritmos de encaminhamento de pacotes utilizando o conceito de códigos convolucionais. Os resultados obtidos por meio de extensivas simulações mostram ganhos em termos da redução de latência e do consumo de energia em relação ao protocolo AODV. A complexidade de implementação é extremamente baixa e compatível com os poucos recursos de hardware dos elementos que usualmente compõem uma rede de sensores sem fio (WSN). Na seção de trabalhos futuros é indicado um extenso conjunto de aplicações em que os conceitos desenvolvidos podem ser aplicados. / Wireless sensor networks (WSNs) have evolved from the idea that small wireless sensors can be used to collect information from the physical environment in a large number of situations. Early work in WSNs were developed by Defense Advanced Research Projects Agency (DARPA)1, so called Smart Dust, based on microelectromechanical systems (MEMS), devices able to detect light, temperature, vibration, magnetism or chemicals, with embedded processing and capable of transmitting wireless data. Currently emerging technologies have taken advantage of the possibility of communication with the World Wide Web to expand to all applications of this technology, among them the Internet of Things IoT. This research, studies to implement a new algorithm and protocol that allows routing of data collected in micro sensors in ad hoc networks scenarios with randomly distributed sensors in adverse areas. Although they were developed several hardware devices by the research community on WSN, there is an effort led by Internet Engineering Task Force (IETF)2, in the implementation and standardization of protocols that meet these mechanisms, with limited energy and processing resources. This work proposes the implementation of new packets forwarding algorithms using the concept of convolutional codes. The results obtained by means of extensive simulations show gains in terms of latency and energy consumption reduction compared to the AODV protocol. The implementation complexity is extremely low and compatible with the few hardware resources usually available in the elements of a wireless sensor network (WSN). In the future works section a large set of applications for which the developed concepts can be applied is indicated.

Made available in DSpace on 2016-04-04T18:31:39Z (GMT). No. of bitstreams: 1 Jose Luis Pagotto.pdf: 3218175 bytes, checksum: df0421d433f5d82aa4e7bdac351f769a (MD5) Previous issue date: 2013-12-18 / The signal propagation between nodes of a network of wireless sensors in an industrial environment can suffer interference from the environment. In this context, the present work shows a study to define an algorithm that is able to adapt the behavior of a wireless sensor network operating within an industrial environment. This study was conducted in environments with specific characteristics, and the results led to the development of an algorithm to adapt communication according to the variability of the environment. A possible benefit of this algorithm is the increase of the reliability of communication in such environments. / A propaga??o do sinal entre os n?s de uma rede de sensores sem fio em um ambiente industrial pode sofrer interfer?ncias do meio. Nesse contexto, este trabalho apresenta um estudo realizado para definir um algoritmo que seja capaz de adequar o comportamento de uma rede de sensores sem fio operando dentro de um ambiente industrial. Este estudo foi realizado em ambientes com caracter?sticas espec?ficas, sendo que os resultados permitiram o desenvolvimento de um algoritmo para adequa??o da comunica??o ? variabilidade do ambiente. Um poss?vel benef?cio advindo deste algoritmo consiste em um aumento da confiabilidade da comunica??o em rela??o ao ambiente.

como cidades inteligentes e Internet das coisas demonstram que esta tecnologia está evoluindo em larga escala. Consequentemente, diversas aplicações desta tecnologia estão sendo desenvolvidas, muitas delas são altamente dependentes de redes de sensores sem fio, pois fazem a coleta de dados em ambientes inóspitos ou de difícil acesso. Para que estes dados coletados de diversos dispositivos possam ser analisados em conjunto é preciso que as coletas sejam simultâneas ou em intervalos próximos de tempo, o que implica que toda a rede de sensores tenha uma elevada precisão no sincronismo. Ademais, diversos protocolos de comunicação sem fio utilizam o sincronismo para estabelecer o compartilhamento do meio de propagação, tendo assim uma maior eficiência na troca de dados. Ao observar a importância de atender a essa necessidade de sincronização de tempo entre dispositivos usados em redes de sensores sem fio, este trabalho se concentra na proposta, implementação e teste de um serviço de sincronização de tempo para redes de sensores sem fio de baixa potência usando relógios de baixa frequência em tempo real em cada nó. Para implementar este serviço, são propostos três algoritmos baseados em estratégias diferentes para alcançar a sincronização desejada. O primeiro baseia-se em uma métrica simples de correção adaptativa; o segundo baseia-se em um mecanismo de predição; já o terceiro utiliza um mecanismo mais complexo, a correção analítica. Todos os algoritmos tem o mesmo objetivo: fazer com que os relógios dos nós sensores convirjam de forma ágil, em seguida, mantê-los com a maior similaridade possível. O objetivo deste trabalho é apresentar o melhor método que garanta a sincronização, mantendo o baixo consumo de energia em uma rede de sensores. Os resultados experimentais fornecem evidências do sucesso no cumprimento deste objetivo, bem como fornece meios para comparar estas três abordagens considerando os melhores resultados de sincronização e os seus custos em termos de consumo de energia. / Environmental monitoring systems are gaining more and more space, concepts such as smart cities and the Internet of things demonstrate that this technology has been developing a lot. Consequently, many applications of this technology are being developed, many of them are dependent on wireless sensor networks, which collect data in inhospitable or difficult-to-access environments. In order to these collected data from several devices to be analyzed together it is necessary that the data collection be simultaneous or at close intervals, which implies that the entire network of sensors has a high precision in the synchronism. In addition, several wireless communication protocols use the synchronism to establish sharing of medium networks, thus having a greater efficiency in the exchange of data. Observing the importance of time synchronization, this work focuses in proposing, implementing and testing time synchronization protocols for low power wireless sensor networks using real time low frequency clocks. To implement this service, three algorithms based on different strategies are proposed to achieve the desired synchronization. The first is based on the simple metric for self-correction; the second is based on a prediction mechanism; while the third uses a more complex mechanism for analytical correction. All the algorithms have the same goal: to make the clock of the sensor nodes converge in an agile way, then to keep them with the greatest possible similarity. The objective of this work is to present the best method to guarantee the synchronization, keeping the low power consumption in a network, sporadically, transmissions. The experimental results provide evidence of success in achieving this goal, as well as providing means to compare these three approaches considering the best synchronization results and their costs in terms of energy consumption. Keywords: Internet of things.

Dans cette thèse, il s'agit de proposer un protocole MAC qui prend en compte certaines spécificités des réseaux de capteurs sans fil linéaires. Ce protocole MAC est basé sur des jetons circulants qui donnent aux noeuds le droit d’accès au canal de transmission. Nous évaluons à partir de ce protocole l’impact des interférences et de la connectivité sur un réseau linéaire. Cette évaluation nous permet de proposer des techniques de clustering dans un réseau de capteurs sans fil linéaire. Nous supposons d’abord un clustering basé sur une exploitation logique du voisinage des noeuds capteurs. Nous considérons dans ce cas, un modèle de propagation déterministe qui ne tient pas en compte la complexité du milieu. Ensuite, nous considérons une technique de clustering dans laquelle le modèle de propagation prend en compte les fluctuations qui rendent le milieu complexe. / In this thesis, we propose a MAC protocol that takes into account the specific characteristics of LSNs. This MAC protocol is based on tokens circulating that give the right to nodes to access to the transmission channel. We evaluate from this protocol the impact of interference and connectivity in the LSN. This evaluation allows us to propose clustering techniques in a LSN. We assume first a clustering based on a logical exploitation of the vicinity of the sensor nodes. In this case, we assume that a deterministic propagation model that does not take into account the complexity of the environment. Next, we consider a clustering technique wherein the propagation model takes into account the fluctuations that make complex the medium.

L'avènement de l'Internet des Objets a permis de déployer de nombreux réseaux de capteurs sans-fil. Ces réseaux sont utilisés dans des domaines aussi variés que l'agriculture, l'industrie ou la ville intelligente, où ils permettent d'optimiser finement les processus. Ces appareils sont le plus souvent alimentés par des piles ou batteries, ce qui limite leur autonomie. De plus, il n'est pas toujours possible ou financièrement viable de changer ou recharger les batteries. Une solution possible est d'alimenter ces capteurs en récupérant l'énergie présente dans l'environnement alentour. Ces sources d'énergie sont cependant peu fiables, et le capteur doit être capable d'éviter de vider complètement sa réserve d'énergie. Afin de moduler sa consommation d'énergie, le capteur peut adapter sa qualité de service à ses capacités énergétiques. L'appareil peut ainsi fonctionner en continu sans interruption de service. Cette thèse présente les méthodes utilisées pour la conception d'un capteur entièrement autonome alimenté par récupération d'énergie ambiante, communiquant sur un réseau longue portée LoRa. Afin d'assurer l'alimentation électrique, une carte permettant de récupérer de l'énergie depuis plusieurs sources d'énergie simultanément a été conçue. Un module logiciel de gestion d'énergie a ensuite été développé afin de calculer un budget énergétique que le capteur peut dépenser, et choisir la meilleure manière de dépenser ce budget pour exécuter une ou plusieurs tâches. Ce travail a ainsi permis le développement d'un prototype de produit industriel entièrement autonome en énergie. / The advent of the Internet of Things has enabled the roll-out of a multitude of Wireless Sensor Networks. These networks can be used in various fields, such as agriculture, industry or the smart city, where they facilitate fine optimization of processes. These devices are often powered by primary or rechargeable batteries, which limits their battery life. Moreover, it is sometimes not possible or financially viable to change and/or recharge these batteries. A possible solution is to harvest energy from the environment to power these sensors. But these energy sources are unreliable, and the sensor must be able to prevent the complete depletion of its energy storage. In order to adapt its energy consumption, the node can match its quality of service to its energetical capabilities. Thus, the device can continuously operate without any service interruption. This thesis presents the methods used for the conception of a completely autonomous sensor, powered by energy harvesting and communicating through a long range LoRa network. In order to ensure its power supply, a board has been designed to harvest energy from multiple energy sources simultaneously. A power management software module has then been developed to calculate an energy budget the sensor can use, and to choose the best way to spend this budget over one or multiple tasks. This work has enabled the development of an energy autonomous industrial sensor prototype.

Le cadre général de la thèse concerne les réseaux de capteurs et la sécurisation des communications sans fil pour la mise en œuvre de systèmes fiables de surveillance orienté santé. Plus précisément, ce travail de thèse présente un nouveau système de surveillance biomédical à base de réseau de capteurs sans fil, pour la mesure de l'activité électrique du cerveau. Un réseau de capteurs sans fil de type EEG (électroencéphalogramme) permet de surveiller les ondes cérébrales spontanées, y compris les ondes normales et anormales, des patients souffrant de différents types d'épilepsie. Un capteur sans fil enregistre les signaux du patient (via le cuir chevelu) et filtre ces signaux et leurs données en parallèle selon un traitement approprié des ondes cérébrales. Il est dès lors possible de prédire la gravité d'une attaque épileptique à venir. Une première approche est proposée pour analyser les anomalies des ondes cérébrales et déclencher des alertes le cas échéant. De tels systèmes peuvent permettre de sauver de nombreux patients en prédisant l’arrivée les crises avant qu'elles ne surviennent et éviter ainsi les accidents et les comportements à risque lors d'une crise d'épilepsie. De plus, l’approche peut être utiliser pour d'autres mesures de diagnostic médical. Une autre approche basé sur l’apprentissage pour la prédiction en utilisant les réseaux de neurones de type FFNN (Feed Forward Neural Network ) est également présentée. Par ailleurs, Les approches de prédiction, exploitent la norme IEEE802.11n pour la transmission des données avec un protocole de confidentialité pour la sécurisation des communications. La mise en œuvre de la sécurité peut réduire considérablement le temps de la prédiction et retarder les signaux d’alerte des crises. Les mesures effectuées permettent la calibration des algorithmes de prédiction pour tenir compte des délais introduits par la sécurisation des communications. / The general framework of the thesis concerns sensor networks and the privacy protocols for wireless communications in the implementation of reliable healthcare systems. More precisely, it presents a novel biomedical wireless sensor Network monitoring system, as a predictor and advance sensitive portable electroencephalogram (EEG). The EEG wireless sensor network proposed to monitor spontaneous brain waves, including normal and abnormal waves, for the patients suffering from different types of epilepsy. The biomedical epilepsy wireless sensor Network monitoring system (WSN-EEG) read’s signals from a wireless sensor network on the patient scalp, and filter these signals to run parallel data processing for the brain waves. However, the predicting procedure for the severity of the forthcoming epileptic attack based on, a proposed mathematical model, which analyses the abnormality in the brain waves and alerts by giving signals for the patient. This method can save many patients by predicting the seizure before it occurs and helps them from different injuries and risky behavior arising during epilepsy attack. In addition, the proposed approaches can use the patient data for further medical diagnosis measures. Another approach is proposed as a learning-based approach for prediction using Feed Forward Neural Network (FFNN) for the alert system. The research used the IEEE802.11n as a communication method for the wireless sensor networks and measure the IEEE802.11n security performances as privacy protocol for data transmission of the proposed systems. The measurements indicated the calibration of the prediction algorithms to take account of the delays introduced by the security of the communications in the data transmission and seizure prediction which might significantly reduce prediction time and delay the alert signals.

Sensor networks use a group of collaborating sensor nodes to collect information about real world phenomena. Sensor nodes use low-power short-range radio links to communicate with each other. Communication between sensor nodes shows significant variation over time and space. This can lead to unreliable and unpredictable network performance. These dynamic and lossy characteristics of wireless links pose major challenges for building reliable sensor networks and raise new issues that data delivery protocols must address. This thesis addresses the problems of designing protocols to overcome time-varying environmental conditions that lead to unpredictable network performance. The goal is to provide reliable data delivery in sensor networks and to minimise energy use. The major contributions of this thesis are: measuring the performance of wireless links in field trials on a time scale of weeks; systematic analysis of strengths and weaknesses of existing data delivery protocols; and the design, implementation and testing of a novel autonomic communication framework. We have measured link quality over time in experiments in unattended outdoor environments. Most previous work focused on spatial properties and experiments were not extensive, only lasting for a few hours. Besides common phenomena found in other work, such as the variation of network performance over time and the existence of asymmetric links, we find that links are independent over long time scales, and performance patterns of links are different. We also analyse the performance of data delivery protocols that use different techniques to improve reliability in sensor networks. Through systematic analysis of strengths and weaknesses of existing data delivery strategies, we find that networks using a single technique can only perform well for a limited range of link conditions. Different strategies are required in different operating conditions. Based on these experimental and theoretical studies, a novel autonomic communication framework (ACF) for wireless sensor networks is proposed. Nodes in this ACF are able to change their behaviour to adapt to time-varying environments so that optimal network performance can be achieved. Our framework provides a holistic solution for reliable data delivery to overcome time-varying wireless links. Our implementation and experimental evaluations demonstrate that this holistic framework is effective for reliable and energy-efficient data delivery in realistic sensor network settings.

Target tracking is one of the typical applications of wireless sensor networks: a large number of spatially deployed sensor nodes collaboratively sense, process and estimate the target state (e.g., position, velocity and heading). This thesis aimed to develop the collaborative information processing techniques that jointly address information processing and networking for the distributive estimation of target state in the highly dynamic and resources constrained wireless sensor networks. Taking into account the interplay between information processing and networking, this thesis proposed a collaborative information processing framework. The framework integrates the information processing which is responsible for the representation, fusion and processing of data and information with networking which caters for the formation of network, the delivery of information and the management of wireless channels. Within the proposed collaborative information processing framework, this thesis developed a suite of target tracking algorithms on the basis of the recursive Bayesian estimation method. For tracking a single target in wireless sensor networks, this thesis developed the sequential extended Kalman filter (S-EKF), the sequential unscented Kalman filter (S-UKF) and the Particle filter (PF). A novel extended Kalman filter and Particle filter hybrid algorithm, named as EKPF was also developed. The simulation results showed that the EKPF outperformed other three algorithms in terms of tracking accuracy and robustness. Moreover, to help evaluate the performance of the developed tracking algorithms, the posterior Cramer-Rao lower bound (PCRLB) which is the theoretical lower bound on the mean square error of the target state estimation was also computed. To tackle the measurement origin uncertainty in practical target tracking in wireless sensor networks, this thesis designed a Particle filter and probability density association filter (PDAF) hybrid algorithm, named as PF-PDAF for tracking a single target under the dual assumptions of clutter and missed detections. The PF-PDAF combines the advantages of PDAF algorithm in effectively solving the data association problem with the merits of PF that can accommodate the general non-Gaussian, nonlinear state space model. The PCRLB under measurement origin uncertainty was also derived and computed. For multiple target tracking in wireless sensor networks, this thesis designed a Particle filter and joint probabilistic data association filter (JPDAF) hybrid algorithm, named as PFJPDAF. The PF-JPDAF algorithm extends the traditional JPDAF to solve the general nonlinear non-Gaussian multiple targets tracking problems in wireless sensor networks. In the highly energy and communication bandwidth constrained wireless sensor networks, a critical consideration is that the information processing needs to be distributive. By adopting the hierarchical network architecture to achieve dynamic sensor nodes clustering and utilizing the Gaussian mixture model (GMM) to propagate estimation results amongst sensor clusters, this thesis developed the distributive PF, the distributive EKPF, the distributive PF-PDAF and the distributive PF-JPDAF tracking algorithms. Moreover, this thesis proposed a composite objective function incorporating both the information utility and the energy consumption measures to facilitate the sensing nodes selection in the distributive tracking algorithms. This composite objective function enables the distributive tracking algorithms to achieve the desirable tracking accuracy while still maintaining the lower energy consumption. / Thesis (Ph.D.) - University of Adelaide, School of Electrical and Electronic Engineering, 2008

Cette thèse est une tentative vers la conception de la bande Ka émetteur-récepteur RF pour les réseaux de capteurs sans fil (WSN), pour lesquelles la consommation d'énergie, le coût et la taille sont des paramètres critiques. Au sens de la consommation d'énergie, un transmetteur RF est la partie la plus cruciale d'un nœud de capteur. Nous avons choisi STMicroelectronics 90nm global purpose (GP) pour atteindre la technologie CMOS à faible puissance, faible coût et de petite taille. Pour la première fois, nous avons introduit la bande Ka dans le context de WSN, a fin de bénéficier de l'immunité élevée du réseau et la petite taille antenne. Étant donné que la technologie que nous avons choisi et du kit associé fonderie de conception n'est pas pour la conception RF, nous avons été obligés de mettre au point un outil de conception individuelle pour la bande à ondes millimétriques. De cette façon, nous avons développé une solution simple et précise le modèle MOS transistor, comprenant charge et le modèle de capacité, modèle de bruit et le modèle complet des effets parasites. Nous avons proposé une nouvelle structure pour les inducteurs de la ligne de transmission et un modèle précis de RLGC a été développé pour la conception et la simulation de ces inducteurs. Et puis par la simulation de la pleine d'onde (full wave) électromagnétique dans le logiciel HFSS, nous avons extrait des parameters du modele d'incucteurs , et d'autres éléments passifs, telles que des pads RF et T-jonctions. Comme notre première expérience, nous avons conçu et optimisé une LNA à 30 GHz, en utilisant notre outil de conception. Le LNA conçu a été fabriqué dans STMicroelectronics 90nm global dans le processus de GP CMOS et a été mesurée dans le laboratoire IMEP. Les résultats des mesures montrent 10dB gain de puissance et de 4,8 dB figre bruit (noise figure) avec 4mW DC la consommation de puissance. Dans l'étape suivante, nous avons conçu et optimisé mieux 30GHz LNA. La simulation post-layout montre 13.9dB gain de puissance et 3.6d figre bruit, avec seulement 3 mW de consommation de puissance. Nous avons proposé un lien simple radio et un structure simple a ete presente pour le récepteur_émetteur. Dans le récepteur, nous avons utilisé la structure hétérodyne, ou dans la quelle nous avons utilise de l'idee de Mixer Harmonique paire et oscillateur couple, à surmonter de nombreux problèmes se pose en mm bande des ondes dans la technologie CMOS. Le Mixer a été conçu en utilisant les résultats d'analyse et de simulation dans le kit de conception de fonderie: 4dB gain de conversion et de 5,8 double side band figre de bruit avec 2.2Mw consommation de puissance, un excellent résultat en comparaison avec les œuvres similaires rapports comme IF Stage 2GHz qui a été conçu comme multi-slice-amplificateur de la chaîne de detection pour accroître (ugmenter)la performance du récepteur et d'atteindre plus faible consommation d'énergie. Enfin, le récepteur a été simulé dans MATLAB et--87dBm de sensibilité, 890KHz de bande passante, avec 6.65mW consommation d'énergie sont obtenus. L'émetteur a été conçu aussi simple que possible, en utilisant idée power oscillateur, délivrant 6mW puissance RF de l'antenne. L'émetteur a généralement les 25% de power efficacité qui est très bon résultat en comparaison avec les œuvres déclarées.

Wireless sensor networks are attractive largely because they need no wired infrastructure. But precisely this feature makes them energy constrained, and the consequences of this hard energy constraint are the overall topic of this thesis. We are in particular concerned with principles for energy efficient wireless communication and the energy-wise trade-off between sensing and radio communication. Radio transmission between sensors incurs both a fixed energy cost from radio circuit processing, and a variable energy cost related to the level of radiated energy. We here find that transmission techniques that are otherwise considered efficient consumes too much processing energy. Currently available sensor node radios typically have a maximum output power that is too limited to benefit from transmission-efficient, but processing-intensive, techniques. Our results provide new design guidelines for the radio output power. With increasing transmission energy -- with increasing distance -- the considered techniques should be applied in the following order: output power control, polarisation receiver diversity, error correcting codes, multi-hop communication, and cooperative multiple-input multiple-output transmissions. To assess the measurement capability of the network as a whole, and to facilitate a study of the sensing-communication trade-off, we devise a new metric: the network measurement capacity. It is based on the number of different measurement sequences that a network can provide, and is hence a measure of the network's readiness to meet a large number of possible events. Optimised multi-hop routing under this metric reveals that the energy consumed for sensing has decisive impact on the best multi-hop routes. We also find support for the use of hierarchical heterogeneous network structures. Model parameter uncertainties have large impact on our results and we use probability theory as logic to include them consistently. Our analysis shows that common assumptions can give misleading results, and our analysis of radio channel measurements confirms the inadequacy of the Rayleigh fading channel model. / wisenet