For intelligent systems, the ability to adapt a sensor's sensing capabilities offers promise for reducing numbers, weight, and volume of sensors required. This basic idea is in line with a recent assertion by the well-known roboticist Rodney Brooks, that versatile robots could be used to perform various tasks instead of requiring a large number of specialized robots.In the current work, we consider the concept of a "smart" sensor which could dynamically adapt itself to replace multiple static sensors--within the application area of ingestible smart pills, where small sensors might be required to detect problems such as bleeding or tumours.\\ Simulations were used to evaluate some basic strategies for how to adapt the sensor and their effectiveness was compared; as well, a hardware prototype using LEDs to indicate system switching was prepared.

Orientadora: Profa. Dra. Aline de Oliveira Neves Panazio / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2014. / Redes de sensores sem o (WSN - Wireless Sensor Networks) têm sido usadas na observação de fenômenos, identicação de sistemas, equalização de canais, além de aplicações nas mais diversas áreas. Considerando o caso de redes homogêneas com protocolo ponto a ponto, nas quais os sensores são capazes de processar suas informações e se comunicar com sensores vizinhos, diversos algoritmos adaptativos vêm sendo aplicados no processamento dos dados medidos. Estes algoritmos podem ser supervisionados ou não supervisionados. Buscando estimar parâmetros comuns através de um processamento distribuído, a topologia da rede passa a ser uma característica importante e precisa ser levada em conta nos algoritmos utilizados. Tais algoritmos operam em modo de difusão, considerando a troca de informações entre sensores vizinhos na atualização dos coecientes dos ltros adaptativos de cada sensor. O mapeamento da topologia da rede é feito de forma matricial através das chamadas matrizes de combinação. Neste trabalho, estudamos o impacto da escolha da matriz de combinação no desempenho dos algoritmos supervisionados. No caso de algoritmos não supervisionados, como a única proposta encontrada na literatura considerava um caso bastante restrito em que o algoritmo só poderia ser aplicado a uma rede com topologia em anel e comunicação unidirecional entre os nós, propomos um novo algoritmo capaz de operar em modo de difusão em qualquer topologia, baseado no clássico critério do módulo constante. O algoritmo proposto é simulado em diversas situações, sempre apresentando vantagens em relação a uma rede sem cooperação entre os nós. / Wireless sensor networks (WSN) have been used in the observation of several phenomena, system identication, channel equalization, and others. Considering the case of homogeneous networks with point to point protocol, in which the sensors are able to process their information and communicate with neighbors, various adaptive algorithms have been applied in the processing of measured data. These algorithms can be supervised or unsupervised. Seeking to estimate common parameters across a distributed processing, network topology becomes an important feature and must be taken into account in the algorithms used. Such algorithms operate in diusion mode, that is, considering the exchange of information between sensors to update the coecients of the adaptive lters. Thenetwork topology is mapped through the use of a matrix, denoted combination matrix. In this work, we study the impact of the choice of the combination matrix on the performance of supervised algorithms. In the case of blind methods, the only technique found in the literature was applied to the specic case of a network with ring topology and unidirectional communication between nodes. Thus, we propose a new algorithm capable of operating in diusion mode on any topology, based on the classical constant modulus criterion. The proposed algorithm is simulated in several scenarios, always presenting advantages over a network without cooperation between nodes.

Dans cette thèse, nous proposons des techniques de gestion de données pour économiser l’énergie dans les réseaux de capteurs périodiques basés sur l’architecture de clustering. Premièrement, nous proposons d’adapter le taux d’échantillonnage du capteur à la dynamique de la condition surveillée en utilisant le modèle de one-way ANOVA et des tests statistiques (Fisher, Tukey et Bartlett), tout en prenant en compte l’énergie résiduelle du capteur. Le deuxième objectif est d’éliminer les données redondantes générées dans chaque cluster. Au niveau du capteur, chaque capteur cherche la similarité entre les données collectées à chaque période et entre des périodes successives, en utilisant des fonctions de similarité. Au niveau du CH, nous utilisons des fonctions de distance pour permettre CH d’éliminer les ensembles de données redondantes générées par les nœuds voisins. Enfin, nous proposons deux stratégies actif/inactif pour ordonnancer les capteurs dans chaque cluster, après avoir cherché la corrélation spatio-temporelle entre les capteurs. La première stratégie est basée sur le problème de couverture des ensembles tandis que la seconde prend avantages du degré de corrélation et les énergies résiduelles de capteurs pour ordonnancer les nœuds dans chaque cluster. Pour évaluer la performance des techniques proposées, des simulations sur des données de capteurs réelles ont été menées. La performance a été analysée selon la consommation d’énergie, la latence et l’exactitude des données, et la couverture, tout en montrant comment nos techniques peuvent améliorer considérablement les performances des réseaux de capteurs. / In this thesis, we propose energy-efficient data management techniques dedicated to periodic sensor networks based on clustering architecture. First, we propose to adapt sensor sampling rate to the changing dynamics of the monitored condition using one-way ANOVA model and statistical tests (Fisher, Tukey and Bartlett), while taking into account the residual energy of sensor. The second objective is to eliminate redundant data generated in each cluster. At the sensor level, each sensor searches the similarity between readings collected at each period and among successive periods, based on the sets similarity functions. At the CH level, we use distance functions to allow CH to eliminate redundant data sets generated by neighboring nodes. Finally, we propose two sleep/active strategies for scheduling sensors in each cluster, after searching the spatio-temporal correlation between sensor nodes. The first strategy uses the set covering problem while the second one takes advantages from the correlation degree and the sensors residual energies for scheduling nodes in the cluster. To evaluate the performance of the proposed techniques, simulations on real sensor data have been conducted. We have analyzed their performances according to energy consumption, data latency and accuracy, and area coverage, and we show how our techniques can significantly improve the performance of sensor networks.