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Les Réseaux de capteurs sans fil (WSN) sont une technologie émergente avec des applications potentielles dans divers domaines de la vie quotidienne, tels que la surveillance structurelle et environnementale, la médecine, la surveillance militaire, les explorations robotisées, etc. Les nœuds de capteurs doivent fonctionner pendant une longue période avec des batteries capacité limitée, par conséquent le facteur plus important dans les WSN est la consommation d'énergie. Dans cette thèse, nous proposons des techniques d'optimisation algorithmiques dynamiques, et adaptative pour la réduction de l'énergie. Tout d'abord, un modèle énergétique précis est présenté. Ce modèle repose sur des mesures réelles de courant consommé pour différents scénarios qui peuvent se produire lors de la communication entre les nœud. Il en est conclu que la couche MAC joue un rôle essentiel dans la réduction de l'énergie consommée. Ensuite, un protocole MAC dynamique est présenté. Il adapte de manière dynamique l’intervalle de réveil des nœuds de capteurs à partir d’une estimation du trafic. L’algorithme adaptatif modélisé de façon heuristique pour comprendre le comportement de convergence des paramètres algorithmiques. Le protocole est appliqué sur des réseaux de capteurs corporels et il surclasse les autres protocoles MAC en termes de latence ainsi que de consommation d'énergie ce qui permet donc d'augmenter la durée de vie de trois à six fois. Enfin, une technique basée sur l’optimisation adaptative de la puissance d'émission radio est appliquée sur des canaux variant dans le temps. La puissance de sortie est réglée dynamiquement au meilleur niveau de puissance selon l’état du canal, ce qui diminue la consommation d’un facteur deux. / Wireless Sensor Networks (WSN) are a fast emerging technology with potential applications in various domains of daily-life, such as structural and environmental monitoring, medicine, military surveillance, robotic explorations etc. WSN devices are required to operate for a long time with limited battery capacity, therefore, the most important constraint in WSN is energy consumption. In this thesis, we propose algorithmic-level dynamic and adaptive optimization techniques for energy reduction in WSN. First, an accurate energy model is presented. This model relies on real-time power measurements of various scenarios that can occur during communication between sensor nodes. It is concluded that MAC layer plays a pivotal role for energy reduction. Then, a traffic-aware dynamic MAC protocol is presented which dynamically adapts the wake-up schedule of sensor nodes through traffic estimation. An adaptive algorithm is designed for this purpose that is heuristically modeled to understand the convergence behavior of algorithmic parameters. The proposed protocol is applied to body area networks and it outperforms other low-power MAC protocols in terms of latency as well as energy consumption and consequently increases the lifetime from three to six times. Finally, an SNR-based adaptive transmit power optimization technique is applied under time-varying channels. The output power is dynamically tuned to best power level under slow varying channel, which results in an average gain by two times.

[Truncated abstract] In an urban network, any change to the capacity of a road or an intersection will generally result in some traffic changing its route. In addition the presence of intersections creates the need for frequent stops. These stops increase the fuel consumption by anywhere between thirty to fifty percent as evidenced by published standardised vehicle fuel consumption figures for urban and for country driving. Other components of vehicle operating costs such as tyre and brake wear and time costs will also be increased by varying amounts. Yet almost all methods in use for economic evaluation of urban road projects use open road vehicle operating costs (sometimes factored to represent an average allowance for stopping at intersections) for one year or sometimes two years in the analysis period and then make assumptions about how the year by year road user benefits may change throughout the period in order to complete the analysis. This thesis will describe a system for estimating road user costs in an urban network that calculates intersection effects separately and then adds these effects to the travel costs of moving between intersections. Daily traffic estimates are used with a distribution of the flow rate throughout the twenty-four hours giving variable speed of travel according to the level of congestion at different times of the day. For each link, estimates of traffic flow at two points in time are used to estimate the year-by-year traffic flow throughout the analysis period by linear interpolation or extrapolation. The annual road user costs are then calculated from these estimates. Annual road user benefits are obtained by subtracting the annual road user costs for a modified network from the annual road user costs for an unmodified network. The change in the road network maintenance costs are estimated by applying an annual per lane maintenance cost to the change in lane-kilometres of road in the two networks. The Benefit Cost Ratio is calculated for three discount rates. An estimate of the likely range of error in the Benefit Cost Ratio is also calculated