Cooperative Synchronization in Wireless Networks

Synchronization is a key functionality in wireless network, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds

Tracking in Wireless Sensor Networks Using Particle Filtering: Physical Layer Considerations

Abstract-In this paper, a new framework for target tracking in a wireless sensor network using particle filters is proposed. Under this framework, the imperfect nature of the wireless communication channels between sensors and the fusion center along with some physical layer design parameters of the network are incorporated in the tracking algorithm based on particle filters. We call this approach "channel-aware particle filtering." Channel-aware particle filtering schemes are derived for different wireless channel models and receiver architectures. Furthermore, we derive the posterior Cramér-Rao lower bounds (PCRLBs) for our proposed channel-aware particle filters. Simulation results are presented to demonstrate that the tracking performance of the channel-aware particle filters can reach their theoretical performance bounds even with relatively small number of sensors and they have superior performance compared to channel-unaware particle filters. Index Terms-Channel-aware signal processing, particle filters, posterior Cramér-Rao lower bound, wireless communication channels, wireless sensor networks (WSNs)

Effiziente Lokalisierung von Nutzern und Geräten in Smarten Umgebungen

The thesis considers determination of location of sensors and users in smart environments using measurements of Received Signal Strength (RSS). The first part of the thesis focuses on localization in Wireless Sensor Networks and contributes two fully distributed algorithms which address the Sensor Selection Problem and provide the best trade-off between energy consumption and localization accuracy among the algorithms considered. Furthermore, the thesis contributes to Device Free Localization an indoor localization concept providing scalable and highly accurate location estimates (prototype: 0.36m² MSE) while using a COTS passive RFID-System and not relying on user-carried sensors

Localization in mobile wireless and sensor networks

[No abstract available

Estimation sous contraintes de communication (algorithmes et performances asymptotiques)

L'essor des nouvelles technologies de télécommunication et de conception des capteurs a fait apparaître un nouveau domaine du traitement du signal : les réseaux de capteurs. Une application clé de ce nouveau domaine est l'estimation à distance : les capteurs acquièrent de l'information et la transmettent à un point distant où l'estimation est faite. Pour relever les nouveaux défis apportés par cette nouvelle approche (contraintes d'énergie, de bande et de complexité), la quantification des mesures est une solution. Ce contexte nous amène à étudier l'estimation à partir de mesures quantifiées. Nous nous concentrons principalement sur le problème d'estimation d'un paramètre de centrage scalaire. Le paramètre est considéré soit constant, soit variable dans le temps et modélisé par un processus de Wiener lent. Nous présentons des algorithmes d'estimation pour résoudre ce problème et, en se basant sur l'analyse de performance, nous montrons l'importance de l'adaptativité de la dynamique de quantification pour l'obtention d'une performance optimale. Nous proposons un schéma adaptatif de faible complexité qui, conjointement, estime le paramètre et met à jour les seuils du quantifieur. L'estimateur atteint de cette façon la performance asymptotique optimale. Avec 4 ou 5 bits de résolution, nous montrons que la performance optimale pour la quantification uniforme est très proche des performances d'estimation à partir de mesures continues. Finalement, nous proposons une approche à haute résolution pour obtenir les seuils de quantification non-uniformes optimaux ainsi qu'une approximation analytique des performances d'estimation.With recent advances in sensing and communication technology, sensor networks have emerged as a new field in signal processing. One of the applications of his new field is remote estimation, where the sensors gather information and send it to some distant point where estimation is carried out. For overcoming the new design challenges brought by this approach (constrained energy, bandwidth and complexity), quantization of the measurements can be considered. Based on this context, we study the problem of estimation based on quantized measurements. We focus mainly on the scalar location parameter estimation problem, the parameter is considered to be either constant or varying according to a slow Wiener process model. We present estimation algorithms to solve this problem and, based on performance analysis, we show the importance of quantizer range adaptiveness for obtaining optimal performance. We propose a low complexity adaptive scheme that jointly estimates the parameter and updates the quantizer thresholds, achieving in this way asymptotically optimal performance. With only 4 or 5 bits of resolution, the asymptotically optimal performance for uniform quantization is shown to be very close to the continuous measurement estimation performance. Finally, we propose a high resolution approach to obtain an approximation of the optimal nonuniform quantization thresholds for parameter estimation and also to obtain an analytical approximation of the estimation performance based on quantized measurements.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

ISEE.U: Distributed online active target localization with unpredictable targets

This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimator. ISEE.U takes noisy distances at each agent and finds a control that maximizes localization accuracy. We do not assume specific target dynamics and, thus, our method is robust when facing unpredictable targets. Each agent computes the control that maximizes overall target position accuracy via a local estimate of the Fisher Information Matrix. We compared the proposed method with a state of the art algorithm outperforming it when the target movements do not follow a prescribed trajectory, with x100 less computation time, even when our method is running in one central CPU