Sur la radionavigation dans les villes intelligentes du futur : Le cas des réseaux de capteurs sans fils

摘要

With the development of the internet of things, the number of communicating objects is rapidly increasing especially with the emergence of the ``Smart City'' concept. As in the case of WiFi access points, many of these objects would operate at fixed and known positions. Such information is made available in the Cloud under the Big Data paradigm, thus leading to the so called geo-referencing of the nodes. In such a context, we pose the problem of opportunistic vehicular radio-localization by means of neighborhood discovery and signal processing of received neighboring signals. We propose a novel approach for vehicle self-localization as a contribution to the field of intelligent transportation systems. The proposed solution is opportunistic, passive and non-intrusive regarding the network operations and deployed applications. The thesis defends a localization system that seamlessly integrates with a standard communication stack and covers two layers : physical and link. At physical level, we use an antenna array for collecting and processing the received signals for both identification and localization. As soon as a packet is detected at MAC layer, the corresponding signals are exploited to localize the source with respect to the vehicle body frame. By using the source identifier, its position in the external (or global) frame is looked up in a database that was previously built off-line and made accessible locally or remotely. By assuming that the vehicle orientation is known, the position of the vehicle is then estimated using basis change equations.