No abstract

Abstract. Location awareness of a user or a device has always been considered as a key element for enhancing network performance and improving user experience by enabling efficient mobility and innovative location-based services. In this paper, we present our proposal named WIFE (Wireless Indoor positioning based on Fingerprint Evaluation), a user-based location determination system which utilizes the information of the Signal Strength (SS) received from the surrounding Access Points (APs) inside a building. We focus on a WiFi environment for its low cost and ease of deployment and study fingerprint-based deterministic techniques for their simplicity and reduced processing time and resource requirements. We first address the inherent impairments of an indoor environment which prevent a positioning system from being accurate and then describe our proposed methodology for mitigating them.

International audienceThe emergence of innovative location-oriented services and the great advances in mobile computing and wireless networking motivated the development of positioning systems in indoor environments. However, despite the benefits from location awareness within a building, the implicating indoor characteristics and increased user mobility impeded the implementation of accurate and time-efficient indoor localizers. In this paper, we consider the case of indoor positioning based on the correlation between location and signal intensity of the received Wi-Fi signals. This is due to the wide availability of WLAN infrastructure and the ease of obtaining such signal strength (SS) measurements by standard 802.11 cards. With our focus on the radio scene analysis (or fingerprinting) positioning method, we study both deterministic and probabilistic schemes. We then describe techniques to improve their accuracy without increasing considerably the processing time and hardware requirements of the system. More precisely, we first propose considering orientation information and simple SS sample processing during the training of the system or the entire localization process. For dealing with the expanded search space after adding orientation-sensitive information, we suggest a hierarchical pattern matching method during the real-time localization phase. Numerical results based on real experimental measurements demonstrated a noticeable performance enhancement, especially for the deterministic case which has additionally the advantage of being less complex compared to the probabilistic one

International audienceIndoor positioning has gained more and more interest lately since it offers the possibility of using the location information in several network functionalities improvement or user services deployment. In this paper, we consider the case of WLAN-based indoor positioning for its simplicity and ease of deployment. With our focus on scene analysis methods, we study both deterministic and probabilistic schemes and try to improve their accuracy performance without increasing the processing and hardware requirements of the system. More precisely, orientation-based sampling of WiFi signal strength (SS) measurements and simple pre-processing of these samples are proposed for mitigating the inherent impairments of the wireless medium. Results based on real experimental measurements showed a considerable accuracy increase especially for the deterministic cas

Indoor positioning techniques based on radio fingerprints outstand over other localization methods because of their independence from radio propagation models and costeffectiveness in terms of hardware and deployment requirements. However, their reported best achieved accuracy is bounded due to the random environmental changes which cause the inconsistency between the stored fingerprints and the current radio behavior. In order to overcome this limitation, we propose a cooperative localization scheme, whereby users exchange their real-time signal measurements in order to update and improve their estimated location. The update process relies on a modified version of the neural network structure of Self-Organizing Maps by considering the signal relationship between users. Performance evaluation results demonstrate accuracy improvement over the baseline fingerprinting technique while keeping the communication and complexity overheads low.