Localisation indoor à l'aide des capteurs d'un smartphone

L'environnement \textit{indoor} permet un grand nombre de services issus des technologies de l'information et de la communication. La localisation de l'utilisateur, via celle de son smartphone, est donc un élément-clé de cette réussite. Cette thèse s'intéresse au suivi des déplacements de l'utilisateur grâce aux capteurs de mouvement embarqués dans son smartphone. Elle repose sur la détermination du type de locomotion. Nous proposons une solution de navigation \textit{indoor} complète, permettant de proposer à l'utilisateur un chemin jusqu'à sa destination dans n'importe quel bâtiment tout en connaissant sa position à chaque instant, avec une précision de l'ordre du mètre. De façon analogue, nous avons également montré que nous pouvons déterminer le mode de transport d'un utilisateur pour une application de détection de places de parking libres

Multi-features indoor localization

L'environnement \textit{indoor} permet un grand nombre de services issus des technologies de l'information et de la communication. La localisation de l'utilisateur, via celle de son smartphone, est donc un élément-clé de cette réussite. Cette thèse s'intéresse au suivi des déplacements de l'utilisateur grâce aux capteurs de mouvement embarqués dans son smartphone. Elle repose sur la détermination du type de locomotion. Nous proposons une solution de navigation \textit{indoor} complète, permettant de proposer à l'utilisateur un chemin jusqu'à sa destination dans n'importe quel bâtiment tout en connaissant sa position à chaque instant, avec une précision de l'ordre du mètre. De façon analogue, nous avons également montré que nous pouvons déterminer le mode de transport d'un utilisateur pour une application de détection de places de parking libres.Indoor environments present opportunities for a rich set of location-aware services in the information and communications technology (ICT) area. Therefore, accurately localizing a user indoors has become a key enabling technology. This thesis addresses the issue of tracking a user equipped with an off-the-shelf smartphone by exploiting its embedded motion sensors. Leveraging key characteristics of human locomotion, we propose a complete, infrastructure-free indoor navigation solution, allowing a user to navigate any unknown building with meter-level accuracy. Finally, extending our understanding of locomotion to outdoors areas where users are inside vehicles, we design and implement a smartphone application for smart on-street parking

InPReSS: Indoor Plan Reconstruction Using the Smartphone's Five Senses

Electronic ISBN:978-1-5386-3180-5International audienceToday, we can use our smartphones to drive from Los Angeles to New York, yet we can not use them to find our way around Charles de Gaulle Airport. GPS simply does not work indoors and while outside areas are mapped very well, the indoors of most buildings remain a mystery to our favorite navigation apps. While the problem of indoor localization has attracted a lot of attention, with several solutions with meter-level accuracy emerging as research prototypes, the problem of building accurate and detailed indoor maps has been neglected. We present InPReSS, a solution for dynamic indoor map construction without explicit user input. It leverages the sensors available on off-the-shelf smartphones and their ubiquity to construct floor plans as users go about their daily business. The InPReSS approach consists of four steps: 1. Collecting readings from five sensors, tagged with their location from smartphones available in the target building, and divide the floor plan into cells using a Voronoi diagram. 2. Clustering the cells into rooms based on the sensor readings. 3. Identifying doors and ways between adjacent rooms using the Voronoi diagram and user movement traces. 4. Building a two-level graph of the floor plan for navigation

Unlocking the Smartphone's Senses for Smart City Parking

International audienceStudies have shown that in places like New York City drivers often spend over 20 min looking for parking, contributing to as much as 30% of the total traffic. In response, cities like San Francisco have deployed systems capable of pointing drivers to the closest available parking spot. Unfortunately, such systems have gained little traction as they rely on specialized infrastructure that is expensive to build and maintain. We present SmartPark, a smartphone based system that relaxes the requirement for specialized infrastructure by relying on the smartphone's sensors and ubiquitous Wi-Fi and cellular infrastructure. To accomplish this, SmartPark addresses two major challenges, under the constraint of minimum impact on battery life: automatic transportation mode detection and location matching. Solved together, they enable SmartPark to automatically detect when a user pulls out of a parking spot, making it available again. It addresses each challenge using a combination of thorough statistical analysis of the sensor readings and a novel Random Forest based classification algorithm. Experimental results from 12 volunteers, using 7 different smartphones, in 3 different cities show that SmartPark can distinguish 9 different transportation modes with accuracy between 95.57-100%, enabling it to correctly detect unparking events virtually 100% of the time. This is accomplished with a minimum impact on battery life-running SmartPark on a fully charged LG Google Nexus 5 for 5 h straight caused the battery level to drop only about 4%

SmartPark : Se garer dans les Smart Cities ? Un bon créneau pour les smartphones

International audienceDe plus en plus connectées, les villes recherchent un système de détection de places de parking sur smartphone. Pourtant, les coûts de déploiement et de maintenance de l'infrastructure nécessaires aux solutions existantes demeurent beaucoup trop élevés. Nous présentons SmartPark, un système de stationnement en ville sans infrastructure dédiée. SmartPark utilise un système de classification de type Random Forest à partir des capteurs d'un smartphone pour déterminer la libération d'une place de parking. Nous proposons par ailleurs une nouvelle métrique de comparaison de signaux RF environnants, afin de résoudre le problème du changement de véhicule. Nous avons mené des expérimentations à l'aide de 12 volontaires, utilisant 7 smartphones différents et dans 3 villes différentes. Elles ont montré que SmartPark peut identifier 9 modes de transport différents avec une précision de 98,72%, ce qui permet la détection de la libération d'une place de parking dans 29 cas sur 29

Acrux: Indoor Localization Without Strings

International audienceWe present Acrux, the first indoor localization system to achieve meter level accuracy while relying exclusively on a single fix and the sensors commonly found in off-the-shelf smartphones. Acrux uses dead-reckoning, the approach that gives probably the best chance at a completely autonomous indoor localization system. Unfortunately, it has not been mastered on smartphones beyond a few dozen meters due to its inherent integration drift. As a result, all dead-reckoning based solutions in literature require periodic recalibration using input from outside -- attaching strings preventing indoor localization from becoming mainstream. While it is virtually impossible to completely eliminate integration drift, Acrux is the first solution to succeed in dead-reckoning with meter level accuracy for several hundred meters, enough to relax the requirement for periodic recalibration in most indoor scenarios. To accomplish this, Acrux replaces step-counting, the standard approach for measuring distance using sensors, with an approach that measures the speed of locomotion. Although a straightforward accurate estimation of motion speed using the erroneous sensors found on smartphones is infeasible, Acrux combines a novel approach with measurement based analysis to achieve that. Leveraging its excellent dead-reckoning capability, Acrux is shown to provide indoor localization with median error between SI0.7 meter and SI1.2 meter and 98% percentile error of SI3 meter in a dozen of scenarios in 4 different buildings -- without any recalibration

COExiST: Revisiting Transmission Count for Cognitive Radio Networks

International audienceTransmission count, the number of transmissions required for delivering a data packet over a link, is part of almost all state-of-the-art routing metrics for wireless networks. In traditional networks, peer-to-peer interference and channel errors are what define its value for the most part. In cognitive radio networks, however, there is a third culprit that can impact the transmission count: primary user interference. It may be tempting to think of primary user interference as no different than interference caused by other peers. However, unlike peers, primary users do not follow the same protocol and have strict channel access priority over the secondary users. Motivated by this observation, we carry out an empirical study on a USRP testbed for analyzing the impact of primary users. Our measurements show that a primary user has a distinct impact on the transmission count, which the de facto standard approach, ETX, designed for traditional networks, fails to capture. To resolve this, we present COExiST (for COgnitive radio EXpected transmISsion counT): a link metric that accurately captures the expected transmission count over a wireless link subject to primary user interference. Extensive experiments on a five-node USRP testbed demonstrate that COExiST accurately captures the actual transmission count in the presence of primary users -- the 80th percentile of the error is less than 20%

COExiST : une métrique caractérisant la qualité des liens sans fil dans les réseaux de radios cognitives

International audienceLes transmissions sans fil sont loin d’être parfaites car elles dépendent de la qualité des canaux utilisés et notamment des collisions pouvant s'y produire. Plusieurs retransmissions sont alors nécessaires pour pouvoir effectivement acheminer un paquet de données sur un lien sans fil. D` es lors, le nombre total de tentatives, aussi appelé nombre de transmissions par paquet, a été largement utilisé pour évaluer la qualité des liens radio et concevoir des métriques de routage efficaces pour les réseaux multi-sauts. À l'aide d'une plate-forme de test composée de radios logicielles USRP, nous démontrons que la métrique ETX, communément utilisée pour l'estimation de cette quantité dans les réseaux sans fil traditionnels, n'est plus adaptée au contexte des réseaux de radios cognitives. En effet, pour ce type de réseaux, un nouveau phénomène doit être pris en compte. Il s'agit des interférences provenant des utilisateurs primaires qui, contrairement aux utilisateurs secondaires, disposent d'une priorité sur le canal et peuvent réaliser des transmissions a tout instant. Après avoir identifié la manière dont ces interférences affectent le nombre de transmissions par paquet, nous proposons COExiST † , une métrique tenant compte des particularités des utilisateurs primaires. De nouvelles mesures réalisées en environnement réel mettent alors en évidence la précision de COExiST pour estimer le nombre moyen de transmissions par paquet : 80% du temps, l'erreur relative est inférieure a 20%

Unlocking the Smartphone's Sensors for Smart City Parking

An early version of this work appeared in the Proc. of IEEE ICC 2016 (Krieg et al., 2016)International audienceStudies have shown that drivers often spend over 20 min cruising for parking in city centers, accounting for as much as 30% of the traffic congestion. In response, cities like San Francisco have deployed systems capable of providing drivers real-time parking availability information. However, such systems rely on specialized infrastructure whose installation and maintenance costs in the tens of millions of dollars, unaffordable for many cities. We present SmartPark, a system for real-time parking information that relaxes the requirement for specialized infrastructure, relying instead on the smartphone’s sensors and the ubiquitous Wi-Fi and cellular infrastructure. To accomplish this, SmartPark addresses two major challenges, under the constraint of minimum impact on battery life: transportation mode detection and location matching. To minimize initial deployment cost and risk, SmartPark introduces an analytical approach for estimating parking availability even when only a small fraction of users adopt the application. We evaluate SmartPark using simulations and in the wild. Simulation results show that SmartPark, benefiting from as little as 20% adoption rate, can estimate parking availability with accuracy above 90%. Experimental results with the help of 12 volunteers show that SmartPark detects unparking events 97% of the time while triggering zero false positives