On the Sampling Frequency of Human Mobility

International audienceIn this paper, we aim at answering the question " at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information? ". Our quest for a response unveils (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. We conduct analyses using fine-grained GPS trajectories of 119 users worldwide. Our findings have potential applications in ubiquitous computing and mobile service design, in terms of energy efficiency, location-based service operations, active probing of subscribers' positions in mobile networks and trajectory data compression

Investigations sur la fréquence d’échantillonnage de la mobilité

Recent studies have leveraged tracking techniques based on positioning technologiesto discover new knowledge about human mobility. These investigations have revealed, amongothers, a high spatiotemporal regularity of individual movement patterns. Building on these findings,we aim at answering the question “at what frequency should one sample individual humanmovements so that they can be reconstructed from the collected samples with minimum loss of information?”.Our quest for a response leads to the discovery of (i) seemingly universal spectralproperties of human mobility, and (ii) a linear scaling law of the localization error with respectto the sampling interval. Our findings are based on the analysis of fine-grained GPS trajectoriesof 119 users worldwide. The applications of our findings are related to a number of fields relevantto ubiquitous computing, such as energy-efficient mobile computing, location-based service operations,active probing of subscribers’ positions in mobile networks and trajectory data compression.Des études récentes ont mis à profit des techniques de suivi basées sur des technologiesde positionnement pour étuder la mobilité humaine. Ces recherches ont révélé, entreautres, une grande régularité spatio-temporelle des mouvements individuels. Sur la base de cesrésultats, nous visons à répondre à la question «à quelle fréquence doit-on échantillonner lesmouvements humains individuels afin qu’ils puissent être reconstruits à partir des échantillonsrecueillis avec un minimum de perte d’information? Notre recherche d’une réponse à cette questionnous a conduit à la découverte de (i) propriétés spectrales apparemment universelles de lamobilité humaine, et (ii) une loi de mise à l’échelle linéaire de l’erreur de localisation par rapportà l’intervalle d’échantillonnage. Nos résultats sont basés sur l’analyse des trajectoires GPS de119 utilisateurs dans le monde entier. Les applications de nos résultats sont liées à un certainnombre de domaines pertinents pour l’informatique omniprésente, tels que l’informatique mobileéconome en énergie, les opérations de service basées sur l’emplacement, le sondage actif despositions des abonnés dans les réseaux mobiles et la compression des données de trajectoire

A Novel Sensor-Free Location Sampling Mechanism

In recent years, mobile device tracking technologies based on various positioning systems have made location data collection an ubiquitous practice. Applications running on smartphones record location samples at different frequencies for varied purposes.The frequency at which location samples are recorded is usually pre-defined and fixed but can differ across applications; this naturally results in big location datasets of various resolutions. What is more, continuous recording of locations results usually in redundant information, as humans tend to spend significant amount of their time either static or in routine trips, and drains the battery of the recording device. In this paper, we aim at answering the question "at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?". Our analyses on fine-grained GPS trajectories from users around the world unveil (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. Building on these results, we challenge the idea of a fixed sampling frequency and present a lightweight, energy efficient, mobility aware adaptive location sampling mechanism. Our mechanism can serve as a standalone application for adaptive location sampling, or as complimentary tool alongside auxiliary sensors (such as accelerometer and gyroscope). In this work, we implemented our mechanism as an application for mobile devices and tested it on mobile users worldwide. The results from our preliminary experiments show that our method adjusts the sampling frequency to the mobility habits of the tracked users, it reliably tracks a mobile user incurring acceptable approximation errors and significantly reduces the energy consumption of the mobile device

Online Trajectory Data Reduction using Connection-preserving Dead Reckoning

Moving objects databases (MODs) store objects ’ trajecto-ries by spatiotemporal polylines that approximate the ac-tual movements given by sequences of sensed positions. De-termining such a polyline with as few vertices as possible under the constraint that it does not deviate by more than a certain accuracy bound from the sensed positions is an algorithmic problem known as trajectory reduction. A specific challenge is online trajectory reduction, i.e. con-tinuous reduction with position sensing in realtime. This particularly is required for moving objects with embedded position sensors whose movements are tracked and stored by a remote MOD. In this paper, we present Connection-preserving Dead Re-ckoning (CDR), a new approach for online trajectory reduc-tion. It outperforms the existing approaches by 30 to 50%. CDR requires the moving objects to temporally store some of the previously sensed positions. Although the storage consumption of CDR generally is small, it is not bounded. We therefore further present CDRM whose storage alloca-tion and execution time per position fix can be adjusted and limited. Even with very limited storage allocations of less than 1 kB CDRM outperforms the existing approach by 20 to 40%

Location update algorithms for position sharing

Mit der immer zunehmenden Anzahl an Smartphones innerhalb der letzten Jahre hat auch deren Einfluss auf unser tägliches Leben immer mehr zugenommen. Unter anderem wurden auch die sogenannten Location Based Services immer beliebter. Diese Location Based Services benötigen die Position eines mobilen Benutzers und finden dann z. B. das nächste Restaurant. Natürlich ist die Sicherheit der Benutzerdaten hierbei ein wichtiges Thema. Die Daten sind meistens auf Location Servern gespeichert, und wenn der Anbieter des Servers kompromittiert oder unsicher ist, können die Daten verschlampt oder gestohlen werden. Aus diesem Grund wurden viele Ansätze zum Verschleiern der tatsächlichen Benutzerposition vorgeschlagen, so auch von Rothermel, Dürr und Skvortsov. Diese Diplomarbeit stellt nun neue Techniken vor, um den eben genannten Basis-Ansatz für das Verschleiern von Benutzerpositionen weiter zu verbessern. Das Hauptziel ist dabei, die Anzahl der Update-Nachrichten zu reduzieren, die vom mobilen Objekt an die Location Server geschickt werden, um die Position des Benutzers aktuell zu halten. Die vorgeschlagenen Techniken werden analysiert, und das entwickelte System wird simuliert, um die Kommunikation zwischen dem mobilen Objekt und den Location Servern, sowie die Kommunikation zwischen den Location Servern und den Location Based Services auszuwerten. Hierbei werden echte Benutzer-Trajektorien aus dem GeoLife-Datensatz verwendet. Die Evaluierung zeigt, dass die Nachrichtenzahl in manchen Fällen um über 90% reduziert werden kann. Hierbei findet jedoch im Hinblick auf den Basis-Ansatz weder ein Verlust der Sicherheit noch der Genauigkeit statt