Exploiting Device-to-Device Communications to Enhance Spatial Reuse for Popular Content Downloading in Directional mmWave Small Cells

With the explosive growth of mobile demand, small cells in millimeter wave (mmWave) bands underlying the macrocell networks have attracted intense interest from both academia and industry. MmWave communications in the 60 GHz band are able to utilize the huge unlicensed bandwidth to provide multiple Gbps transmission rates. In this case, device-to-device (D2D) communications in mmWave bands should be fully exploited due to no interference with the macrocell networks and higher achievable transmission rates. In addition, due to less interference by directional transmission, multiple links including D2D links can be scheduled for concurrent transmissions (spatial reuse). With the popularity of content-based mobile applications, popular content downloading in the small cells needs to be optimized to improve network performance and enhance user experience. In this paper, we develop an efficient scheduling scheme for popular content downloading in mmWave small cells, termed PCDS (popular content downloading scheduling), where both D2D communications in close proximity and concurrent transmissions are exploited to improve transmission efficiency. In PCDS, a transmission path selection algorithm is designed to establish multi-hop transmission paths for users, aiming at better utilization of D2D communications and concurrent transmissions. After transmission path selection, a concurrent transmission scheduling algorithm is designed to maximize the spatial reuse gain. Through extensive simulations under various traffic patterns, we demonstrate PCDS achieves near-optimal performance in terms of delay and throughput, and also superior performance compared with other existing protocols, especially under heavy load.Comment: 12 pages, to appear in IEEE Transactions on Vehicular Technolog

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

Understanding Mobile Data Demand regarding Mobility: The report for mid-term thesis evaluation

Smartphones are supposedly the fastest-spreading technology in human history. Global mobile data traffic has a growth of 74% in 2015, and is predicted to have an eightfold increase in 2020. Hence the understanding of subscriber’s mobile data demand is of great significance for solutions managing the increasing data traffic as well as improving quality of communication service. A core problem in understanding mobile data demand is to what degree is mobile data traffic predictable? We explore the predictability of data volume for individuals. Specifically, our goal is to determine the maximum probability of forecasting data volume for each subscriber. To this end, we mine a large-scale mobile dataset with both voice traffic and data traffic, construct a dataset of time series of data volume and explore the upper bound of predictability hidden in the time series. We find a overall > 90% of predictability hidden in individual’s time series of data volume

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