Spatio-Temporal Completion of Call Detail Records for Human Mobility Analysis

International audienceCall Detail Records (CDRs) have been widely used in the last decades for studying different aspects of human mobility. The accuracy of CDRs strongly depends on the user-network interaction frequency: hence, the temporal and spatial sparsity that typically characterize CDR can introduce a bias in the mobility analysis. In this paper, we evaluate the bias induced by the use of CDRs for inferring important locations of mobile subscribers, as well as their complete trajectories. Besides, we propose a novel technique for estimating real human trajectories from sparse CDRs. Compared to previous solutions in the literature, our proposed technique reduces the error between real and estimated human trajectories and at the same time shortens the temporal period where users’ locations remain undefined

Content consumption cartography of the Paris urban region using cellular probe data

A present issue in the evolution of mobile cellular networks is determining whether, how and where to deploy adaptive content and cloud distribution solutions at base station and back-hauling network level. In order to answer these questions, in this paper we document the content consumption in Orange cellular network for Paris metropolitan area. From spatial and application-level extensive analysis of real data, we numerically and statistically quantify the geographical distribution of content consumption with per-service classifications. We provide experimental statistical distributions usable for further research in the area

On the Quest for Representative Behavioral Datasets: Mobility and Content Demand

International audienceMobile datasets are widely used as firsthand sources for human mobility research. These datasets are often incomplete or have heterogeneous spatiotemporal resolutions, e.g. a dataset is often aggregated or in lack of fields. In many cases, a reliable dataset in human mobility research comes from sampling or merging original datasets, a challenging task. In this paper, we present our experience on creating a reliable dataset describing mobile data traffic in individual’s spatiotemporal view. We focus on individuals having enough geographical information and merge their call records from one dataset with the data traffic records extracted from another dataset. Based on this dataset, we perform an analysis of user demand on mobile data traffic in terms of spatial and temporal behaviors. For each subscriber, sessions are put into a 3-dimensional space in terms of space, time and volume and are clustered by applying DBScan. Characteristics of are revealed from the statistical analysis on clusters. Subscribers are also categorized according to their clusters

Filling the Gaps: On the Completion of Sparse Call Detail Records for Mobility Analysis

International audienceCall Detail Records (CDRs) have been widely used in the last decades for studying different aspects of human mobility. The accuracy of CDRs strongly depends on the user-network interaction frequency: hence, the temporal and spatial spar-sity that typically characterize CDR can introduce a bias in the mobility analysis. In this paper, we evaluate the bias induced by the use of CDRs for inferring important locations of mobile subscribers, as well as their complete trajectories. Besides, we propose a novel technique for estimating real human trajectories from sparse CDRs. Compared to previous solutions in the literature, our proposed technique reduces the error between real and estimated human trajectories and at the same time shortens the temporal period where users' locations remain undefined

Relevance of Context for the Temporal Completion of Call Detail Record

Call Detail Records (CDRs) are an important source of information in the study of different aspects of human mobility. However, their utility is often limited by spatio-temporal sparsity. In this paper, we first evaluate the effectiveness of CDRs in measuring relevant mobility features. We then investigate whether the information of user's instantaneous whereabouts provided by CDRs enables us to estimate positions over longer time spans. Our results confirm that CDRs ensure a good estimation of radii of gyration and important locations, yet they lose some location information. Most importantly, we show that temporal completion of CDRs is straightforward and efficient: thanks to the fact that they remain fairly static before and after mobile communication activities, the majority of users' locations over time can be accurately inferred from CDRs. Finally, we observe the importance of user's context, i.e., of the size of the current network cell, on the quality of the CDR temporal completion.Les statistiques d’appel (ou en anglais Call Detail Records - CDR) sont une importante source d’information dans l’étude des différents aspects de la mobilité humaine. Cependant,leur utilité est souvent limitée par son spartiété spatio-temporelle. Dans cet article, nous évaluons d’abord l’efficacité de l’utilisation des CDR pour la mesure des caractéristiques de mobilité pertinentes. Nous nous demandons ensuite si les informations de localisation instantanée de l’utilisateur fournies par les CDR nous permettent d’estimer leurs positions sur des périodes longues. Nos résultats confirment que les CDR assurent une bonne estimation des rayons de giration et des emplacements importants, mais ils perdent certaines informations de localisation.Plus important encore, nous montrons que l’achèvement temporel des CDR est simple et efficace:grâce au fait qu’ils restent relativement statiques avant et après les activités de communication mobile, la majorité des emplacements des utilisateurs dans le temps peut être correctement dé-duite des CDR. Enfin, on observe l’importance du contexte de l’utilisateur, c’est-à-dire de la taille de la cellule de réseau actuelle, sur la qualité de l’achèvement temporel des CDR

Spatio-Temporal Predictability of Cellular Data Traffic

The knowledge of the upper bounds of mobile data traffic predictors provides not only valuable insights on human behavior but also new opportunities to reshape mobile network management and services as well as provides researchers with insights into the design of effective prediction algorithms. In this paper, we leverage two large-scale real-world datasets collected by a major mobile carrier in a Latin American country to investigate the limits of predictability of cellular data traffic demands generated by individual users. Using information theory tools, we measure the maximum predictability that any algorithm has potential to achieve. We first focus on the predictability of mobile traffic consumption patterns in isolation. Our results show that it is theoretically possible to anticipate the individual demand with a typical accuracy of 85% and reveal that this percentage is consistent across all user types. Despite the heterogeneity of users, we also find no significant variability in predictability when considering demographic factors or different mobility or mobile service usage. Then, we analyze the joint predictability of the traffic demands and mobility patterns. We find that the two dimensions are correlated, which improves the predictability upper bound to 90% on average

Gestion de ressources et de congestion dans les réseaux mobiles

The Internet was initially conceived to serve fix and sedentary usages, while current socio-technological trends clearly show that future Internet users will be increasingly mobile and nomadic. At present, the speed at which this evolution takes place and the inadequate management of access networks represent a major obstacle in the development of advanced services. To solve these problems and to meet the needs of mobile Internet, service providers need to master the needed capacity expansion in their backhauling network, otherwise the data traffic will clog their networks in the future. Next-generation network deployments promise to deliver higher bandwidth and speed, but they often imply high capital and operational expenditures. An alternative economically and technically viable way is represented by mobile data offloading solutions. These solutions can reduce the load on radio spectrum, on base stations and on backhauling network. The most commonly used offloading solutions are over small-networks and over Wi-Fi networks. In the same context, and in order to solve the problem of congestion in the cellular network, a new solution has emerged recently : Information Centric Networking for in-network caching that permits to minimize content access latency. The objective of this thesis is to study these new traffic and content offloading solutions in cellular networks while taking into account the mobility patterns and human behavior.L’Internet a été initialement conçu pour servir des usages fixes et sédentaires, cependant les projections montrent que les futurs utilisateurs d'Internet seront de plus en plus mobiles. A l'heure actuelle, la rapidité avec laquelle cette évolution se déroule et la gestion souvent insuffisante des réseaux d'accès représentent un obstacle majeur au développement de services avancés. Afin de résoudre ces problèmes et répondre aux besoins de l'Internet mobile, les fournisseurs de services ont besoin de maîtriser l'expansion de la capacité nécessaire dans leurs réseaux de collecte, sinon le trafic de donnés va pouvoir boucher leurs réseaux dans le futur. Le déploiement des nouvelles générations de réseaux fournit des hautes bandes passantes et débits mais implique souvent des grandes dépenses en capital et en exploitation. Une alternative économiquement et techniquement viable est représentée par les solutions de déchargement du trafic mobile. Ces solutions peuvent réduire la surcharge sur le spectre radio et sur les stations de base et sur le réseau de collecte. Les solutions de déchargement les plus couramment utilisées sont le déchargement sur les réseaux de femtocellules et les réseaux Wi-Fi. Dans le même contexte, pour résoudre le problème de congestion dans le réseau cellulaire, une nouvelle solution est récemment apparue: Information Centric networking permettant la mise en cache des contenus dans le réseau ce qui minimise le temps d'accès aux contenus. L'objectif de cette thèse est donc d'étudier ces nouvelles solutions de déchargement de trafic et de contenu dans les réseaux cellulaires en prenant en considération les schémas de mobilité et les comportements humains

On Fair Network Cache Allocation to Content Providers

International audienceIn-network caching is an important solution for content offloading from content service providers. However despite a rather high maturation in the definition of caching techniques, minor attention has been given to the strategic interaction among the multiple content providers. Situations involving multiple Content Providers (CPs) and one Internet Service Provider (ISP) having to give them access to its caches are prone to high cache contention, in particular at the appealing topology cross-points. In this paper, we propose a resource allocation and pricing framework to support the network cache provider in the cache allocation to multiple CPs, for situations where CPs have heterogeneous sets of files and untruthful demands need to be avoided. As cache imputations to CPs need to be fair and robust against overclaiming, we evaluate common proportional and max-min fairness (PF, MMF) allocation rules, as well as two coalitional game rules, the Nucleolus and the Shapley value. We find that the naive least-recently-used-based cache allocation approach provides proportional fairness. Moreover, the game-theoretic rules outperform in terms of content access latency the naive cache allocation approach as well as PF and MMF approaches, while sitting in between PF and MMF in terms of fairness. Furthermore, we show that our pricing scheme encourages the CPs to declare their truthful demands by maximizing their utilities for real declarations

Reinforcement Learning based model for Maximizing Operator's Profit in Open-RAN

International audienceOpen Radio Access Network (O-RAN) is a novel architecture that enables the disaggregation and the virtualization of network components. This would provide new ways to mix and match network components by "opening up" the interfaces between them. O-RAN enables driving down the costs of network deployments and allows the entry of new players into the RAN market. It enables network operators to maximize resource utilization and deliver new network edge services at a lower cost, resulting in higher profits for operators. In this context, we consider a computing resource allocation problem for maximizing the operator's profit. Given that an operator receives subscribers' payments and pays the infrastructure provider's costs, we model the problem using Mixed Integer Linear Programming (MILP). Then, we propose to solve the problem using Reinforcement Learning (RL). Our simulation results demonstrate the ability of the RL agent to increase the operator's profit while reducing the algorithmic complexity of the MILP solver

A Recurrent Neural Network Based Approach for Coordinating Radio and Computing Resources Allocation in Cloud-RAN

International audienceCloud Radio Access Network (Cloud-RAN) is a novel architecture that aims at centralizing the baseband processing of base stations. This architecture opens paths for joint, flexible, and optimal management of radio and computing resources. To increase the benefit from this architecture, efficient resource management algorithms need to be devised. In this paper, we consider a coordinated allocation of radio and computing resources to mobile users. Optimal resource allocation that respects the Hybrid-Automatic-Repeat-Request deadline may require formulating high-complexity and resourceheavy algorithms. We consider two Integer Linear Programming problems (ILP) that implement a coordinated allocation of radio and computing resources with the objectives of maximizing throughput and maximizing users' satisfaction, respectively. Since solving these highly-complex problems requires a high execution time, we investigate low-complexity alternatives based on machine learning models; more precisely on Recurrent Neural Networks (RNN). These RNN models aim to depict the performance of the ILP problems with a much lower execution time. Our simulation results demonstrate the great ability of RNN models to perform very closely to the ILP problems while being able to reduce the execution time by up to 99.65%