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International audience; In the future, IoT devices will be part of the robotics ecosystem, and the border between IoT and robotics will blur. Already today, we observe converging trends between low-end IoT devices and minibots (i.e. tiny, cheap robots) concerning their hardware, and open source software. Micro-drones are an example of this trend. In this paper, we explore the potential of programming minibots with the open source robotics software framework ROS2, running on top of the IoT operating system RIOT; we call the fruitful association of both elements RIOT-ROS2. In this article, the emphasis is particularly on the networking layer: using an information-centric networking (ICN) paradigm, we design and implement the communication primitives for RIOT-ROS2. We further evaluate the performance of our design on prototype minibots based on cheap, off-the-shelf hardware elements. We show that RIOT-ROS2 fits on low-end robotics hardware such as a System-on-Chip costing under $2, based on an ARM Cortex-M0+ microcontroller. Our experiments also show that the latency incurred with our information-centric approach is acceptable for minibot control, even on a low-throughput IEEE 802.15.4 radio.

International audience; The Internet of Things (IoT) consists in a variety of smart connected objects, among which a category of low-end devices based on micro-controllers. The orchestration of low-end IoT devices is not straightforward because of the lack of generic and holistic solutions articulating cloud-based tools on one hand, and low-end IoT device software on the other hand. In this paper, we describe such a solution, combining a cloud-based IDE, graphical programming, and automatic JavaScript generation. Scripts are pushed over the Internet and over-the-air for the last hop, updating runtime containers hosted on heterogeneous low-end IoT devices running RIOT. We demonstrate a prototype working on common off-the-shelf low-end IoT hardware with as little as 32kB of memory.

International audience; The Internet of Things (IoT) connects a variety of small devices, via gateways, to the cloud. Use-cases often require IoT devices to run logic that is not predetermined before deployment, and that must be updated during the lifetime of the device. In this paper, we explore the potential of over-the-air scripting and updatable runtime containers hosting application logic on heterogeneous low-end IoT devices. Based on RIOT and Javascript, we provide a proof-of-concept implementation of this approach for a building automation IoT scenario. A preliminary evaluation shows our prototype runs on common off-the-shelf low-end IoT hardware with as little as 32kB of memory.

We consider community detection in Degree-Corrected Stochastic Block Models. We perform spectral clustering on $\widehat{H} = \left(\frac{1}{\widehat{D}_i \widehat{D}_j} A_{ij} \right)_{i,j=1}^n,$ where $A$ is the adjacency matrix of the network containing $n$ vertices and $\widehat{D}_i$ is the observed degree of node $i$. We show that this leads to consistent recovery of the block-membership of all but a vanishing fraction of nodes, even when the lowest degree is of order log$(n)$. There turns out to be a natural connection between $\widehat{H}$ and random walks on instances of the random graph. Moreover, $\widehat{H}$ appears to have a better behaved eigenspace than the ordinary adjacency matrix in case of a very heterogeneous degree-sequence.

Understanding human mobility patterns is crucial to fields such as urban mobility and mobile network planning. For this purpose, we make use of large-scale datasets recording individuals spatio-temporal locations, from eight major world cities: Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contributions are two-fold: first, we show significant similarities in people’s mobility habits regardless of the city and nature of the dataset. Second, we unveil three persistent traits present in an individual’s urban mobility: repetitiveness, preference for shortest-paths, and confinement. These characteristics uncover people’s tendency to revisit few favorite venues using the shortest-path available. Fil: Mucelli Rezende Oliveira, Eduardo. Ecole Polytechnique; Francia. Institut National de Recherche en Informatique et en Automatique; Francia Fil: Carneiro Viana, Aline. Institut National de Recherche en Informatique et en Automatique; Francia Fil: Sarraute, Carlos. Grandata Labs; Argentina Fil: Brea, Jorge Nicolás. Grandata Labs; Argentina Fil: Alvarez Hamelin, Jose Ignacio. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long". Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Tecnologías y Ciencias de la Ingeniería "Hilario Fernández Long"; Argentina

International audience; This paper sheds light on the trade-os between privacy and utility in mobility-based geographic datasets. We aim at nd-ing out whether it is possible to protect the privacy of the users in a dataset while, at the same time, maintaining intact the utility of the information that it contains. In particular, we focus on geo-indistinguishability as a privacy-preserving sanitization methodology, and we evaluate its eects on the utility of the Geolife dataset. We test the sanitized dataset in two real world scenarios: 1. Deploying an infrastructure of WiFi hotspots to ooad the mobile trac of users living, working, or commuting in a wide geographic area; 2. Simulating the spreading of a gossip-based epidemic as the outcome of a device-to-device communication protocol. We show the extent to which the current geo-indistinguishability techniques trade privacy for utility in real world applications and we focus on their eects at the levels of the population as a whole and of single individuals.

International audience; Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

This paper shows that human personality can be accurately predicted by looking at the data generated by our smartphones. GPS location, calls, battery usage and charging, networking context like bluetooth devices and WiFi access points in proximity, and more give enough information about individual habits, reactions, and idiosyncrasies to make it possible to infer the psychological traits of the user. We demonstrate this by using machine learning techniques on a dataset of 55 volunteers who took a psychological test and allowed continuous collection of data from their smartphones for a time span of up to three years. Openness, Conscientiousness, Extroversion, Agreeableness, and Neuroticism (the so called Big5 personality traits) can be predicted with good accuracy even by using just a handful of features. The possible applications of our findings go from network optimization, to personal advertising, and to the detection of mental instability and social hardship in cities and neighborhoods. We also discuss the ethical concerns of our work, its privacy implications, and ways to tradeoff privacy and benefits.

This paper considers a data collection network consisting of uncoordinated, heterogeneous, and possibly mobile devices. These devices use medium and short-range radio technologies, which require multi-hop communication to deliver data to some sink. While numerous techniques from managed networks can be adapted, one of the most efficient (from the energy and spectrum use perspective) is \emph{network coding} (NC). NC is well suited to networks with mobility and unreliability, however, practical NC requires a precise identification of individual packets that have been mixed together. In a purely decentralized system, this requires either conveying identifiers in headers along with coded information as in COPE, or integrating a more complex protocol in order to efficiently identify the sources (participants) and their payloads. This paper presents a novel solution, Network Coding with Random Packet Index Assignment (NeCoRPIA), where packet indices in NC headers are selected in a decentralized way, by simply choosing them randomly. Traditional network decoding techniques apply directly when all original packets have different indices. When this is not the case, i.e., in case of collisions of indices, a specific decoding algorithm is proposed. A theoretical analysis of its performance in terms of complexity and decoding error probability is described. Simulation results match well the theoretical results. Comparisons of NeCoRPIA header lengths with those of a COPE-based NC protocol are also provided.

Swarm intelligence-based approach designed to intermittently connected networks.Greedy Ant Colony Optimization and Cultural Algorithms to address the routing problem.The utility of nodes as message forwarders considers a set of socialaware metrics.Operational metrics characterize the opportunistic social connectivity between nodes.CGrAnt outperforms Epidemic, PROPHET, and dLife in message delivery and redundancy. Understanding nodes mobility is of fundamental importance for data delivery in opportunistic and intermittently connected networks referred to as Delay Tolerant Networks (DTNs). The analysis of such mobility patterns and the understanding of how mobile nodes interact play a critical role when designing new routing protocols for DTNs. The Cultural Greedy Ant (CGrAnt) protocol is a hybrid Swarm Intelligence-based approach designed to address the routing problem in such dynamic and complex environment. CGrAnt is based on: (1) Cultural Algorithms (CA) and Ant Colony Optimization (ACO) and (2) operational metrics that characterize the opportunistic social connectivity between wireless users. The most promising message forwarders are selected via a greedy transition rule based mainly on local information captured from the DTN environment. Whenever global information is available, it can also be used to support decisions. We compare the performance of CGrAnt with Epidemic, PROPHET, and dLife protocols in two different mobility scenarios under varying networking parameters. Results obtained by the ONE simulator show that CGrAnt achieves a higher message delivery and lower message redundancy than the three protocols in both scenarios. The only exception is in one of the scenarios, when messages have a time to live lower than 900 min, where CGrAnt delivers a bit less messages than dLife, although with a lower message redundancy.

International audience; Due to the limited coverage of WiFi APs, users' mobility has a severe impact on the performance of mobile offloading systems. The present study is a contribution in this context as offloading zones are identified and characterized from individual GPS trajectories when small offloading time windows are considered. The results show that (i) attending to users mobility, ten seconds is the minimum offloading time window that can be considered; (ii) offloading predictive methods can have variable performance according to the period of the day; and (iii) per-user opportunistic decision models can determine offloading system design and performance.

International audience; In this paper, we design and experiment ODYSSE (Opportunistic Duty cYcle based routing protocol for wirelesS Sensor nEtworks) protocol. It combines three main mechanisms: (i) duty cycle, where nodes alternate between active and sleep states, (ii) opportunistic routing where routing tables do not exist and the next hop is elected once the packet arrives, and (iii) source coding with LDPC (Low-Density Parity-Check) codes in order to face packet losses while minimizing energy consumption. We focus on two heterogeneous scenarios: bulk image transmission and infrequent events reporting. Modeling the average waiting delay of forwarders, we show that simple relay selection strategies are effective. We used 45 Arduino nodes communicating with IEEE 802.15.4 (XBee) within the large platform FIT IoT-LAB (IoT-LAB is part of the large platform FIT: Future Internet of Things). We implement and extensively study the behavior and performance of our proposal ODYSSE. We show that the three techniques fit perfectly, yielding a robust low complexity protocol for highly constrained nodes in typical IoT applications.

International audience; Employing mobile devices to perform data analytics is a typical fog computing application that utilizes the intelligence at the edge of networks. Such an application relies on the knowledge of the mobility of mobile devices and their users, e.g., to deploy computation tasks efficiently at the edge. This paper surveys the literature on the mobility-related utilization of operator-collected CDR (charging data records) – the most significant proxy of large-scale human mobility studies. We provide an innovative introductory guide to the CDR data preliminary. It reveals original issues regarding CDR-based mobility feature computation and applications at the edge. Our survey plays an important role in utilizing mobile devices in terms of both human mobility investigation and fog computing.

International audience; For human mobility studies across many disciplines, mobile network data serves as a primary source of human footprints with geo-referenced and time-stamped records of human communication activities. Nevertheless, the quality of mobility information provided by mobile network data is usually not satisfactory on many users. Due to the nature of human communications, individual trajectories inferred from mobile network data are often substantially incomplete , and the pattern of missing locations is not uniform over time but is highly related to communication activities. In this paper, we propose a novel hierarchical approach based on tensor factorization to reconstruct such incomplete individual trajectories. The data-driven simulation shows that, with ground-truth locations in precision of 200 meters, our approach can recover a trajectory from 10% of its known locations with a distance error below 750 meters, which outperforms the existing proposals in the literature.

International audience; We leverage two large-scale real-world datasets to provide the pioneer results on the limits of predictability of per-user mobile data traffic demands over time and space. Using information theory tools, we measure the maximum predictability that any algorithm has potential to achieve. We first focus on the predictability of mobile data traffic consumption patterns in isolation. Our results show that it is theoretically possible to anticipate individual demands with a typical accuracy of 85%. Then, we analyze the joint predictability of mobile data traffic demands and mobility patterns. Their correlation that we find leads to a higher theoretical potential performance in joint prediction. Besides, we propose a novel practice to evaluate the spatiotemporal correlation of per-user mobile data traffic.

International audience; Des études récentes ont profité des techniques de suivi basées sur les technologies de positionnement d' étudier afin la mobilité humaine. Ces recherches ont révélé, entre autres, une grande régularité spatio-temporelle des mouvements individuels. Sur la base de ces résultats, nous visons à répondrè a la question "a quelle fréquence doit-on échantillonner les mouvements humains individuels afin qu'ils puissent être reconstruits à partir des échantillons recueillis avec un minimum de perte d'information?". Notre quête d'une réponse a conduit à la découverte de (i) propriétés spectrales apparemment universelles de la mobilité humaine, et (ii) une loi de misè a l' échelle linéaire de l'erreur de localisation par rapport à l'intervalle d' échantillonnage. Nos résultats sont basés sur l'analyse des trajectoires GPS de 119 utilisateurs dans le monde entier. Les applications de nos résultats sont liées un certain nombre de domaines pertinents pour l'informatique omniprésente, tels que l'informatique mobilé econome en énergie, les opérations de service basées sur l'emplacement, le sondage actif des positions des abonnés dans les réseaux mobiles et la compression des données de trajectoire.

The understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers.; La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnés.

The understanding of human behaviors is a central question in multi-disciplinary research and has contributed to a wide range of applications. The ability to foresee human activities has essential implications in many aspects of cellular networks. In particular, the high availability of mobility prediction can enable various application scenarios such as location-based recommendation, home automation, and location-related data dissemination; the better understanding of mobile data traffic demand can help to improve the design of solutions for network load balancing, aiming at improving the quality of Internet-based mobile services. Although a large and growing body of literature has investigated the topic of predicting human mobility, there has been little discussion in anticipating mobile data traffic in cellular networks, especially in spatiotemporal view of individuals.For understanding human mobility, mobile phone datasets, consisting of Charging Data Records (CDRs), are a practical choice of human footprints because of the large-scale user populations and the vast diversity of individual movement patterns. The accuracy of mobility information granted by CDR depends on the network infrastructure and the frequency of user communication events. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR data is often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data and cause the loss of whereabouts in individual trajectories.In this thesis, we present novel solutions of the reconstruction of individual trajectories and the prediction of individual mobile data traffic. Our contributions address the problems of (1) overcoming the incompleteness of mobility information for the use of mobile phone datasets and (2) predicting future mobile data traffic demand for the support of network management applications.First, we focus on the flaw of mobility information in mobile phone datasets. We report on an in-depth analysis of its effect on the measurement of individual mobility features and the completeness of individual trajectories. In particular, (1) we provide a confirmation of previous findings regarding the biases in mobility measurements caused by the temporal sparsity of CDR; (2) we evaluate the geographical shift caused by the mapping of user locations to cell towers and reveal the bias caused by the spatial sparsity of CDR; (3) we provide an empirical estimation of the data completeness of individual CDR-based trajectories. (4) we propose novel solutions of CDR completion to reconstruct incomplete. Our solutions leverage the nature of repetitive human movement patterns and the state-of-the-art data inference techniques and outperform previous approaches shown by data-driven simulations.Second, we address the prediction of mobile data traffic demands generated by individual mobile network subscribers. Building on trajectories completed by our developed solutions and data consumption histories extracted from a large-scale mobile phone dataset, (1) we investigate the limits of predictability by measuring the maximum predictability that any algorithm has potential to achieve and (2) we propose practical mobile data traffic prediction approaches that utilize the findings of the theoretical predictability analysis. Our theoretical analysis shows that it is theoretically possible to anticipate the individual demand with a typical accuracy of 75% despite the heterogeneity of users and with an improved accuracy of 80% using joint prediction with mobility information. Our practical based on machine learning techniques can achieve a typical accuracy of 65% and have a 1%~5% degree of improvement by considering individual whereabouts.In summary, the contributions mentioned above provide a step further towards supporting the use of mobile phone datasets and the management of network operators and their subscribers.; La capacité à prévoir les activités humaines a des implications essentielles dans de nombreux aspects des réseaux cellulaires. En particulier, la haute disponibilité de la prédiction de la mobilité peut permettre différents scénarios d'application; une meilleure compréhension de la demande de trafic de données mobiles peut aider à améliorer la conception de solutions pour l'équilibrage de la charge du réseau. Bien que de nombreux chercheurs aient étudié le sujet de la prédiction de la mobilité humaine, il y a eu peu de discussions sur l'anticipation du trafic de données mobiles dans les réseaux cellulaires.Pour comprendre la mobilité humaine, les ensembles de données de téléphones mobiles, consistant en des enregistrements de données de taxation (CDR), constituent un choix pratique d'empreintes humaines. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d'interaction sont généralement faibles, les données CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur de telles données et provoquer la perte de trajectoires individuelles.Dans cette thèse, nous présentons de nouvelles solutions de reconstruction de trajectoires individuelles et de prédiction de trafic de données mobiles individuelles. Nos contributions abordent les problèmes de (1) surmonter l'incomplétude des informations de mobilité pour l'utilisation des ensembles de données de téléphonie mobile et (2) prédire la future demande de trafic de données mobiles pour le support des applications de gestion de réseau.Premièrement, nous nous concentrons sur la faille de l'information sur la mobilité dans les ensembles de données de téléphones mobiles. Nous rapportons une analyse en profondeur de son effet sur la mesure des caractéristiques de mobilité individuelles et l'exhaustivité des trajectoires individuelles. En particulier, (1) nous fournissons une confirmation des résultats antérieurs concernant les biais dans les mesures de mobilité causées par la rareté temporelle de la CDR; (2) nous évaluons le décalage géographique provoqué par la cartographie des emplacements des utilisateurs vers les tours cellulaires et révélons le biais causé par la rareté spatiale de la CDR; (3) nous fournissons une estimation empirique de l'exhaustivité des données des trajectoires CDR individuelles. (4) nous proposons de nouvelles solutions de complétion CDR pour reconstruire incomplète. Nos solutions tirent parti de la nature des modèles de mouvements humains répétitifs et des techniques d'inférence de données de pointe et surpassent les approches précédentes illustrées par des simulations axées sur les données.Deuxièmement, nous abordons la prédiction des demandes de trafic de données mobiles générées par les abonnés individuels du réseau mobile. Sur la base de trajectoires complétées par nos solutions développées et nos historiques de consommation de données extraites d'un ensemble de données de téléphonie mobile à grande échelle, (1) nous étudions les limites de prévisibilité en mesurant la prévisibilité maximale que tout algorithme peut atteindre. les approches de prédiction du trafic de données mobiles qui utilisent les résultats de l'analyse théorique de la prévisibilité. Notre analyse théorique montre qu'il est théoriquement possible d'anticiper la demande individuelle avec une précision typique de 75% malgré l'hétérogénéité des utilisateurs et avec une précision améliorée de 80% en utilisant la prédiction conjointe avec des informations de mobilité. Notre pratique basée sur des techniques d'apprentissage automatique peut atteindre une précision typique de 65% et avoir un degré d'amélioration de 1% à 5% en considérant les déplacements individuels.En résumé, les contributions mentionnées ci-dessus vont dans le sens de l'utilisation des ensembles de données de téléphonie mobile et de la gestion des opérateurs de réseau et de leurs abonnés.

Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long time periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique to complete individual CDR-based trajectories that hinges on tensor factorization as a core method. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on a hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.; Les données de la téléphonie mobile sont une source populaire d’informations de positionnement dans de nombreuses études récentes qui ont grandement amélioré notre compréhension de la mobilité humaine. Ces données consistent en événements de communication horodatés et géoréférencés enregistrés par les opérateurs de réseau, abonné par abonné. Ils permettent un suivi sans précédent des populations de millions d’individus sur de longues périodes couvrant plusieurs mois. Néanmoins, en raison des processus inégaux qui régissent les communications mobiles, l’échantillonnage des emplacements d’utilisateurs fourni par les données de la téléphonie mobile a tendance à être rare et irrégulier dans le temps, ce qui entraîne des lacunes substantielles dans les informations de trajectoire résultantes. Dans cet article, nous illustrons la gravité du problème par le biais d’une étude empirique d’un ensemble de données CDR (Call Detail Record) à grande échelle. Nous proposons ensuite Context-enhanced Trajectory Reconstruction, une nouvelle technique permettant de compléter des trajectoires individuelles basées sur la CDR et reposant sur la factorisation du tenseur en tant que méthode de base. La solution proposée déduit les emplacements manquants avec un déplacement médian dans deux cellules du réseau à partir de la position réelle de l’utilisateur, sur une base horaire et même lorsque le montant de sa mobilité initiale est connu. Notre approche nous permet de revisiter les travaux fondamentaux à la lumière de données complètes sur la mobilité, en révélant les biais potentiels que les trajectoires incomplètes obtenues à partir de la CDR héritée induisent sur des résultats clés concernant les lois de mobilité humaine, l’unicité des trajectoires et la prévisibilité des mouvements.

Call Detail Records (CDR) are an important source of information in the study of diverse aspects of human mobility. The accuracy of mobility information granted by CDR strongly depends on the radio access infrastructure deployment and the frequency of interactions between mobile users and the network. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR are often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data. In this paper, we precisely address this subject. First, we evaluate the spatial error in CDR, caused by approximating user positions with cell tower locations. Second, we assess the impact of the limited spatial and temporal granularity of CDR on the estimation of standard mobility metrics. Third, we propose novel and effective techniques to reduce temporal sparsity in CDR by leveraging regularity in human movement patterns. Tests with real-world datasets show that our solutions can reduce temporal sparsity in CDR by recovering 75% of daytime hours, while retaining a spatial accuracy within 1 km for 95% of the completed data.

Call Detail Records (CDR) are an important source of information in the study of diverse aspects of human mobility. The accuracy of mobility information granted by CDR strongly depends on the radio access infrastructure deployment and the frequency of interactions between mobile users and the network. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR are often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data. In this paper, we precisely address this subject. First, we evaluate the spatial error in CDR, caused by approximating user positions with cell tower locations. Second, we assess the impact of the limited spatial and temporal granularity of CDR on the estimation of standard mobility metrics. Third, we propose novel and effective techniques to reduce temporal sparsity in CDR, by leveraging regularity in human movement patterns.; Call Detail Records (CDR) sont une source importante d’informations dans l’étude des divers aspects de la mobilité humaine. L’exactitude des informations de mobilité accordées par CDR dépend fortement du déploiement de l’infrastructure d’accès radio et de la fréquence des interactions entre les utilisateurs mobiles et le réseau. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d’interaction sont généralement faibles, les CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur ces données. Dans cet article, nous abordons précisément ce sujet. Tout d’abord, nous évaluons l’erreur spatiale dans CDR, causée par l’approximation des positions de l’utilisateur avec les emplacements de la tour de téléphonie cellulaire. Deuxièmement, nous évaluons l’impact de la granularité spatiale et temporelle limitée du CDR sur l’estimation des métriques de mobilité standard. Troisièmement, nous proposons des techniques nouvelles et efficaces pour réduire la rareté temporelle dans la CDR, en tirant parti de la régularité des mouvements humains.

Call Detail Records (CDR) are an important source of information in the study of diverse aspects of human mobility. The accuracy of mobility information granted by CDR strongly depends on the radio access infrastructure deployment and the frequency of interactions between mobile users and the network. As cellular network deployment is highly irregular and interaction frequencies are typically low, CDR are often characterized by spatial and temporal sparsity, which, in turn, can bias mobility analyses based on such data. In this paper, we precisely address this subject. First, we evaluate the spatial error in CDR, caused by approximating user positions with cell tower locations. Second, we assess the impact of the limited spatial and temporal granularity of CDR on the estimation of standard mobility metrics. Third, we propose novel and effective techniques to reduce temporal sparsity in CDR, by leveraging regularity in human movement patterns.; Call Detail Records (CDR) sont une source importante d’informations dans l’étude des divers aspects de la mobilité humaine. L’exactitude des informations de mobilité accordées par CDR dépend fortement du déploiement de l’infrastructure d’accès radio et de la fréquence des interactions entre les utilisateurs mobiles et le réseau. Comme le déploiement du réseau cellulaire est très irrégulier et que les fréquences d’interaction sont généralement faibles, les CDR sont souvent caractérisées par une parcimonie spatio-temporelle qui, à son tour, peut biaiser les analyses de mobilité basées sur ces données. Dans cet article, nous abordons précisément ce sujet. Tout d’abord, nous évaluons l’erreur spatiale dans CDR, causée par l’approximation des positions de l’utilisateur avec les emplacements de la tour de téléphonie cellulaire. Deuxièmement, nous évaluons l’impact de la granularité spatiale et temporelle limitée du CDR sur l’estimation des métriques de mobilité standard. Troisièmement, nous proposons des techniques nouvelles et efficaces pour réduire la rareté temporelle dans la CDR, en tirant parti de la régularité des mouvements humains.

International audience; Motivated by scenario requirements for 5G cellular networks, we study one of the candidate protocols for massive random access: the family of random access methods known as Coded Slotted ALOHA (CSA). A recent trend in research has explored aspects of such methods in various contexts, but one aspect has not been fully taken into account: the impact of path loss, which is a major design constraint in long-range wireless networks. In this article, we explore the behavior of CSA, by focusing on the path loss component correlated to the distance to the base station. Path loss provides opportunities for capture, improving the performance of CSA. We revise methods for estimating CSA behavior, provide bounds of performance, and then, focusing on the achievable throughput, we extensively explore the key parameters, and their associated gain (experimentally). Our results shed light on the behavior of the optimal distribution of repetitions in actual wireless networks.

A matching in a two-sided market often incurs an externality: a matched resource may become unavailable to the other side of the market, at least for a while. This is especially an issue in online platforms involving human experts as the expert resources are often scarce. The efficient utilization of experts in these platforms is made challenging by the fact that the information available about the parties involved is usually limited. To address this challenge, we develop a model of a task-expert matching system where a task is matched to an expert using not only the prior information about the task but also the feedback obtained from the past matches. In our model the tasks arrive online while the experts are fixed and constrained by a finite service capacity. For this model, we characterize the maximum task resolution throughput a platform can achieve. We show that the natural greedy approaches where each expert is assigned a task most suitable to her skill is suboptimal, as it does not internalize the above externality. We develop a throughput optimal backpressure algorithm which does so by accounting for the `congestion' among different task types. Finally, we validate our model and confirm our theoretical findings with data-driven simulations via logs of Math.StackExchange, a StackOverflow forum dedicated to mathematics., A part of it presented at Allerton Conference 2017, 18 pages

(6-page paper); International audience; In this paper, we leverage two large-scale real-worlddatasets to provide the first results on the limits of predictabilityof cellular data traffic demands generated by individual usersover time and space. Using information theory tools, we measurethe maximum predictability that any algorithm has potential toachieve. We first focus on the predictability of mobile trafficconsumption patterns in isolation. Our results show that it istheoretically possible to anticipate the individual demand witha typical accuracy of 85% and reveal that this percentage isconsistent across all user types. Then, we analyze the jointpredictability of the traffic demands and mobility patterns. Wefind that the two dimensions are correlated, which improves thepredictability upper bound to 90% on average.

International audience; Energy efficiency is a major driving factor in the Internet of Things (IoT). In this context, an IoT approach based on Information-Centric Networking (ICN) offers prospects for low energy consumption.Indeed, ICN can provide local in-network content caching so that relevant IoT content remains available at any time while devices are in deep-sleep mode most of the time.In this paper, we evaluate NDN enhanced with CoCa, a simple side protocol we designed to exploit content names together with smart interplay between cooperative caching and power-save sleep capabilities on IoT devices.We perform extensive, large scale experiments on real hardware with IoT networks comprising of up to 240 nodes, and on an emulator with up to 1000 nodes.We show in practice that, with NDN+CoCa, devices can reduce energy consumption by an order of magnitude while maintaining recent IoT content availability above 90%.We furthermore provide auto-configuration mechanisms enabling practical ICN deployments on IoT networks of arbitrary size with NDN+CoCa. With such mechanisms, each device can autonomously configure names and auto-tune parameters to reduce energy consumption as demonstrated in this paper.

International audience; In this paper, we start from two observations. First, many application scenarios that benefit from ICN involve battery-driven nodes connected via shared media. Second, current link layer technologies are completely ICN-agnostic, which prevents filtering of ICN packets at the device driver level. Consequently, any ICN packet, Interest as well as Data, is processed by the CPU. This sacrifices local system resources and disregards link layer support functions such as wireless retransmission. We argue for a mapping of names to MAC addresses to efficiently handle ICN packets, and explore dynamic face-based mapping schemes. We analyze the impact of this link-layer adaptation in real-world experiments and quantitatively compare different configurations. Our findings on resource consumption, and reliability on constrained devices indicate significant gains in larger networks.

International audience; In this paper, we demonstrate how NDN can be used as network primitive on low-cost robots with the Robot Operating System (ROS).

The use of Named Data Networking (NDN) for distributed multi-user applications, e.g. group messaging and file sharing, requires NDN synchronization protocols to maintain the same shared dataset (and its updates) among all nodes. ChronoSync [1], RoundSync [2], and PartialSync [3] are some proposals to address this issue, see [4]. Here we focus on the state-of-the-art protocol RoundSync [2]: we study its core features, that permit participating nodes to detect, propagate, and reconcile all changes. Particular attention is given to the case of multiple changes per round. We then propose an improved variant, iRoundSync, that exchanges fewer messages in the multiple-change case and is more resilient to packet losses. We quantify the performance gain of iRoundSync on a simple topology.

International audience; This paper presents a mobility-aware channel allocation strategy for clustered ad hoc network. Our main novelty is to consider the user mobility associated with traffic history and node popularity to guide the channel allocation process, while quickly responding to changes in the network topology. In our performance evaluation and contrarily to related works, we use a realistic mobility model based on user behavior and consider channels with largest spectral distance as well as the smallest number of occurrences. Obtained results show that our strategy presents throughput 15,43 % and 17,74% higher when compared with RANDOM and LD algorithms respectively, and lower overhead when compared with TABU algorithm.

International audience; Call Detail Records (CDRs) are a primary source of whereabouts in the study of multiple mobility-related aspects. However, the spatiotemporal sparsity of CDRs often limits their utility in terms of the dependability of results. In this paper, driven by real-world data across a large population, we propose two approaches for completing CDRs adaptively, to reduce the sparsity and mitigate the problems the latter raises. Owing to high-precision sampling, the comparative evaluation shows that our approaches outperform the legacy solution in the literature in terms of the combination of accuracy and temporal coverage. Also, we reveal those important factors for completing sparse CDR data, which sheds lights on the design of similar approaches.

The workshop is hosted by the International Conference on Embedded Wireless Systems and Networks (EWSN); International audience; In the future, the Internet will not only connect computers and smart objects, but also a wide variety of semi-and fully-autonomous robots. This paper focuses on low-cost robots, and studies the recent convergence between low-cost robot hardware and IoT hardware. The potential for further convergence is then explored in terms of common embedded software platforms and common communication protocols, which could be used on both low-end IoT devices and low-cost robots. Finally, a proof-of-concept is provided based on RIOT and Aversive++ as software platform, show-cased running on a low-cost four-legged IoT robot.

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.

International audience; This paper presents a detailed measurement-driven model of mobile data traffic usage of smartphone subscribers, using a large-scale dataset collected from a major 3G network in a dense metropolitan area. Our main contribution is a synthetic, measurement-based, mobile data traffic generator capable of simulating traffic-related activity patterns over time for different categories of subscribers and time periods for a typical day in their lives. We first characterize individual subscribers' routinary behaviour, followed by a detailed investigation of subscribers' temporal usage patterns (i.e., " when " and " how much " traffic is generated). We then classify the subscribers into six distinct profiles according to their usage patterns and model these profiles according to two daily time periods: peak and non-peak hours. We show that the synthetic trace generated by our data traffic model consistently replicates a subscriber's profiles for these two time periods when compared to the original dataset. Broadly, our observations bring important insights into temporal network resource usage. We also discuss relevant issues in traffic demands and describe implications in network solution evaluation and privacy.

International audience; Motivated by community detection, we characterise the spectrum of the non-backtracking matrix $B$ in the Degree-Corrected Stochastic Block Model. Specifically, we consider a random graph on $n$ vertices partitioned into two equal-sized clusters. The vertices have i.i.d. weights $\{ \phi_u \}_{u=1}^n$ with second moment $\Phi^{(2)}$. The intra-cluster connection probability for vertices $u$ and $v$ is $\frac{\phi_u \phi_v}{n}a$ and the inter-cluster connection probability is $\frac{\phi_u \phi_v}{n}b$. We show that with high probability, the following holds: The leading eigenvalue of the non-backtracking matrix $B$ is asymptotic to $\rho = \frac{a+b}{2} \Phi^{(2)}$. The second eigenvalue is asymptotic to $\mu_2 = \frac{a-b}{2} \Phi^{(2)}$ when $\mu_2^2 > \rho$, but asymptotically bounded by $\sqrt{\rho}$ when $\mu_2^2 \leq \rho$. All the remaining eigenvalues are asymptotically bounded by $\sqrt{\rho}$. As a result, a clustering positively-correlated with the true communities can be obtained based on the second eigenvector of $B$ in the regime where $\mu_2^2 > \rho.$ In a previous work we obtained that detection is impossible when $\mu_2^2 < \rho,$ meaning that there occurs a phase-transition in the sparse regime of the Degree-Corrected Stochastic Block Model. As a corollary, we obtain that Degree-Corrected Erd\H{o}s-R\'enyi graphs asymptotically satisfy the graph Riemann hypothesis, a quasi-Ramanujan property. A by-product of our proof is a weak law of large numbers for local-functionals on Degree-Corrected Stochastic Block Models, which could be of independent interest.

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.

The recent boost up of mobile data consumption is straining cellular networks in metropolitan areas and is the main reason for the ending of unlimited data plans by many providers. To address this problem, we propose the use of series opportunistic delegation as a data traffic offload solution by investigating two main questions: (i) “Can we characterize a given social mobile scenario by observing only a small portion of it?”. (ii) “How to exploit this characterization so to design solutions that alleviate overloaded cellular networks?”. In our solution we build a social-graph of the given scenario by observing it for a period as short as 1-week, and then leverage a few, socially important users in the social-graph—the VIPs—to offload the network. The proposed VIP selection strategies are based on social network properties and are compared to the optimal (offline) solution. Through extensive evaluations with real and synthetic traces we show the effectiveness of VIP delegation both in terms of coverage and required number of VIPs – down to 7% in average of VIPs are needed in campus-like scenarios to offload about 90% of the traffic.

International audience; Opportunistic communication, off-loading and decentrlaized distribution have been proposed as a means of cost efficient disseminating content when users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery latency, security and privacy concerns. This paper, presents a novel hybrid content storage and distribution system addressing the trust and privacy concerns of users, lowering the cost of content distribution and storage, and shows how they can be combined uniquely to develop mobile social networking services. The system exploit the fact that users will trust their friends, and by replicating content on friends’ devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, and show that it is NP hard. Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends’ devices, to maximize availability. Then using both real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated. The practicality of the proposed system, is demonstrated through an implementation on Android smartphones.

International audience; VANETs have emerged as an exciting research and application area. Increasingly vehicles are being equipped with embedded sensors, processing and wireless communication capabilities. This has opened a myriad of possibilities for powerful and potential life-changing applications on safety, efficiency, comfort, public collaboration and participation, while they are on the road. Although, considered as a special case of a Mobile Ad Hoc Network, the high but constrained mobility of vehicles bring new challenges to data communication and application design in VANETs. This is due to their highly dynamic and intermittent connected topology and different application's QoS requirements. In this work, we survey VANETs focusing on their communicationand application challenges. In particular, we discuss the protocol stack of this type of network, and provide a qualitative comparison between most common protocols in the literature. We then present a detailed discussion of dfferent categories of VANET applications. Finally, we discuss open research problems to encourage the design of new VANET solutions.

In the IoT, the trade-off between content availability and energy efficiency plays a crucial role. The standard approach to energy efficiency in the IoT combines low-power radios, network protocols letting devices sleep most of the time, and centralized caching of content, either at a designated proxy or in the cloud via an uplink, which maintains IoT content availability while devices sleep. However, this approach is not possible in a number of IoT scenarios, where a designated gateway/proxy is unavailable most of the time. In this paper, we propose an approach leveraging distributed caching for IoT content, based on an information-centric networking paradigm. We extend the NDN protocol with a variety of caching and replacement strategies, and we discuss alternative approaches for extending NDN to accommodate such IoT use cases. Based on extensive experiments on real IoT hardware in a network gathering hundreds of nodes, we demonstrate these caching strategies can bring 90% reduction in energy consumption while maintaining IoT content availability above 90%.

International audience; This article presents a user behavior aware channel allocation scheme for mobile ad hoc networks. The proposed scheme uses a distributed approach to dynamically allocate the IEEE 802.11b/g spectrum. Our main contribution is to consider the user behavior, represented by the mobility, traffic, and node degree to guide the channel allocation process. In addition, we use a mobility model based on the social context found in people sharing common interests. The results show that our strategy presents throughput 15,43% and 17,74% higher when compared with RANDOM and LD algorithms, respectively.

International audience; We consider the benefits of coding in wireless networks, specifically its role in exploiting the local broadcast property of the wireless medium. We first argue that for unicast, the throughput achieved with network coding is the same as that achieved without any coding. This argument highlights the role of a general max-flow min-cut duality and is more explicit than previous proofs. The maximum throughput can be achieved in multiple ways without any coding, for example, using backpressure routing, or using some centralized flow scheduler that is aware of the network topology. However, all such schemes, in order to take advantage of the local broadcast property, require dynamic routing decisions for choosing the next hop for each packet from among the nodes where it is successfully received. This choice seems to depend critically on feedback signaling information like queue lengths, or ARQ. In contrast, note that the use of network coding can achieve the same without such feedback, in exchange for decoding overhead. A key issue to be resolved in making a comparison between routing and coding would be how critical feedback signaling is, for the throughput of routing policies. With this motivation, we first explore how feedback at a given node affects its throughput, with arbitrary rates of its one-hop neighbors to the destination. Static routing policies which are essentially feedback independent , are considered. An explicit characterization of the optimal policies under such a feedback constraint is obtained, which turns out to be a natural generalization of both flooding and traditional routing (which does not exploit local broadcast, because the next hop is fixed prior to the transmission). When losses at the receivers are independent (still allowing for dependencies on transmissions by two different nodes, to model interference), the reduction in capacity due to constraining the feedback is limited to a constant fraction (e−1=37%) of the coding capacity, and gets arbitrarily close to optimal as the unconstrained capacity goes to zero. We also extend this analysis to a layered multihop network and also compare the throughput of flooding to backpressure via simulations for a layered network assuming independent losses. Finally, if there are dependencies in the losses seen by receivers from a single broadcast, the reduction could be arbitrarily bad, even with just two hops.

International audience; Recent proposals to simplify the operation of the IoT include the use of Information Centric Networking (ICN) paradigms. While this is promising, several challenges remain. In this paper, our core contributions (a) leverage ICN communication patterns to dynamically optimize the use of TSCH (Time Slotted Channel Hopping), a wireless link layer technology increasingly popular in the IoT, and (b) make IoT-style routing adaptive to names, resources, and traffic patterns throughout the network -- both without cross-layering.

International audience; Vehicular networks are networks comprised by vehicles trav-eling cities and highways. During their trajectories, these vehicles interact with other vehicles and road side units in order to make safer and enjoyable trac. These interactions may be influenced by several factors. To mention a few: vehicle speed, roads condition, time of day and weather. Moreover, driver behavior and its interests can influence in vehicle features. In this context, the Vehicular Social Networks arise as a new perspective to vehicular networks, where the vehicles " socialize " and share common interests. In this work, we evaluate the behavior of vehicles using two mobility scenarios, in order to classify them according to the interactions performed, identifying common interests and similar routines. Thus, we use metrics of complex networks and statistical techniques. Results prove the existence of routines and human features in Vehicular Networks.

International audience; In this paper, we propose, design and experiment an energy efficient protocol for Wireless Sensor Networks (WSNs) named Opportunistic Duty cYcle based routing protocol for wirelesS Sensor nEtworks (ODYSSE). The main key innovation of ODYSSE is that it judiciously makes use of three mechanisms. The first one is duty cycling which consists in randomly switching on/off transceivers to save energy. The second one is opportunistic routing in which the next hop is not rigidly fixed: any node closer to the destination might become a relay. The third one, is source coding using LDPC, Low-Density Parity-Check codes. With asynchronous duty cycling as a starting point, the above techniques fit perfectly, yielding a robust low complexity protocol for highly constrained nodes. ODYSSE is implemented and installed in an experimental testbed composed of 45 Arduino nodes communicating with IEEE 802.15.4 (XBee) modules deployed in the large-scale platform FIT IoT-LAB. Results show that the performance obtained is very satisfying in both following scenarios: high load (images) and light load (reporting of infrequent event).

International audience; The Internet of Things (IoT) is projected to soon interconnect tens of billions of new devices, in large part also connected to the Internet. IoT devices include both high-end devices which can use traditional go-to operating systems (OS) such as Linux, and low-end devices which cannot, due to stringent resource constraints, e.g. very limited memory, computational power, and power supply. However, large-scale IoT software development, deployment, and maintenance requires an appropriate OS to build upon. In this paper, we thus analyse in detail the specific requirements that an OS should satisfy to run on low-end IoT devices, and we survey applicable operating systems, focusing on candidates that could become an equivalent of Linux for such devices i.e. a one-size-fits-most, open source OS for low-end IoT devices.

International audience; Call 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.

International audience; In this abstract, we start discussion on (a) leveraging ICN communication patterns to dynamically optimize the use of TSCH (Time Slotted Channel Hopping), a wireless link layer technology increasingly popular in the IoT. Through a series of experiments on FIT IoT-LAB interconnecting typical IoT hardware, we find that our proposal is fully robust against wireless interference, and almost halves the energy consumed for transmission when compared to CSMA. Most importantly, our adaptive scheduling prevents the time-slotted MAC layer from sacrificing throughput and delay.

International audience; Low-power, memory-ecient networking mechanisms are an essential part of the Internet of Things (IoT) and thus, fundamental to Smart Health applications lever-aging IoT. This paper presents Bloom-RPL, an optimization of RPL, the standard low-power routing protocol for IoT. This paper evaluates Bloom-RPL both on an emulator, and on an IoT testbed using real hardware , to conclude that Bloom-RPL dramatically improves RPL's link check scalability with respect to both IoT device density and convergence time needed to detect a link break.

International audience; Decades of software engineering shows that updating deployed software is not a nice-to-have, but a must-have. Deployed software (e.g. an IP protocol stack) is never bug free, and these bugs must be patched to increase its robustness and security, and as communication standards and application logic evolve, deployed software typically needs to evolve in parallel. As the IoT emerges, the same will apply to deployed IoT software.