Vers une capture participative mobile efficace : assignation des tâches et déchargement des données

The ubiquity of sensors-equipped mobile devices has enabled people to contribute data via crowdsensing systems. This emergent paradigm comes with various applications. However, new challenges arise given users involvement in data collection process. In this context, we introduce collaborative sensing schemes which tackle four main questions: How to assign sensing tasks to maximize data quality with energy-awareness? How to minimize the processing time of sensing tasks? How to motivate users to dedicate part of their resources to the crowdsensing process ? and How to protect participants privacy and not impact data utility when reporting collected sensory data ? First, we focus on the fact that smart devices are energy-constrained and develop task assignment methods that aim to maximize sensor data quality while minimizing the overall energy consumption of the data harvesting process. The resulting contribution materialized as a Quality and Energy-aware Mobile Sensing Scheme (QEMSS) defines first data quality metrics then models and solves the corresponding optimization problem using a Tabu-Search based heuristic. Moreover, we assess the fairness of the resulted scheduling by introducing F-QEMSS variant. Through extensive simulations, we show that both solutions have achieved competitive data quality levels when compared to concurrent methods especially in situations where the process is facing low dense sensing areas and resources shortcomings. As a second contribution, we propose to distribute the assignment process among participants to minimize the average sensing time and processing overload com- pared to a fully centralized approach. Thus, we suggest to designate some participants to carry extra sensing tasks and delegate them to appropriate neighbors. The new assign- ment is based on predicting users local mobility and sensing preferences. Accordingly, we develop two new greedy-based assignment schemes, one only Mobility-aware (MATA) and the other one accounting for both preferences and mobility (P-MATA), and evaluate their performances. Both MATA and P-MATA consider a voluntary sensing process and show that accounting for users preferences minimize the sensing time. Having showing that, our third contribution in this thesis is conceived as an Incentives-based variant, IP-MATA+. IP-MATA+ incorporates rewards in the users choice model and proves their positive impact on enhancing their commitment especially when the dedicated budget is shared function of contributed data quality. Finally, our fourth and last contribution addresses the seizing of users privacy concerns within crowdsensing systems. More specifically, we study the minimization of the incurred privacy leakage in data uploading phase while accounting for the possible quality regression. That is, we assess simultaneously the two competing goals of ensuring queriers required data utility and protecting participants’ sensitive information. Thus, we introduce a trust entity to the crowdsensing traditional system. This entity runs a general privacy-preserving mechanism to release a distorted version of sensed data that responds to a privacy-utility trade-off. The proposed mechanism, called PRUM, is evaluated on three sensing datasets, different adversary models and two main data uploading scenarios. Results show that a limited distortion on collected data may ensure privacy while maintaining about 98% of the required utility level.The four contributions of this thesis tackle competing issues in crowdsensing which paves the way at facilitating its real implementation and aims at broader deploymentL’ubiquité des terminaux intelligents équipés de capteurs a donné naissance à un nouveau paradigme de collecte participative des données appelé Crowdsensing. Pour mener à bien les tâches de collecte, divers défis relatifs à l’implication des participants et des demandeurs de services doivent être relevés. Dans ce contexte, nous abordons quatre questions majeures inhérentes à ce problème: Comment affecter les tâches de collecte afin de maximiser la qualité des données d’une façon éco-énergétique ? Comment minimiser le temps nécessaire à la collecte et au traitement des tâches? Comment inciter les participants à dédier une partie de leurs ressources pour la collecte? et Comment protéger la vie privée des participants tout en préservant la qualité des données reportées ? Tout d’abord, nous nous intéressons au fait que les ressources énergétiques des terminaux mobiles restent limitées. Nous introduisons alors des modèles de déploiement de tâches qui visent à maximiser la qualité des données reportées tout en minimisant le coût énergétique global de la collecte. Ainsi, notre première contribution se matérialise en un modèle d’allocation appelé, QEMSS. QEMSS définit des métriques de qualité de données et cherche à les maximiser en se basant sur des heuristiques utilisant la recherche taboue. De plus, afin de rendre le processus d’allocation résultante plus équitable, nous faisons appel à un deuxième algorithme, F-QEMSS, extension de QEMSS. Les deux solutions ont permis d’obtenir des niveaux de qualité de données compétitifs principalement dans les situations défavorables des zones de faible densité ou de ressources limitées. En outre, afin de minimiser le temps moyen de collecte et de traitement des données, une deuxième phase d’allocation distribuée est ajoutée. Plus précisément, nous proposons dans cette deuxième contribution de désigner des participants responsables de déléguer des tâches. Ces derniers prédisent le comportement d’autres utilisateurs en termes de mobilité et de préférences de collecte. Par conséquent, nous développons deux types d’allocation; MATA qui ne tient compte que de la mobilité et P-MATA qui tient compte à la fois de la mobilité et des préférences des participants. Les deux allocations démontrent que l’estimation des préférences des utilisateurs minimise le temps de collecte et évite le rejet des tâches. La troisième contribution de cette thèse, IP-MATA+, propose des incitations aux participants, ce qui favorise leur engagement aux campagnes de collecte notamment quand le budget dédié est partagé en fonction de la qualité des contributions. Pour finir, nous considérons la problématique de la vie privée des participants au crowdsensing. Particulièrement, nous ciblons la minimisation du risque de divulgation de la vie privée durant la phase du déchargement tout en veillant à l’utilité des données collectées. Ainsi, la quatrième contribution de cette thèse vise à assurer simultanément deux objectifs concurrents, à savoir assurer l’utilité des données nécessaire aux demandeurs et protéger les informations sensibles des participants. Pour ce faire, nous introduisons une entité de confiance dans le système de collecte ayant pour rôle d’exécuter un mécanisme qui génère une version altérée de la donnée collectée qui répond au compromis de protection et d’utilité. La solution développée, appelée PRUM, a été évaluée sur des datasets de collecte participative en variant les scénarios d’attaque et de déchargement des données. Les résultats obtenus prouvent qu’une altération limitée des données collectées peut assurer une protection des informations sensibles des participants tout en préservant environ 98% de l’utilité des données obtenue pour les demandeurs. Pour conclure, nos contributions abordent diverses problématiques complémentaires inhérentes à la collecte participative des données ouvrant la voie à des mises en œuvre réelles et facilitant leur déploiemen

QEMSS: A selection scheme for participatory sensing tasks.

International audienceThe new generation of smart devices, equipped with a large variety of sensors, enhances the Participatory Sensing of data. However, many issues arise when selecting participants to perform the sensing tasks. These issues are necessarily related to the limited energetic resources of devices, the impact of users mobility as well as the quality of collected data, recently defined as “Quality of Information” (QoI). In this context, we propose QEMSS (QoI and Energy aware Mobile Sensing Scheme) as a selection scheme for participatory sensing tasks, taking into consideration the quality of sensed data, QoI, and the dedicated energy for their acquisition. The aim of our model QEMSS is to select, among all participants in the sensing campaigns, the subset of users who maximizes the QoI of non redundant information while minimizing the overall energy consumption. Todo so, we illustrate our selection scheme based on the Tabu Search algorithm in order to achieve a sub-optimal solution. Simulation results were compared to two other State of The art schemes: the Random Selection (RS) and a method based on a greedy search (DPS). Our scheme is proved to be as performing as the two othermethods. Particularly, our scheme achieves a very high quality of information in challenging scenarios such as low dense areas and/or low energetic resources

Allocation Equitable des Tâches de Collecte Participative de Données

International audienceLa collecte participative de données est un paradigme émergent qui permet aux utilisateurs munis de dispositifs intelligents de collecter et de partager des données sur un phénomène particulier. Pour assurer une bonne qualité d'information (QoI), il faut garantir l'engagement des participants tout en respectant la contrainte énergétique de leurs mobiles. Ce trade-off est difficile à réaliser vu la nature contradictoire des deux objectifs de la maximisation de la QoI et la minimisation de la fréquence de capture de données des terminaux des utilisateurs. Dans cet article, nous élaborons un modèle d'allocation équitable des tâches de collecte participative afin d'optimiser l'autonomie des terminaux et atteindre le niveau de QoI requis par les services/usagers demandeurs. Nous présentons d'abord le problème d'optimisation multi-objectif correspondant. Ensuite, nous proposons l'algorithme d'allocation équitable des tâches de sensing, (F-QEMSS), où nous avons eu recours à l'heuristique de la recherche taboue (TS) pour résoudre ce problème. Les résultats de la simulation montrent l'efficacité de notre solution. Particulièrement, F-QEMSS réalise un taux d'équité de 96% mesuré par l'indice de Jain tout en préservant le même niveau de QoI atteint par des algorithmes d'allocation non équitables

QoI and Energy-Aware Mobile Sensing Scheme: A Tabu-Search Approach

International audienceMobile phones equipped with a rich set of embedded sensors enhance participatory sensing to collect data for different applications. However, many challenges arise when selecting participants to perform sensing tasks. Among these challenges, we can cite energy consumption, users' mobility impact and the quality of retrieved data, recently defined as Quality of Information (QoI). In this work, we study the QoI and Energy-aware Mobile Sensing (QEMSS) problem. Hence, for a given set of users, a sensing area and data quality requirements, the objective of QEMSS is to find the subset of users that maximizes QoI in terms of spatial and temporal metrics while minimizing the overall energy consumption and reducing the redundancy during the sensing process. We propose a meta-heuristic algorithm based on Tabu-Search to provide a sub-optimal solution. Simulation results, for both deterministic and unknown participants' trajectories, are compared to other state-of-the-art methods. This allows showing that our approach outperforms both the greedy- based and the random selection strategies. Particularly, the achieved data quality by our scheme is significantly higher in challenging scenarios such as low dense areas or scarce users' energy resources

Fair QoI and Energy-aware Task Allocation in Participatory Sensing

International audienc

Preference and Mobility-Aware Task Assignment in Participatory Sensing

International audienceParticipatory Sensing is a new paradigm of mobile sensing where users are actively involved in leveraging the power of their smart devices to collect and share information. Motivated by its potential applications, we tackle in this paper the task assignment problem for a requester encountering a crowd of participants while considering their mobility model and sensing preferences. We aim to minimize the overall processing time of sensing tasks. Hence, we introduce first the Mobility-Aware Task Assignment scheme in both oFfline (MATAF) and oNline (MATAN) models where requesters investigate the participants’ arrival model in different compounds of the sensing region. Further, we enhance such schemes by jointly taking into account participants’ mobility and sensing preferences. We advocate then two other task assignment models, P-MATAF (offline) and P- MATAN (online). All proposed algorithms adopt a greedy- based selection strategy and address the minimization of the average makespan of all sensing tasks. We conduct extensive performance evaluation based on real traces while varying the number of tasks and associated workloads. Results proved that our proposed schemes have achieved lower average makespan and higher number of delegated tasks

An Energy-aware End-to-End Crowdsensing Platform: Sensarena

International audienceNowadays, smart-devices come with a rich set of built-in sensors besides being full-fledged processing and communicating handsets. This empowers the crowd to collect and share sensed data about various city-related phenomena, a new paradigm denoted as Crowdsensing. In this context, we introduce Sensarena; an end-to-end general-use crowdsensing platform which consists of three main elements: two different android- based applications and a central server. The first mobile application is destined to the participants to conduct sensing campaigns and the second is for requestors to submit their sensing requests. Besides, the server side is designed to host energy-aware sensing tasks assignment mechanisms and storage of different types of data. The developed platform has been exhaustively tested for different scenarios and proved a competitive performance while responding to both participants and requestors requirements

On The Privacy-Utility Tradeoff in Participatory Sensing Systems

International audienceThe ubiquity of sensors-equipped mobile devices has enabled citizens to contribute data via participatory sensing systems. This emergent paradigm comes with various applications to improve users’ quality of life. However, the data collection process may compromise the participants’ privacy when report- ing data tagged or correlated with their sensitive information. Therefore, anonymization and location cloaking techniques have been designed to provide privacy protection, yet to some cost of data utility which is a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring the queriers’ required data utility and protecting the participants’ privacy. First, we introduce a trust worthy entity to the participatory sensing traditional system. Also, we propose a general privacy-preserving mechanism that runs on this entity to release a distorted version of the sensed data in order to minimize the information leakage with its associated private information. We demonstrate how to identify a near-optimal solution to the privacy-utility tradeoff by maximizing a privacy score while considering a utility metric set by data queriers (service providers). Furthermore, we tackle the challenge of data with large size alphabets by investigating quantization techniques. Finally, we evaluate the proposed model on three different real datasets while varying the prior knowledge and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants’ privacy while maintaining about 98% of the required data utility

A lower energetic, protein and uncooked cornstarch intake is associated with a more severe outcome in glycogen storage disease type III: an observational study of 50 patients.

International audienceBackground:Glycogen storage disease type III (GSDIII), due to a deficiency of glycogen debrancher enzyme (GDE), is particularly frequent in Tunisia. Phenotypic particularities of Tunisian patients remain unknown. Our aim was to study complications of GSDIII in a Tunisian population and to explore factors interfering with its course.Methods:A retrospective longitudinal study was conducted over 30 years (1986–2016) in the referral metabolic center in Tunisia.Results:Fifty GSDIII patients (26 boys), followed for an average 6.75 years, were enrolled. At the last evaluation, the median age was 9.87 years and 24% of patients reached adulthood. Short stature persisted in eight patients and obesity in 19 patients. Lower frequency of hypertriglyceridemia (HTG) was associated with older patients (p<0.0001), higher protein diet (p=0.068) and lower caloric intake (p=0.025). Hepatic complications were rare. Cardiac involvement (CI) was frequent (91%) and occurred early at a median age of 2.6 years. Severe cardiomyopathy (50%) was related to lower doses of uncooked cornstarch (p=0.02). Neuromuscular involvement (NMI) was constant, leading to a functional discomfort in 64% of cases and was disabling in 34% of cases. Severe forms were related to lower caloric (p=0.005) and protein intake (p<0.015).Conclusions:A low caloric, protein and uncooked cornstarch intake is associated with a more severe outcome in GSDIII Tunisian patients. Neuromuscular and CIs were particularly precocious and severe, even in childhood. Genetic and epigenetic factors deserve to be explored