Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

International audienceSoil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture

C3PO: A Network and Application Framework for Spontaneous and Ephemeral Social Networks

International audienceThe C3PO project promotes the development of new kind of social networks called Spontaneous and Ephemeral Social Networks (SESNs) dedicated to happenings such as cultural or sport events. SESNs rely on both opportunistic networks formed dynamically by the mobile devices of event attendees, and on an event-based communication model. Therefore, user can exchange digital contents with the other members of their SESNs, even without Internet access. This paper presents the framework developed in the C3PO project to provide network and application supports in such challenged networks. This framework exploits the different wireless interfaces of the mobile devices to interconnect them and to disseminate content through the resulting opportunistic network. At the application layer, this framework is composed of plugins that process locally the data stream to offer generic features, or to easily build applications dedicated to specific happenings

Following the Right Path: Using Traces for the Study of DTNs

Contact traces collected in real situations represent a popular material for the study of a Delay Tolerant Network. Three main use cases can be defined for traces: social analysis, performance evaluation and statistical analysis. In this paper, we perform a review on the technicalities of real trace collection and processing. First, we identify several factors which can influence traces during collection, filtering or scaling, and illustrate their impact on the conclusions, based on our experience with four datasets from the literature. We subsequently propose a list of criteria to be verified each time a trace is to be used, along with recommendations on which filters to apply depending on the envisioned use case. The rationale is to provide guidelines for researchers needing to perform trace analysis in their studies

Self-Organizing Maps Applied to Soil Conservation in Mediterranean Olive Groves

International audienceSoil degradation and hot climate explain the poor yield of olive groves in North Algeria. Edaphic and climatic data were collected from olive groves and analyzed by Self-Organizing Maps (SOMs). SOM is a non-supervised neural network that projects high-dimensional data onto a low-dimension topological map, while preserving the neighborhood. In this paper, we show how SOMs enable farmers to determine clusters of olive groves, to characterize them, to study their evolution and to decide what to do to improve the nutritional quality of oil. SOM can be integrated in the Intelligent Farming System to boost conservation agriculture

Cognitive privacy for personal clouds

This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure

Human Mobility Support for Personalised Data Offloading

International audienceWiFi Access Points (APs) can be used to offload data or computation tasks while users are commuting. However, due to APs' limited coverage, offloading performance is heavily impacted by the users' mobility. This work proposes to leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We define Offloading Regions (ORs) as areas where a commuter's mobility would enable offloading, and propose an unsupervised learning methodology to extract ORs from mobility traces. Then, we characterise and analyse ORs according to offloading opportunity metrics such as type, availability, total time to offload, and offloading delay. Results show that in 50% of the trips, users spend more than 48% of the travel time inside ORs extracted according to the proposed methodology. The ability to predict the next ORs would benefit offloading orchestration. Offloading mobility predictability, although crucial, proves to be challenging, expressed by the poor predictive performance of well-known models (≈ 37% acc. for the best predictor). We show that mobility regularity properties improve predictive performance up to ≈ 35%. Finally, we look into the impact of further OR extraction and prediction parameters. We show that the exploration phase length does not impact the discovery of low relevance ORs, and that both filtering low relevance OR and predicting multiple ORs increase predictability. By characterising the trade-off between mobility predictability and offloading opportunities in transit, we highlighting the need for offloading systems to adopt hybrid strategies, i.e., mixing opportunistic and predictive strategies. The conclusions and findings on offloading mobility properties are likely to generalise for varied urban scenarios given the high degree of similarity between the results obtained for the two different and independently collected mobility datasets

Towards a Geocentric Mobile Syndication System

International audienceThe present paper proposes a new approach towards filtering and processing the ever growing quantity of data published from mobile devices before it even reaches the Internet. We tackle this issue by circumscribing data to the zone where it is published (geocentricity) and allowing mobile device owners to republish the data they deem relevant (syndication). Results we obtained through simulation show that our solution enables to extract information that is relevant for a majority of users, whilst allowing less relevant data to be exchanged on a more local scale before it disappears entirely

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