Facing the hard truth

Full title: Facing the hard truth: showing users what mobile apps can learn about them from the location data they collect
Website: http://datatransparencylab.org/dtl-2017/grantees-2017/
Funding: Data Transparency Lab
Timeframe: 2017 - 2019
Role: Co-Principal Investigator
While most mobile applications nowadays fulfil basic privacy-preserving properties, they still leave significant surfaces open for privacy breaches that leverage on subtle features of the collected data. The focus of this proposal is to analyse this problem for trajectory data. Such data is the foundation of Location-Based Services (LBS), which represent a significant portion of today's most popular mobile services, but may also be collected by applications that offer opt-in geo-referencing or even by the mobile operator itself. While the the user is usually informed at install time that the service will access positioning data, she is given absolutely no information about the frequency with which data is collected and how it is used precisely upon collection - including purposes that go beyond the primary objective of the application. The objective of this project is raising user awareness about the privacy leakage of trajectory data. To this end, we will provide end-users with (i) a clear, intuitive visualizations of the precise spatiotemporal trajectory information gathered by each mobile application, (ii) an equivalent visualization from localization data possibly gathered by the operator from their mobile network activity, and (iii) indirect knowledge that may infer from the trajectory data it gathered by using data mining techniques.


Full title: Re-thinking the fundamentals of vehicular networking with transportation theory and complex network science
Website: http://www.wcsg.ieiit.cnr.it/Reflex/website/
Funding: EU FP7 PEOPLE CIG 2013
Timeframe: 2014 - 2018
Role: Scientist in Charge
ReFleX aims at characterizing in a comprehensive manner the topological features of large-scale urban vehicular networks built on top of DSRC-based V2V and V2I communication technologies. To that end, the project adopts a multidisciplinary approach, bringing together tools from vehicular networking, wireless communications, transportation theory, and complex network science.
The study will unveil the (yet largely unknown) vehicular network connectivity properties induced by a pervasive adoption of DSRC technolgies in urban environments. That way, ReFleX will evidence the strengths, weaknesses and overall capabilities of large-scale V2V/V2I communication systems, including their actual availability and reliability.
In turn, the understanding of the basic features of the vehicular network topology will allow testing current protocols and architectures intended for DSRC-based networks, by verifying their fitness to the connectivity dynamics of large-scale urban vehicular networks. ReFleX will then take an original bottom-up approach to protocol desgin that puts the network connectivity at the core of the process, and it will propose enhancements and novel network solutions that adapt to the actual features of the network topology.


Full title: Data Aware Wireless Networks for Internet of Everything
Funding: EU H2020-MSCA-RISE-2017
Timeframe: 2017 - 2021
Role: CNR-IEIIT Principal Investigator
Whilst traffic demand is increasing exponentially, network operators' revenue remains flat. There is an urgent for data driven 4G/5G networks. DAWN4IoE exploits heterogeneous big data analytics to optimize both the deployment and operations of wireless networks. It designs protocols that enable future Data Aware Wireless Networks (DAWN) for enabling a new age of Internet of Everything (IoE). The proposal has been developed to address the following open issues in data driven flexible systems: (1) characterizing user mobility and wireless data traffic patterns; (2) inferring user Quality-of-Experience (QoE) from combining data sets; (3) using data analytics to assist cell planning; (4) using data-driven techniques to optimise the network using Self-Organising-Network (SON) algorithms; and (5) optimally caching data to accelerate and optimise data storage and transmission.


Full title: Anonymous mobile traffic DAta GEneration
Funding: PIA 2016
Timeframe: 2016 - 2018
Role: CNR-IEIIT Principal Investigator
ADAGE aims at developing solutions for the preservation of user privacy in data derived from Mobile Call Records or from passive monitoring probes deployed in mobile networks. The solutions proposed in the project will abide different rigorous anonymization criteria while retaining the informational value the data in terms of user mobility, subscriber activity patterns, or communication graphs.



Full title: Mobile context-Adaptive CAching for COntent-centric networking
Website: https://macaco.inria.fr/
Funding: EU CHIST-ERA 2012
Timeframe: 2013 - 2016
Role: CNR-IEIIT Principal Investigator
Finding new ways to manage the increased data usage and to improve the level of service required by the new wave of smartphones applications is an essential issue. MACACO project proposes an innovative solution to this problem by focusing on data offloading mechanisms that take advantage of context and content information. Our intuition is that if it is possible to extract and forecast the behaviour of mobile network users in the three- dimensional space of time, location and interest (i.e. 'what', 'when' and 'where' users are pulling data from the network), it is possible to derive efficient data offloading protocols. Such protocols would pre-fetch the identified data and cache it at the network edge at an earlier time, preferably when the mobile network is less charged, or offers better quality of service. Caching can be done directly at the mobile terminals, but as well at the edge nodes of the network (e.g., femtocells or wireless access points).
Building on previous research efforts in the fields of social wireless networking, opportunistic communications and content networking, MACACO will address several issues. The first one is to derive appropriate models for the correlation between user interests and their mobility. Lots of studies have characterized mobile nodes mobility based on real world data traces, but knowledge about the interactions with user interests in this context is still missing. To fill this gap, MACACO proposes to acquire real world data sets to model mobile node behaviour in the aforementioned three-dimensional space. The second issue addressed is the derivation of efficient data-offloading algorithms leveraging the large-scale data traces and corresponding models. Firstly, simple and efficient prediction algorithms will be derived to forecast the node’s mobility and interests. Then, MACACO has to output data pre-fetching mechanisms that both improves the perceived quality of service of the mobile user and noticeably offloads peack bandwidth demands at the cellular network.


Full title: Adaptive Behavior and Cloud Distribution
Website: http://abcd.lip6.fr/
Funding: ANR INFRA 2013
Timeframe: 2013 - 2017
Role: Work Package Leader
The pervasiveness of information and communication technologies is driving a social evolution, whose tangible effect is observable in human mobility behavior and digital usages. Originally, the Internet was conceived to serve fix and sedentary usages, while current trends clearly show that future Internet users will be increasingly mobile and nomadic. The extremely rapid pace at which this evolution is taking place practically manifests through poor service availability, and represents a major impediment for advanced services. The exponential growth of mobile Internet usages calls for a novel Cloud computing and resource-provisioning solution to offload the access networks, which need to be geographically distributed and temporally adaptive. Recent studies, showing that mobile user movement patterns can be accurately predicted by analyzing samples of their displacements, suggest that forecasting network customer mobility and usages can play a major role to that end. However, the data consumption dynamics and their correlation with macroscopic user mobility behaviors are largely unknown today. The reason is the still insufficient coordination between traffic engineering, usage profiling and user mobility detection, and the lack of public exploitable access data traces.
In this project, we aim at filling this void, from both a fundamental and technological standpoint, creating an interdisciplinary expertise of academic laboratories on cloud networking, wireless access and mobile networking and socio-economic impact of telecommunications, as well as of industry R&D on socioeconomic analysis of mobile networks and on large-scale WiFi network management. Our purpose is to define adequate solutions to capture mobility patterns, correlating mobility with usages for a mobile Cloud offloading architecture for telecom networks. This solution requires a reliable estimation of mobility and usages metrics for adaptive Cloud distribution and resource allocation. Indeed, the network efficiency might be very positively affected if selected cloud servers could be proactively distributed close to identifiable rendezvous points regularly approached by masses of users, or along geolocated consumption flows adaptively determined. This solution aims to master "flash-mobs effects" while increasing network efficiency. The usages we care about are mobile Internet services such as streaming, digital maps, social networks, and software updates. Mobile access networks commonly suffer upon smartphone software releases and during special events aggregating large masses of persons sharing similar interests (e.g., sport events), hence concurrently accessing similar services. Such events shall be detected in real-time so as to dynamically allocate network resources and move virtual machines close to access gateways. Cloud services can largely profit from such a distributed access: by hosting resources out of the user terminal, they enable remote processing and storage of personal data. Mobile equipment has notably limited computing and energy resources, and the presence of close-enough cloud virtual machines and their adaptive migration along users’ displacements can allow computation offloading, grant important battery energy savings, while guaranteeing connection resiliency.

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marco.fiore at ieiit.cnr.it marco.fiore at inria.fr