3.1 Emergent mobile distributed systems

Uncertainty in the execution environment calls for designing mobile distributed systems that are able to run in a beforehand unknown, ever-changing context. Nevertheless, the complexity of such change cannot be tackled at system design-time. Emergent mobile distributed systems are systems which, due to their automated, dynamic, environment-dependent composition and execution, emerge in a possibly non-anticipated way and manifest emergent properties, i.e., both systems and their properties take their complete form only at runtime and may evolve afterwards. This contrasts with the typical software engineering process, where a system is finalized during its design phase. MiMove's research focuses on enabling the emergence of mobile distributed systems while assuring that their required properties are met. This objective builds upon pioneering research effort in the area of emergent middleware initiated by members of the team and collaborators  3, 5.

3.2 Large-scale mobile sensing and actuation

The extremely large scale and dynamicity expected in future mobile sensing and actuation systems lead to the clear need for algorithms and protocols for addressing the resulting challenges. More specifically, since connected devices will have the capability to sense physical phenomena, perform computations to arrive at decisions based on the sensed data, and drive actuation to change the environment, enabling proper coordination among them will be key to unlocking their true potential. Although similar challenges have been addressed in the domain of networked sensing, including by members of the team  11, the specific challenges arising from the extremely large scale of mobile devices – a great number of which will be attached to people, with uncontrolled mobility behavior – are expected to require a significant rethink in this domain. MiMove's research investigates techniques for efficient coordination of future mobile sensing and actuation systems with a special focus on their dependability.

3.3 Mobile social crowd-sensing

While mobile social sensing opens up the ability of sensing phenomena that may be costly or impossible to sense using embedded sensors (e.g., subjective crowdedness causing discomfort or joyfulness, as in a bus or in a concert) and leading to a feeling of being more socially involved for the citizens, there are unique consequent challenges. Specifically, MiMove's research focuses on the problems involved in the combination of the physically sensed data, which are quantitative and objective, with the mostly qualitative and subjective data arising from social sensing. Enabling the latter calls for introducing mechanisms for incentivising user participation and ensuring the privacy of user data, as well as running empirical studies for understanding the complex social behaviors involved. These objectives build upon previous research work by members of the team on mobile social ecosystems and privacy, as well as a number of efforts and collaborations in the domain of smart cities and transport that have resulted in novel mobile applications enabling empirical studies of social sensing systems.

3.4 Active and passive probing methods

We are developing methods that actively introduce probes in the network to discover properties of the connected devices and network segments. We are focusing in particular on methods to discover properties of home networks (connected devices and their types) and to distinguish if performance bottlenecks lie within the home network versus in the different network segments outside (e.g., Internet access provider, interconnects, or content provider). Our goal is to develop adaptive methods that can leverage the collaboration of the set of available devices (including end-user devices and the home router, depending on which devices are running the measurement software).

We are also developing passive methods that simply observe network traffic to infer the performance of networked applications and the location of performance bottlenecks, as well as to extract patterns of web content consumption. We are working on techniques to collect network traffic both at user's end-devices and at home routers. We also have access to network traffic traces collected on a campus network and on a large European broadband access provider.

3.5 Inferring user online experience

We are developing hybrid measurement methods that combine passive network measurement techniques to infer application performance with techniques from HCI to measure user perception as well as methods to directly measure application quality. We later use the resulting datasets to build models of user perception of network performance based only on data that we can obtain automatically from the user device or from user's traffic observed in the network.

3.6 Real time data analytics

The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. The time value of data is crucial for many IoT-based systems requiring real-time (or near real-time) control and automation. Such systems typically collect data continuously produced by “things” (i.e., devices), and analyze them in (sub-) seconds in order to act promptly, e.g., for detecting security breaches of digital systems, for spotting malfunctions of physical assets, for recommending goods and services based on the proximity of potential clients, etc. Hence, they require to both ingest and analyze in real-time data arriving with different velocity from various IoT data streams.

Existing incremental (online or streaming) techniques for descriptive statistics (e.g., frequency distributions, frequent patterns, etc.) or predictive statistics (e.g., classification, regression) usually assume a good enough quality dataset for mining patterns or training models. However, IoT raw data produced in the wild by sensors embedded in the environment or wearable by users are prone to errors and noise. Effective and efficient algorithms are needed for detecting and repairing data impurities (for controlling data quality) as well as understanding data dynamics (for defining alerts) in real-time, for collections of IoT data streams that might be geographically distributed. Moreover, supervised deep learning and data analytics techniques are challenged by the presence of sparse ground truth data in real IoT applications. Lightweight and adaptive semi-supervised or unsupervised techniques are needed to power real-time anomaly and novelty detection in IoT data streams. The effectiveness of these techniques should be able to reach a useful level through training on a relatively small amount of (preferably unlabeled) data while they can cope distributional characteristics of data evolving over time.

4.1 Mobile urban systems for smarter cities

With the massive scale adoption of mobile devices and further expected significant growth in relation with the Internet of Things, mobile computing is impacting most – if not all – the ICT application domains. One such domain is the one of "smart cities". The smart city vision anticipates that the whole urban space, including buildings, power lines, gas lines, roadways, transport networks, and cell phones, can all be wired together and monitored. Detailed information about the functioning of the city then becomes available to both city dwellers and businesses, thus enabling better understanding and consequently management of the city's infrastructure and resources. This raises the prospect that cities will become more sustainable environments, ultimately enhancing the citizens' well being. There is the further promise of enabling radically new ways of living in, regulating, operating and managing cities, through the increasing active involvement of citizens by ways of crowd-sourcing/sensing and social networking.

Still, the vision of what smart cities should be about has been and keeps evolving at a fast pace in close concert with the latest technology trends. It is notably worth highlighting how mobile and social network use has reignited citizen engagement, thereby opening new perspectives for smart cities beyond data analytics that have been initially one of the core foci for smart cities technologies. Similarly, open data programs foster the engagement of citizens in the city operation and overall contribute to make our cities more sustainable. The unprecedented democratization of urban data fueled by open data channels, social networks and crowd sourcing enables not only the monitoring of the activities of the city but also the assessment of their nuisances based on their impact on the citizens, thereby prompting social and political actions. However, the comprehensive integration of urban data sources for the sake of sustainability remains largely unexplored. This is an application domain that we focus on, further leveraging our research on emergent mobile distributed systems, large-scale mobile sensing & actuation, and mobile social crowd-sensing.

In particular, we concentrate on the following specialized applications:

• Democratization of urban data for healthy cities. We integrate the various urban data sources, especially by way of crowd-Xing, to better understand city nuisances. This goes from raw pollution sensing (e.g., sensing noise) to the sensing of its impact on citizens (e.g., how people react to urban noise and how this affects their health).
• Social applications. Mobile applications are being considered by sociologists as a major vehicle to actively involve citizens and thereby prompt them to become activists. We study such a vehicle from the ICT perspective and in particular elicit relevant middleware solutions to ease the development of such “civic apps".

4.2 Home network diagnosis

With the availability of cheap broadband connectivity, Internet access from the home has become a ubiquity. Modern households host a multitude of networked devices, ranging from personal devices such as laptops and smartphones to printers and media centers. These devices connect among themselves and to the Internet via a local-area network–a home network–that has become an important part of the “Internet experience”. In fact, ample anecdotal evidence suggests that the home network can cause a wide array of connectivity impediments, but their nature, prevalence, and significance remain largely unstudied.

Our long-term goal is to assist users with concrete indicators of the quality of their Internet access, causes of potential problems and–ideally–ways to fix them. We intend to develop a set of easy-to-use home network monitoring and diagnosis tools. The development of home network monitoring and diagnosis tools brings a number of challenges. First, home networks are heterogeneous. The set of devices, configurations, and applications in home networks vary significantly from one home to another. We must develop sophisticated techniques that can learn and adapt to any home network as well as to the level of expertise of the user. Second, Internet application and services are also heterogeneous with very diverse network requirements. We must develop methods that can infer application quality solely from the observation of (often encrypted) application network traffic. There are numerous ways in which applications can fail or experience poor performance in home networks. Often there are a number of explanations for a given symptom. We must devise techniques that can identify the most likely cause(s) for a given problem from a set of possible causes. Finally, even if we can identify the cause of the problem, we must then be able to identify a solution. It is important that the output of the diagnosis tools we build is “actionable”. Users should understand the output and know what to do.

In our patternship with Princeton University (associate team HOMENET) we have deployed monitoring infrastructure within users’ homes. We are developing a mostly passive measurement system to monitor the performance of user applications, which we call Network Microscope. We are developing Network Microscope to run in a box acting as home gateway. We have deployed these boxes in  50 homes in the US and  10 in France. The US deployment was ran and financed by the Wall Street Journal. They were interested in understanding the relationship between Internet access speed and video quality. We have been discussing with Internet regulators (in particular, FCC, ACERP, and BEREC) as well as residential access ISP in how Network Microscope can help overcome the shortcomings of existing Internet quality monitoring systems.

4.3 Mobile Internet quality of experience

Mobile Internet usage has boomed with the advent of ever smarter handheld devices and the spread of fast wireless access. People rely on mobile Internet for everyday tasks such as banking, shopping, or entertainment. The importance of mobile Internet in our lives raises people’s expectations. Ensuring good Internet user experience (or Quality of Experience—QoE) is challenging, due to the heavily distributed nature of Internet services. For mobile applications, this goal is even more challenging as access connectivity is less predictable due to user mobility, and the form factor of mobile devices limits the presentation of content. For these reasons, the ability to monitor QoE metrics of mobile applications is essential to determine when the perceived application quality degrades and what causes this degradation in the chain of delivery. Our goal is to improve QoE of mobile applications.

To achieve this goal, we are working on three main scientific objectives. First, we are working on novel methods to monitor mobile QoE. Within the IPL BetterNet we are developing the HostView for Android tool that runs directly on mobile devices to monitor network and system performance together with the user perception of performance. Second, we plan to develop models to predict QoE of mobile applications. We will leverage the datasets collected with HostView for Android to build data-driven models. Finally, our goal is to develop methods to optimize QoE for mobile users. We are currently developing optimization methods for interactive video applications. We envision users walking or driving by road-side WiFi access points (APs) with full 3G/LTE coverage and patchy WiFi coverage (i.e., community Wifi or Wifi APs on Lampposts) or devices with multiple 3G/LTE links. To achieve this goal, we plan to leverage multi-path and cross-layer optimizations.

4.4 Internet Scanning

Internet-wide scanning has enabled researchers to answer a wealth of new security and measurement questions ranging from “How are authoritarian regimes spying on journalists?” to “Are security notifications effective at prompting operators to patch?” Most of these studies have used tools like ZMap, which operates naiıvely, scanning every IPv4 address once. This simplicity enables researchers to easily answer a question once, but the methodology scales poorly when continually scanning to detect changes, as networks change at dramatically different rates. Service configurations change more frequently on cloud providers like Amazon and Azure than on residential networks. Internet providers in developing regions often have extremely short DHCP windows. Some networks are unstable with host presence varying wildly between different hours and others have distinct periodic patterns, e.g., hosts are only available during regional business hours. A handful of large autonomous systems have not had hosts present in decades. Our work in collaboration with Stanford University is developing more intelligent Internet-wide scanning methods to then implement a system that can scan continuously. Such a system will allow for up-to-date analysis of Internet trends and threats with real-time alerts of important events.

6.1 New software

7.1 Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

Participants: Yifan Du (MiMove), Francoise Sailhan (CNAM), Valérie Issarny (MiMove)

Opportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens' active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. This paper tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. We evaluate the proposed solution using an implementation-driven evaluation that leverages a dataset embedding nearly 1 million entries contributed by 550 crowdsensors over a year. Results show that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach.

7.2 IAM - Interpolation and Aggregation on the Move: Collaborative Crowdsensing for Spatio-temporal Phenomena

Participants: Yifan Du (MiMove), Francoise Sailhan (CNAM), Valérie Issarny (MiMove)

Crowdsensing allows citizens to contribute to the monitoring of their living environment using the sensors embedded in their mobile devices, e.g., smartphones. However, crowdsensing at scale involves significant communication, computation, and financial costs due to the dependence on cloud infrastructures for the analysis (e.g., interpolation and aggregation) of spatio-temporal data. This limits the adoption of crowdsensing by activists although sorely needed to inform our knowledge of the environment. As an alternative to the centralized analysis of crowdsensed observations, this paper introduces a fully distributed interpolation-mediated aggregation approach running on smartphones. To achieve so efficiently, we model the interpolation as a distributed tensor completion problem, and we introduce a lightweight aggregation strategy that anticipates the likelihood of future encounters according to the quality of the interpolation. Our approach thus shifts the centralized postprocessing of crowdsensed data to distributed pre-processing on the move, based on opportunistic encounters of crowdsensors through state-of-the-art D2D networking. The evaluation using a dataset of quantitative environmental measurements collected from 550 crowdsensors over 1 year shows that our solution significantly reduces-and may even eliminate-the dependence on the cloud infrastructure, while it incurs a limited resource cost on end devices. Meanwhile, the overall data accuracy remains comparable to that of the centralized approach.

7.3 Consent-driven data use in crowdsensing platforms: When data reuse meets privacy-preservation

Participants: Mariem Brahem (Inria PETRUS), Guillaume Scerri (Inria PETRUS), Nicolas Anciaux (Inria PETRUS), Valérie Issarny (MiMove)

Crowdsensing is an essential element of the IoT; it allows gathering massive data across time and space to feed our environmental knowledge, and to link such knowledge to user behavior. However, there are major obstacles to crowdsensing, including the preservation of privacy. The consideration of privacy in crowdsensing systems has led to two main approaches, sometimes combined, which are, respectively, to trade privacy for rewards, and to take advantage of privacy-enhancing technologies “anonymizing” the collected data. Although relevant, we claim that these approaches do not sufficiently take into account the users' own tolerance to the use of the data provided, so that the crowdsensing system guarantees users the expected level of confidentiality as well as fosters the use of crowdsensing data for different tasks. To this end, we introduce the l-completeness property, which ensures that the data provided can be used for all the tasks to which their owners consent as long as they are analyzed with l-1 other sources, and that no privacy violations can occur due to the related contribution of users with less stringent privacy requirements. The challenge, therefore, is to ensure l-completeness when analyzing the data while allowing the data to be used for as many tasks as possible and promoting the accuracy of the resulting knowledge. We address this challenge with a clustering algorithm sensitive to the data distribution, which is shown to optimize data reuse and utility using a dataset from a deployed crowdsensing application.

7.4 Privacy Preserving Multi Party Computation for Data-Analytics in the IoT-Fog-Cloud Ecosystem

Participants: Julio Lopez-Fenner (Universidad de la Frontera, Chile) Samuel Sepulveda (Universidad de la Frontera, Chile), Luiz Fernando Bittencourt (Universidade Estadual de Campinas, Brazil), Fabio Moreira Costa (Universidade Federal de Goias, Brazil), Nikolaos Georgantas (MiMove)

We propose an architecture for privacy pre- serving protocols in an IoT-Fog-Cloud ecosystem computing hierarchy. We consider the paradigms of Fog and Edge computing, together with a multi-party computation mechanism that enables secure privacy-preserving data processing in terms of exchanged messages and distributed comput- ing. We discuss the potential use of such an architecture in a scenario of pandemics where social distancing monitoring and privacy are pivotal to manage public health yet providing confidence to citizens.

7.5 Classification of Load Balancing in the Internet

Participants: Rafael Almeida (Universidade Federal de Minas Gerais, Brazil), Ítalo Cunha (Universidade Federal de Minas Gerais), Renata Teixeira (MiMove), Darryl Veitch (University of Technology Sydney), Christophe Diot (GoogleInc.)

Recent advances in programmable data planes, software-defined networking, and the adoption of IPv6, support novel, more complex load balancing strategies. We introduce the Multipath Classification Algorithm (MCA), a probing algorithm that extends traceroute to identify and classify load balancing in Internet routes. MCA extends existing formalism and techniques to consider that load balancers may use arbitrary combinations of bits in the packet header for load balancing. We propose optimizations to reduce probing cost that are applicable to MCA and existing load balancing measurement techniques. Through large-scale measurement campaigns, we characterize and study the evolution of load balancing on the IPv4 and IPv6 Internet with multiple transport protocols. Our results show that load balancing is more prevalent and that load balancing strategies are more mature than previous characterizations have found.

7.6 Leveraging Website Popularity Differences to Identify Performance Anomalies

Participants: Giulio Grassi (MiMove), Renata Teixeira (MiMove), Chadi Barakat (Inria DIANA), Mark Crovella (Boston University)

Web performance anomalies (e.g. time periods when metrics like page load time are abnormally high) have significant impact on user experience and revenues of web service providers. Existing methods to automatically detect web performance anomalies focus on popular websites (e.g. with tens of thousands of visits per minute). Across a wider diversity of websites, however, the number of visits per hour varies enormously, and some sites will only have few visits per hour. Low rates of visits create measurement gaps and noise that prevent the use of existing methods. We develop WMF, a web performance anomaly detection method applicable across a range of websites with highly variable measurement volume. To demonstrate our method, we leverage data from a website monitoring company, which allows us to leverage cross-site measurements. WMF uses matrix factorization to mine patterns that emerge from a subset of the websites to “fill in” missing data on other websites. Our validation using both a controlled website and synthetic anomalies shows that WMF's F1-score is more than double that of the state-of-the-art method. We then apply WMF to three months of web performance measurements to shed light on performance anomalies across a variety of 125 small to medium websites.

7.7 Implications of the Multi-Modality of User Perceived Page Load Time

Participants: Flavia Salutari (Télécom Paris), Diego Da Hora (Télécom Paris), Matteo Varvello (Brave Software), Renata Teixeira (MiMove), Vassilis Christophides (MiMove), Dario Rossi (HUAWEI Technologies France)

Web browsing is one of the most popular applications for both desktop and mobile users. A lot of effort has been devoted to speedup the Web, as well as in designing metrics that can accurately tell whether a webpage loaded fast or not. An often implicit assumption made by industrial and academic research communities is that a single metric is sufficient to assess whether a webpage loaded fast. In this work we collect and make publicly available a unique dataset which contains webpage features (e.g., number and type of embedded objects) along with both objective and subjective Web quality metrics. This dataset was collected by crawling over 100 websites-representative of the top 1 M websites in the Web-while crowdsourcing 6,000 user opinions on user perceived page load time (uPLT). We show that the uPLT distribution is often multi-modal and that, in practice, no more than three modes are present. The main conclusion drawn from our analysis is that, for complex webpages, each of the different objective QoE metrics proposed in the literature (such as AFT, TTI, PLT, etc.) is suited to approximate one of the different uPLT modes.

7.8 LZR: Identifying Unexpected Internet Services

Participants: Liz Izhikevich (Stanford), Renata Teixeira (MiMove), Zakir Durumeric (Stanford)

Internet-wide scanning is a commonly used research technique that has helped uncover real-world attacks, find cryptographic weaknesses, and understand both operator and miscreant behavior. Studies that employ scanning have largely assumed that services are hosted on their IANA-assigned ports, overlooking the study of services on unusual ports. In this work, we investigate where Internet services are deployed in practice and evaluate the security posture of services on unexpected ports. We show protocol deployment is more diffuse than previously believed and that protocols run on many additional ports beyond their primary IANA-assigned port. For example, only 3/

7.9 Traffic Refinery: Cost-Aware Traffic Representation for Machine Learning in Networks

Participants: Francesco Bronzino (Université Savoie Mont Blanc), Paul Schmitt (Princeton), Sara Ayoubi (Nokia Bell Labs), Hyojoon Kim (Princeton), Renata Teixeira (MiMove), Nick Feamster (University of Chicago)

Ever more frequently network management tasks apply machine learning on network traffic. Both the accuracy of a machine learning model and its effectiveness in practice ultimately depend on the representation of raw network traffic as features. Often, the representation of the traffic is as important as the choice of the model itself; furthermore, the features that the model relies on will ultimately determine where (and even whether) the model can be deployed in practice. This work develops a new framework and system that enables a joint evaluation of both the conventional notions of machine learning performance (e.g., model accuracy) and the systems-level costs of different representations of network traffic. We highlight these two dimensions for a practical network management task, video streaming quality inference, and show that the appropriate operating point for these two dimensions depends on the deployment scenario. We demonstrate the benefit of exploring a range of representations of network traffic and present Traffic Refinery, a proof-of-concept reference implementation that both monitors network traffic at 10 Gbps and transforms the traffic in real time to produce a variety of feature representations for machine learning models. Traffic Refinery both highlights this design space and makes it possible for network operators to easily explore different representations for learning, balancing systems costs related to feature extraction and model training against the resulting model performance.

7.10 Social Participation Network: Linking things, services and people to support participatory processes

Participants: Grigorios Piperagkas (MiMove), Rafael Angarita (ISEP), Valérie Issarny (MiMove)

Digital technologies have impacted almost every aspect of our society, including how people participate in activities that matter to them. Indeed, digital participation allows people to be involved in different societal activities at an unprecedented scale through the use of Information and Communication Technologies (ICT). Still, enabling participation at scale requires making it seamless for people to: interact with a variety of software platforms, get information from connected physical objects and software services, and communicate and collaborate with their peers. Toward this objective, we introduce and formalize the concept of Social Participation Network, which captures the diverse participation relationships-between people, digital services and connected things-supporting participatory processes. We further present the design of an associated online service to support the creation and management of Social Participation Networks. The design advocates the instantiation of Social Participation Networks within distinct participation contexts-spanning, e.g., private institutions, neighbor communities, and governmental institutions-so that the participants' information and contributions to participation remain isolated and private within the given context.

8.1 Bilateral grants with industry

• “Monitoring and diagnosis of Internet QoE”, Google Faculty Award to Renata Teixeira and D. Choffnes (Northeastern University), 2017-2020.
• “Application Performance Bottleneck Detection”, Comcast Gift to Renata Teixeira, 2018-2020.

9.1 International initiatives

9.2 International research visitors

9.3 National initiatives

“BottleNet: Understanding and Diagnosing End-to-end Communication Bottlenecks of the Internet”, project funded by the French research agency (ANR), from Feb 2016 to Mar 2021.

10.1 Promoting scientific activities

10.2 Teaching - Supervision - Juries

11.1 Major publications

• 1 inproceedings RafaelR. Almeida, ÍtaloÍ. Cunha, RenataR. Teixeira, DarrylD. Veitch and ChristopheC. Diot. Classification of Load Balancing in the Internet IEEE INFOCOM 2020 - International Conference on Computer Communications Beijing / Virtual, China April 2020
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• 2 article RafaelR. Angarita, BrunoB. Lefèvre, ShohrehS. Ahvar, EhsanE. Ahvar, NikolaosN. Georgantas and ValerieV. Issarny. Universal Social Network Bus: Towards the Federation of Heterogeneous Online Social Network Services ACM Transactions on Internet Technology 2019
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• 3 articleAmelA. Bennaceur and ValérieV. Issarny. Automated Synthesis of Mediators to Support Component InteroperabilityIEEE Transactions on Software Engineering2015, 22
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• 4 articleBenjaminB. Billet and ValérieV. Issarny. Spinel: An Opportunistic Proxy for Connecting Sensors to the Internet of ThingsACM Transactions on Internet Technology172March 2017, 1 - 21
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• 5 inproceedingsGordonG. Blair, AmelA. Bennaceur, NikolaosN. Georgantas, PaulP. Grace, ValérieV. Issarny, VatsalaV. Nundloll and MassimoM. Paolucci. The Role of Ontologies in Emergent Middleware: Supporting Interoperability in Complex Distributed SystemsBig Ideas track of ACM/IFIP/USENIX 12th International Middleware ConferenceLisbon, Portugal2011, URL: http://hal.inria.fr/inria-00629059/en
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• 6 articleGeorgiosG. Bouloukakis, NikolaosN. Georgantas, PatientP. Ntumba and ValérieV. Issarny. Automated synthesis of mediators for middleware-layer protocol interoperability in the IoTFuture Generation Computer Systems101December 2019, 1271-1294
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• 7 article FrancescoF. Bronzino, PaulP. Schmitt, SaraS. Ayoubi, GuilhermeG. Martins, RenataR. Teixeira and NickN. Feamster. Inferring Streaming Video Quality from Encrypted Traffic: Practical Models and Deployment Experience Proceedings of the ACM on Measurement and Analysis of Computing Systems 3 3 December 2019
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• 8 articleMauroM. Caporuscio, Pierre-GuillaumeP.-G. Raverdy and ValérieV. Issarny. ubiSOAP: A Service Oriented Middleware for Ubiquitous NetworkingIEEE Transactions on Services Computing992012, URL: http://hal.inria.fr/inria-00519577
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• 9 inproceedings YifanY. Du, FrancoiseF. Sailhan and ValerieV. Issarny. Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations PerCom 2020: IEEE International Conference on Pervasive Computing and Communications Austin / Virtual, United States March 2020
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• 10 inproceedings GiulioG. Grassi, RenataR. Teixeira, ChadiC. Barakat and MarkM. Crovella. Leveraging Website Popularity Differences to Identify Performance Anomalies INFOCOM 2021 - IEEE International Conference on Computer Communications Vancouver / Virtual, Canada May 2021
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• 11 articleSaraS. Hachem, AnimeshA. Pathak and ValérieV. Issarny. Service-Oriented Middleware for Large-Scale Mobile Participatory SensingPervasive and Mobile Computing2014, URL: http://hal.inria.fr/hal-00872407
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11.2 Publications of the year