7.1.1 Gardinet

• Keyword:
  Distributed networks
• Functional Description:

  Gardinet (previously DragonNet) is a generic framework for network coding in wireless networks. It is a initially result of the GETRF project of the Hipercom2 team.

  It is based on intra-flow coding where the source divides the flow in a sequence of payloads of equal size (padding may be used). The design keys of DragonNet are simplicity and universality, DragonNet does not use explicit or implicit knowledge about the topology (such as the direction or distance to the source, the loss rate of the links, ...). Hence, it is perfectly suited to the most dynamic wireless networks. The protocol is distributed and requires minimal coordination. DragonNet architecture is modular, it is based on 5 building blocks (LIB, SIG, Protocol, SEW and DRAGON). Each block is almost independent. This makes DragonNet generic and hence adaptable to many application scenarios. DragonNet derives from a prior protocol called DRAGONCAST. Indeed, DragonNet shares the same principles and theoretical overview of DRAGONCAST. It enriches DRAGONCAST by the information base and signaling required to perform broadcast in wireless networks and in wireless sensor networks in particular.

• URL:
  http://­gitlab.­inria.­fr/­gardinet
• Contact:
  Cédric Adjih
• Participants:
  Antonia Masucci, Cédric Adjih, Hana Baccouch, Ichrak Amdouni

7.1.2 SWIF-codec

• Name:
  An open-source sliding window FEC codec
• Keyword:
  Error Correction Code
• Functional Description:

  This development is done in the context of the "Coding for Efficient Network Communications" IRTF Research Group (NWCRG, [https://datatracker.ietf.org/rg/nwcrg]) and IETF hackathon.

  This work has strong relationships with the Generic API I-D [https://datatracker.ietf.org/doc/draft-roca-nwcrg-generic-fec-api/] and RFC 8681 on RLC codes [https://www.rfc-editor.org/rfc/rfc8681] as examples of sliding window codes.

• URL:
  https://­github.­com/­irtf-nwcrg/­swif-codec
• Authors:
  Vincent Roca, Cédric Adjih, Oumaima Attia, François Michel
• Contact:
  Vincent Roca

7.1.3 SimBle

• Name:
  Simulating Privacy-Preserving Real-World BLE Traces
• Keywords:
  Privacy, Simulation, Bluetooth
• Functional Description:
  SimBle is the first BLE simulation stack capable of generating traces that preserve privacy. It introduces resolvable private addresses that are the core to BLE device and network privacy-provisions. It is capable of emulating the behavior of any real BLE device/hardware. Users have to choose the appropriate device class they want to test, based on the targeted device. It resolved the lack of ground truth for scalable scenarios after the introduction of MAC address randomization.
• URL:
  https://­gitlab.­inria.­fr/­mabhishe/­simble
• Publication:
  hal-03125920
• Contact:
  Abhishek Mishra
• Participants:
  Abhishek Mishra, Aline Carneiro Viana, Nadjib Achir

7.1.4 RIOT

• Name:
  RIOT
• Keywords:
  Internet of things, Operating system, Sensors, Iot, Wireless Sensor Networks, Internet protocols
• Scientific Description:
  While requiring as low as 1,5kB of RAM and 5kB or ROM, RIOT offers real time and energy efficiency capabilities, as well as a single API (partially POSIX compliant) across heterogeneous 8-bit, 16-bit and 32-bit low-hardware. This API is developer-friendly in that it enables multi-threading, standard C and C++ application programming and the use of standard debugging tools (which was not possible so far for embedded programming). On top of this, RIOT includes several network stacks, such as a standard IPv6/6LoWPAN stack and a information-centric network stack (based on CCN).
• Functional Description:

  RIOT is an Open Source operating system that provides standard protocols for embedded systems. RIOT allows, for example, the development of applications that collect sensor data and transmit it to a central node (e.g. a server). This data can then be used for smart energy management for instance.

  RIOT is specially designed for embedded systems, which are strongly constrained in memory and energy. Further, RIOT can easily be ported to different hardware devices and follows the latest evolution of IP standards.

  RIOT applications can readily be tested in the FIT IoT-Lab, which provides a large-scale infrastructure facility with 3000 nodes for testing remotely small wireless devices.

• URL:
  http://­www.­riot-os.­org
• Contact:
  Emmanuel Baccelli
• Participants:
  Emmanuel Baccelli, Koen Zandberg, Oliver Hahm, Francois-Xavier Molina, Alexandre Abadie
• Partners:
  Freie Universität Berlin, University of Hamburg

7.1.5 SIMBox Fraud Simulator

• Name:
  SIMBox fraud strategies and detection simulator
• Keywords:
  Simulation, SIMBox fraud, Fraud detection, Data analytics
• Functional Description:
  SIMBoxFraudSimulator is an open-source simulator of SIMBox fraud strategies and detection methods in LTE networks. It is based on the well-known and broadly used LTE-SIM tool from which we added all the required components to simulate SIMBox fraud. Besides, we inserted various traffic generators and realistic mobility modeling, providing lifelike CDR data and ground-truth for comprehensive fraud detection analysis. The aim is to deploy existing detection methods of the literature for extensive evaluations to highlight their limitations and allow to propose more accurate and rapid-evolving detection approaches.
• Release Contributions:
  Deployment and configuration of 02 of the 03 most popular SIMBox architectures
• URL:
  https://­gitlab.­inria.­fr/­akouamdj/­simboxfraud_detection
• Contact:
  Anne Josiane Kouam Djuigne
• Partner:
  ENS Lyon

8.6.1 MICN: a Network Coding Protocol for ICN with Multiple Distinct Interests per Generation

In Information-Centric Networking (ICN), consumers send interest packets to the network and receive data packets as a response to their request without taking care of the producers, which have provided the content, contrary to conventional IP networks. ICN supports the use of multiple paths; however, with multiple consumers and producers, coordination among the nodes is required to efficiently use the network resources. Network coding (NC) is a promising tool to address this issue. The challenge in the case of NC is to be able to get independent coded content in response to multiple parallel interests by one or several consumers. In this work, we propose a novel construction called MILIC (Multiple Interests for Linearly Independent Contents) that impose constraints on how the replies to interests are coded, intending to get linearly independent contents in response to multiple interests. Several protocol variants, called MICN (MILIC-ICN), built on top of NDN (Named Data Networking), are proposed to integrate these interest constraints and NC of data packets. Numerical analysis and simulations illustrate that the MILIC construction performs well and that the MICN protocols are close to optimal throughput in some scenarios. MICN protocols compare favorably to existing protocols and show significant benefits when considering the total number of transmitted packets in the network, and in the case of high link loss rate

This paper was published in Computer Networks 5.

8.6.2 MICN: Improving Forwarding Techniques

In 5, we presented the novel protocol MICN that integrates network coding and NDN. It enables NDN to take advantage of network coding and allows fast retrieval of content. MICN enables NDN to overcome the challenges of network coding integration by proposing an Interest naming scheme ensuring retrieval of linearly independent content with each Interest.

In follow-up work, alternative approaches to reduce the number of these lingering Interests and consequently the redundant traffic. The alternative approaches are modified Interest forwarding strategies implemented at the clients or the intermediate nodes in the network. These solutions rely on heuristics. A detailed mechanism and forwarding algorithm for each modified forwarding strategy is studied, and the obtained results are compared to the classical MICN (without IC).

The ideas and the results are presented in the Ph.D. Thesis of Hirah Malik “Efficient Network Coding Protocols for Information-Centric Networks”, who defended in Nov. 2021.

8.7.1 Design of Coded Slotted ALOHA with Interference Cancellation Errors

Participants: Iman Hmedoush [Inria, EVA], Cédric Adjih [Inria, EVA], Paul Mühlethaler [Inria, EVA], Chung Shue Chen [Nokia Bell Labs], Charles Dumas [Nokia Bell Labs], Lou Salaun [Nokia Bell Labs].

In this work, we explored a more complex variant than IRSA, Coded Slotted ALOHA (CSA).

Coded Slotted ALOHA (CSA) is a random access scheme based on the application of packet erasure correcting codes to transmitted packets and the use of successive interference cancellation at the receiver. CSA has been widely studied, and a common assumption is that interference cancellation can always be applied perfectly. In this paper, we study the design of the CSA protocol, accounting for a non-zero probability of error due to imperfect interference cancellation (IC). A classical method to evaluate the performance of such protocols is density evolution, originating from coding theory, and that we adapt to our assumptions. Analyzing the convergence of density evolution in asymptotic conditions, we derive the optimal parameters of CSA, i.e., the set of code selection probabilities of users that maximizes the channel load. A new parameter is introduced to model the packet loss rate of the system, which is non-zero due to potential IC errors. Multi-packet reception (MPR) and the performance of 2-MPR are also studied. We investigate the trade-off between optimal load and packet loss rate, which sheds light on new optimal distributions that outperform known ones. Finally, we show that our asymptotic analytical results are consistent with simulations obtained on a finite number of slots.

This work was published in the journal IEEE TVT 1.

8.7.2 A Game Theoretic Approach to Irregular Repetition Slotted Aloha

Participants: Iman Hmedoush [UVSQ], Cédric Adjih [UVSQ], Paul Mühlethaler [UVSQ], Kinda Khawam [UVSQ].

IoT networks largely rely on distributed access of billions of devices but are still lagging in terms of combined reliability and low latency. To mend that shortcoming, it is paramount to adapt existing random access methods for the IoT setting. In this line of work, we shed light on one of the modern candidates for random access protocols fitted for IoT: the “Irregular Repetition Slotted ALOHA” (IRSA). As self-managing solutions are needed to overcome the challenges of IoT, we study the IRSA random access scheme in a distributed setting where groups of users, with fixed traffic loads, are competing for ALOHA-type channel access. To that aim, we adopt a distributed game-theoretic approach where two classes of IoT devices learn autonomously their optimal IRSA protocol parameters to optimize selfishly their own effective throughput. Through extensive simulations, we assess the notable efficiency of the game-based distributed approach. We also show that our IRSA game attains the Nash equilibrium (NE) via the ”better reply” strategy, and we quantify the price of anarchy in comparison with a centralized approach. Our results imply that user competition does not fundamentally impact the performance of the IRSA protocol.

This work was published in the journal IEEE Access 3.

8.8.1 Deep-IRSA: A Deep Reinforcement Learning Approach to Irregular Repetition Slotted ALOHA

Participants: Ibrahim Ayoub [UVSQ], Iman Hmedoush [UVSQ], Cédric Adjih [UVSQ], Kinda Khawam [UVSQ], Samer Lahoud [ESIB].

Irregular Repetition Slotted Aloha (IRSA) is a Random Access (RA) protocol that meets requirements for IoT networks in terms of efficiency. In this line of work, we study IRSA and introduce a variant of the protocol which allows collided users to perform a limited number of re-transmissions. Users can also be divided into classes of service which allows prioritizing a class of users. Using simulations and Deep Reinforcement Learning (DRL), we optimize the protocol and its parameters and thus propose Deep-IRSA. We can observe the learned degree distribution and the performance for various network scenarios and parameters. The observed performance in terms of throughput is excellent. Another important outcome is that the proposed method is generic and can be applied to many variants of IRSA. In addition, our method is a very good alternative to known approaches (such as density evolution) for smaller frame sizes and more complex variants.

This work was presented at PEMWN 2021 11.

8.8.2 Reinforcement Learning for MICN

Participants: Hirah Malik [U. Paris-Saclay, L2S, CNRS], Cédric Adjih [U. Paris-Saclay, L2S, CNRS], Michel Kieffer [U. Paris-Saclay, L2S, CNRS], Claudio Weidmann [ENSEA].

The heuristic-based forwarding strategies that improve network resources utilization in MICN face some shortcomings. For example, the parameters are hardcoded and require tuning for changes in the network state, e.g., in the presence of losses. Our aim in this work is to explore adaptive forwarding solutions based on reinforcement learning algorithms.

After some experimentation with Q-Learning, we adopted Policy Gradient methods. We first implemented the REINFORCE algorithm with and without the baseline, which also correspond to a Contextual Gradient Bandit. We further explored Deep Reinforcement Learning (DRL) approaches, and more specifically, we used The Proximal Policy Optimization algorithm (PPO).

The results indicate that RL algorithms can provide good solutions to implement an adaptive forwarding strategy. Compared to REINFORCE, DRL (PPO) can use a large set of observations and tends to converge faster toward the optimum behavior. The stochastic policy ensures that the main goals of MICN are achieved while reducing the side effect of redundant traffic observed with MICN and other NC algorithms. These results provide a good base for further exploring the integration of RL techniques in MICN to improve its performance.

The ideas and the results are presented the in the PhD Thesis of Hirah Malik “Efficient Network Coding Protocols for Information-Centric Networks”, who defended in Nov. 2021.

8.11.1 Increasing vehicles perception using cooperative relaying and priority-based beaconing [Axis 2]

Participants: Faiz Sanaullah [University Sorbonne Paris Nord], Nadjib Achir [University Sorbonne Paris Nord], Khaled Boussetta [University Sorbonne Paris Nord].

The use of vehicle onboard sensors in increasing the perception of cooperative vehicles is becoming prevailing and essential for road safety. However, trusting only on the onboard sensors could limit the perception of the cooperative vehicles; for example, an object/vehicle can not be detected if some obstruction impedes it. The perception efficiency of vehicles can be increased beyond their field of view (FoV) by exchanging sensor-detected information through V2X communication. The perception of cars can be more accurate as frequently they share the information. However, transmitting numerous messages could saturate the communication channel and deteriorate the perception improvement, especially in dense circumstances.

In this contribution, we address both these issues and propose two different schemes, (i) an information propagating mechanism to increase the cooperative vehicle's perception above their FoV and (ii) an optimal dynamic cooperative priority-based beaconing model to coup with the channel congestion. Using the information propagation mechanism, we can increase the perception of cooperative vehicles up to their maximum communication range. On the other end, the probability-based dynamic cooperative priority beaconing scheme selects the most suitable cooperative vehicle to transmit based on its onboard sensor facility. These proposed mechanisms highly decrease the channel's contention while extending the perception beyond the FoV. We evaluate both the mechanisms for the performance in terms of vehicles awareness, channel load, and communication range through simulations for several vehicle densities. The obtained simulation results show that these proposals provide better performance compared to the classical beaconing schemes as well as eCAM.

This work has been published in 13.

8.11.2 Revealing user-mobility bounds from WiFi probe requests

Participants: Abhishek Kumar Mishra, Aline Carneiro Viana, Nadjib Achir.

Human mobility is challenging to infer, reconstruct, or predict precisely and even further through a more privacy-preserving and scalable manner. Domains and applications are: targeted advertising, epidemic prevention, urban, transportation, or touristic planning, to cite a few. Current GPS-based localization methods are considered sparse in space and time, and RSSI-based passive sniffing methods are challenging due to miscellaneous error sources. Recent literature has shown the large and highly volatile errors in human-location estimation when using observed RSSI from passive sniffing over Wireless packets.

In this proposal, we show, for the first time in literature, that using a theoretical error-formulation applied over real-world experimental datasets, the measurement-induced errors can be estimated as a distribution over short periods and used as allies to estimate trajectories bounds with high confidence as we can see in the following figure. We filter and process RSSI of sniffed WiFi probe request packets, formalize an optimization error estimation problem and finally solve it using multilateration over various samples from the estimated error distribution to obtain human-mobility bounds. In a series of simulations and analyses, we show that the proposed bounds are realistic and accurate. Furthermore, we demonstrate the accuracy and the variation of bounds according to sniffer and user-device densities wireless channel conditions.

This work is related to ANR MITIK project (2020-). We are currently finalizing a paper that should be submitted in the next few weeks.

8.11.3 How mobile can a Mobile Edge Computing be?

Participants: Pedro Cruz Caminha, Aline Carneiro Viana, Nadjib Achir.

The most relevant aspect of Multi-Access Edge Computing (MEC) is to provide computing resources close to the User Equipment (UE). An allocation strategy is then required to define which MEC host should serve each application running in the UEs. However, one of the direct consequences of allocating resources at the Edge is their exposure and sensitivity to the future locations due to the mobility of the UEs, which leads to non-optimal usage in the long run. It is, therefore, essential to integrate mobility anticipation into the allocation strategy. Indeed, in a scenario with high user mobility, the distance (in terms of delay) between a given UE and each MEC host is dynamic, creating a risk to MEC's primary aspect, which is to fulfill the application's computation requirements while guaranteeing its QoS constraints. It is then essential to develop mobility-aware allocation strategies to mitigate this problem and anticipate user demands.

This issue is amplified by the lack of tools available to emulate MEC systems and evaluate the mobility impact and possible mitigation strategies. To tackle this issue, we start an intensive state of the art on the existing platforms tools, we prepared and submitted in 2021 a survey on current MEC platform implementations initiatives. We survey these practical initiatives according to their development strategies, including the ETSI MEC standard compliance, limitations, and impact. We also survey the existing tools making MEC systems a reality. Finally, we give hints to MEC issues yet to be addressed in practice. This work will help researchers and developers design their own MEC systems or improve and simplify the usability of existing ones. We are currently working on the article's revised version, which we will resubmit soon.

We then propose Pythia (see figure below), a MEC system emulator that receives (i) users' mobility information (UE mobility traces), (ii) MEC infrastructure specifications, (iii) a pair containing a MEC and a UE application, and (iv) an allocation strategy, outputting important metrics of user experience. In such a way, Pythia extracts and models properties of short-term mobility behavior of users to anticipate future locations in users' mobility. Such information is then leveraged at the MEC allocation of resources. Pythia emulates the network from the point-of-view of the links between the MEC hosts and the UEs while executing the MEC and UE applications in a virtual environment. We also started implementing the v0 version of the experimentation platform on Grid'5000. The adopted approach follows the ETSI recommendations.

————

8.11.4 Energy Efficient adaptive sampling frequency of human mobility

Participants: Panagiota Katsikouli, Aline Carneiro Viana, Marco Fiore, Diego Madariaga.

In recent years, mobile device tracking technologies based on various positioning systems have made location data collection a ubiquitous practice. Applications running on smartphones record location samples at different frequencies for varied purposes. The frequency at which location samples are recorded is usually pre-defined and fixed but can differ across applications; this naturally results in big location datasets of various resolutions. Moreover, continuous recording of locations usually results in redundant information, as humans tend to spend a significant amount of their time either static or in routine trips, and drains the battery of the recording device.

In this work, we aim at answering the question “at what frequency should one sample individual human movements so that they can be reconstructed from the collected samples with minimum loss of information?”.

Our analyses on fine-grained GPS trajectories from users around the world unveil (i) seemingly universal spectral properties of human mobility, and (ii) a linear scaling law of the localization error with respect to the sampling interval. Our analysis of fine-grained trajectories unveils a novel linear scaling law of the localization error with respect to the sampling interval. Such results were publishedat IEEE Globecom 2017  43.

Building on these results, we challenge the idea of a fixed sampling frequency and present a lightweight, energy-efficient, mobility aware adaptive location sampling mechanism. We thus present DUCTILOC, a location sampling mechanism that takes advantage of the law above to profile users and then adapt the position tracking frequency to their mobility. Our design is energy efficient, as DUCTILOC does not rely on power-hungry sensors or expensive computations; moreover, it provides a handy knob to control energy usage, by configuring the target positioning accuracy. Real-world experiments with an Android implementation of DUCTILOC show that it can effectively adjust the sampling frequency to the mobility habits of each individual and target accuracy level, reducing the energy consumption by 60% to 98% with respect to a baseline periodic sampling.

This work is under submission to the IEEE Trans. on Mobile Computing (TMC) journal and also registered at Hal Inria as the Research Report 36. It represents the final contribution of the collaboration with Marco Fiore, Panagiota Katsikouli, who spent 5 months in our team working as an internship in 2017, and Diego Madariaga who spent 3 months in 2018 in our team working as an internship).

8.11.5 Data offloading decision via mobile crowdsensing

Participants: Emanuel Lima [U. of Porto], Aline Carneiro Viana [U. of Porto], Ana Aguiar [U. of Porto], Paulo Carvalho [Univ. Do Minho].

It is known that mobile network operators are struggling to keep up with such traffic demand, and part of the solution is to offload communications to WiFi networks. Mobile data offloading systems can assist mobile devices in the decision making of when and what to offload to WiFi networks. However, due to the limited coverage of a WiFi AP, the expected offloading performance of such a system is linked with the users mobility.

Several studies on the analysis of human mobility patterns have been carried out focusing on the identification and characterization of important locations in users' life in general. We extended these works by studying human mobility from the perspective of mobile data offloading. In our first study, We define Offloading Regions (ORs) as areas where a commuter's mobility would enable offloading, and propose an unsupervised learning method to extract ORs from mobility traces.

Next, we leverage human mobility to inform offloading tasks, taking a data based approach leveraging granular mobility datasets from two cities: Porto and Beijing. We evaluate the offloading opportunities (ORs) provided to users while they are travelling in terms of availability, time window 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 method. Moreover, results also 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. This work was published at ACM CHANTS 2018 (jointly with ACM MobiCom)  45. Next we extended the above work as following.

We then assess the mobility predictability in an offloading scenario using theoretical and algorithmic evaluation of several mobility predictors. The results show that mobility predictability for offloading purposes is far more challenging than mobility between PoIs. Here, machine learning (ML) predictors outperform common Markov Chain (MC) predictors used in the literature by at least 15%, revealing the importance of context information in an offloading scenario. 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.

This last extended work is under submission at the IEEE Trans. on Mobile Computing (TMC) journal.

This is an on-going work with the PhD Emanuel Lima, a collaboration started when he spent 4 months in 2018 as an intern in our previous team, and his advisors.

8.11.6 Identifying how places impact each other by means of user mobility

Participants: Lucas Santos de Oliveira [EMBRACE], Pedro Olmo Stancioli [Federal U. of Minas Gerais], Aline Carneiro Viana.

The way in which city neighborhoods become popular and how people trajectory impacts the number of visitation is a fundamental area of study in traditional urban studies literature. Many works address this problem by means of user mobility prediction and PoI recommendation.

Different from previous literature, in this work, we are interested in understanding how the neighborhood PoI affect each other by means of human mobility using location-based social networks (LBSNs) data source. In this context, we propose a methodology to quantify the power of point-of-interests (PoIs) in their vicinity, in terms of impact and independence – the first work in the literature (to the best of our knowledge). We modeled POI's visits using the multiflow graph model where each POI is a node and the transitions of users among PoIs are a weighted direct edge.

Using this multiflow graph model, we compute the attract (i.e., the capacity of a PoI to receive people from its vicinity), support (i.e., the capacity of a PoI to disseminate people over its vicinity), and independence (i.e., the potential resilience a PoI has to other PoIs moving out (closing their doors) from its neighborhood) powers of PoIs. Our findings show that in University campus: (i) buildings have low support and attract; (ii) people tend to move over a few buildings and spend most of their time in the same building; and (iii) there is a slight dependence among buildings, even those with high independence receive user visits from other buildings on campus. Globally, we reveal that: (i) our metrics capture places that impact the number of visits in their neighborhood; (ii) cities in the same continent have similar independence patterns; and (iii) places with a high number of visitation and city central areas are the regions with the highest degree of independence.

This work was first published in ACM MobiWac   49 and its extended version published at ACM Transactions on Knowledge Discovery from Data (TKDD) journal 6, also registered at Hal Inria as the Research Report 32. Lucas did an internship in our team from Nov. 2019 to Jan. 2020. He is currently working on the SafeCityMap Inria mission COVID project.

8.11.7 Tactful opportunistic forwarding: What human routines and cooperation can improve?

Participants: Rafael Lima da Costa [CAPES], Aline Carneiro Viana [Federal U. of Bahia], Leobino Sampaio [Federal U. of Bahia], Artur Ziviani [LNCC].

According to literature, one of the 10 key enabling technologies for 5G is device-to-device (D2D) communications, an approach based on direct user involvement. User-centric communication arose as an alternative to increase capillarity and to offload data traffic in cellular networks through opportunistic connections among users. Although having the user as main concern, solutions in the user-centric communication/networking area still do not see the user as an individual, but as a network active element.

In this work, we first introduce the Tactful Networking paradigm, whose goal is to add perceptive senses to the network, by assigning it with human-like capabilities of observation, interpretation, and reaction to daily-life features and involved entities. To achieve this, knowledge extracted from human inherent behavior (routines, personality, interactions, preferences, among others) is leveraged, empowering user-needs learning and prediction to improve QoE while respecting privacy. We survey the area, propose a framework for enhancing human raw data to assist networking solutions and discuss the tactful networking impact through representative examples. This tutorial paper was published at the IEEE Transaction on Emerging Topics in Computational Intelligence in 2020  42.

Then, we study “What type of knowledge can we extract from human mobility routines behavior to leverage opportunistic communication services or protocols?”. The extracted metrics represent different types of knowledge extracted from people routine present in their movements. Because of the strong routine component of human mobility, such metrics capture different but recurrent behaviors on wireless encounters between mobile users. The results show heterogeneity in metric coefficients and contact occurrence and duration in different periods of the day, highlighting the need for characterizing traces before their use. This preliminary study was published at the UrbComp 2020 workshop (jointly with DCOSS)  46.

There is a lack of initiatives beyond traditional techniques or limited human-mobility features to identify routines (spatiotemporal patterns), related consequences (e.g., wireless encounters), and movement decisions (e.g., motion direction) with more granularity and precision. We tackle such limitations by combining the metrics extracted (our previous work) from a real-world and a synthetic dataset and applying them into a novel Tactful Opportunistic cOmmunicaTion Strategy (TOOTS). This strategy features a dissemination policy and a forwarding algorithm. In the former, users' spatiotemporal properties and induced wireless encounters are leveraged to choose content-disseminating nodes that have shown previous encounter routine with destination nodes. The forwarding algorithm relies on nodes' popularity, displacement, network-cell (as PoIs) visiting and proximity, and displacement direction. TOOTS reaches 100% delivery rate with respectively 28%, and 73% less delivery latency, and with 16%, and 27% less overhead in the real-world and synthetic datasets.

This work was published at the AINA 2021 conference 18 and an extended version is under the 2nd round of review at the Computer Communication journal. Rafael Lima da Costa did a joint PhD between the Federal Univ. of Bahia and Inria/IPP, under my co-supervision. He defended in July 2021 and his manuscript is available in 25.

8.11.8 Deciphering Predictability Limits in Human Mobility

Participants: Douglas do Couto Teixiera [Federal U. of Minas Gerais], Aline Carneiro Viana [Federal U. of Minas Gerais], Jussara Almeida [Federal U. of Minas Gerais], Mario S. Alvim [Federal U. of Minas Gerais].

Measuring the predictability of human mobility is a hard task due to the uncertain and heterogeneous behavior of humans, as well as to the variability of parameters influencing such behavior. Besides, literature on mobility predictability is based on a sophisticated compression algorithm whose output bears little resemblance to its input, making it hard to understand what makes one's mobility more or less predictable. If better interpretable, we believe an accurate predictability with short-history of movements can be computed. Because of such compression strategy, they lack extensibility in considering contextual information (e.g., hours of the day, weather, friends' mobility). As contextual information is expected to grow, thanks to the popularity of IoT devices and on-line social networks, it is paramount to find ways to add such information in predictability estimates.

In this research activity, we first establish the theoretical foundations for the computation of interpretable and extensible predictability of mobility. We propose a new measure, regularity, which together with stationarity, helps us understand what makes a person's mobility trajectory more or less predictable. Those metrics are complementary and jointly are able to explain most of the variation in the main state-of-art predictability approach. Additionally, we investigate strategies to incorporate different types of contextual information into predictability estimates, and show that the benefits vary depending on the underlying prediction task. Our results show that, for the next place prediction problem, the use of contextual information plays a larger role than one's history of visited locations in estimating their predictability.

This work was published at ACM SIGSPATIAL 2019, a A+-ranked conference in our domain, and was indicated as a top-six best paper candidate. There is no study in the mobility condition that ensure a general computation of a context-enhanced predictability, as we proposes to do.

We then extend the SigSpatial published work by revisiting this state-of-the-art method, aiming at tackling interpretability and extensibility limitations of predictability. We introduce a novel strategy that seamlessly incorporates contextual information into the main state-of-the-art predictability technique. A broad evaluation and discussion of the benefits of contextual information on several entropy estimators, for both next-cell and next-place prediction. Our results show that introducing context information can indeed improve predictability estimate, when simpler, alternative entropy estimators are employed. However, results also hint at the observation that using context may not always bring benefits to predictability estimate in practical scenarios. This work extension was published at the ACM Transactions on Spatial Algorithms and Systems (TSAS) journal 9, following an invitation of the Editor in Chief resulting from the top-six best paper selection at ACM SigSpatial. A related report can be found in Hal Inria 9.

Going forward, we propose to view human mobility as consisting of two components, routine and novelty, with distinct properties. We propose a technique that allows us to (i) quantify the effect of novelty on predictability, and (ii) gauge how much one's routine deviates from a reference routine that is completely predictable, therefore estimating the amount of unpredictable behavior in one's routine. Our experiments show that routine behavior can be largely explained by three types of patterns: (i) stationary patterns, in which a person stays in her current location for a given time period, (ii) regular visits, in which people visit a few preferred locations with occasional visits to other places, and (iii) diversity of trajectories, in which people change the order in which they visit certain locations.

This work extension was published at EPJ Data Science journal 8, a related report can be found as well in Hal Inria 38.

Douglas Teixeira did a joint PhD between the Federal Univ. of Minas Gerais and Inria/Polythecnic School-IPP, under my co-supervision. He defended in July 2021 and his manuscript is in  52 26.

8.11.9 Identifying and profiling novelty-seeking behavior in human mobility

Participants: Licia Amichi [Boston Univ.], Aline Carneiro Viana [Boston Univ.], Mark Corvella [Boston Univ.], Antonio F. Loureiro [Federal U. of Minas Gerais].

Individuals' mobility prediction models are unable to capture uncertainties in the mobility behavior of individuals, and consequently, suffer from the inability to predict visits to new places. This is due to the fact that current models are oblivious to the exploration aspect of human behavior. We focus on such phenomenon, which has rarely been tackled in the literature but indeed, represents a real issue. We first work on the understanding of the exploration phenomenon and answer the following question: What type of visits characterize the mobility of individuals?. We show that a two-dimensional modeling of human mobility, which explicitly captures both regular and exploratory behaviors, yields a powerful characterization of users. Using such model, we identify the existence of three distinct mobility profiles with regard to the exploration phenomenon – Scouters (i.e., extreme explorers), Routiners (i.e., extreme returners), and Regulars (i.e., without extreme behavior). Our results unveil important novelty preferences of people, which are ignored by literature prediction models. We then discuss how our profiling methodology could be leveraged to improve prediction.

This first work was published at the Student workshop of ACM CONEXT 2019 and in the French Algotel conference 2020 41. An extended version was then published at the ACM SigSpatial 2020  40. A journal under submission to the IEEE Transactions on Emerging Topics in Computing, where we evaluate the impacts of novelty-seeking and the prediction task formulation on the theoretical and practical predictability extents. A technical report on this last work is available at Hal Inria 27, 28.

Next, we claim the tackling of such limitation first requires identifying the purpose of the next probable movement. In this context, we propose a novel framework for adjusting prediction resolution when probable explorations are going to happen. In fact, the geographical occurrences of explorations are far from being random in a coarser-grained spatial resolution. Exploiting these properties, instead of directly predicting a user's next location, we design a two-step predictive framework. First, we predict the mobility purpose of an individual: (i) a return, i.e., a visit to a previously known location, or (ii) an exploration, i.e., a discovery of a new place. Next, we predict the next location or the next coarse-grained zone depending on the inferred type of movement. The results demonstrate substantial improvements in the accuracy of prediction by dint of fruitfully forecasting coarse-grained zones used for exploration activities.

This work was published at the ACM SigSpatial 2021 10.

Licia Amichi did a PhD under my supervision. She defended in November 2021 and the final version of her manuscript will be on-line from Feb. 2022.

8.12.1 Public Wireless Packets Anonymously Hurt You

Participants: Abhishek Kumar Mishra, Aline Carneiro Viana, Nadjib Achir, Catuscia Palamidessi.

With growing privacy concerns over the last decade, two of the most notable wireless technologies – i.e., BLE and WiFi – are being investigated more in terms of privacy vulnerabilities. To this regard, steps were taken by the standard, notably MAC address randomization, to address these concerns. MAC address randomization can hide the device's identity to some extent. This feature has been the backbone of user privacy in wireless networks, especially BLE and WiFi. However, most of the existing related works suggest flaws in randomization or suggest inferring insights from the transmitted public data in the packets. Unfortunately, such solutions are not feasible due to resource constraints. To the best of our knowledge, none of current work give a global view on the privacy issues in the design of public wireless packets itself. Current works do not look into counter-measures of timing-based attacks, which are more generic and effective than we would see in the upcoming sections.

The novelty of this contribution lies in the investigation of the root causes of growing privacy concerns in public wireless packets. The key contributions are 1) Classification of current attacks in the literature based on methodology 2) Revealing key design flaws in current WiFi and BLE public packets 3) Solutions and recommendations to rectify the flaws we detect in the design.

This work is related to ANR MITIK project (2020-), described in its ANR annual report and also published in LCN 2021 17.

8.12.2 SimBle: a tool to generating privacy preserving real-world BLE traces with ground truth

Participants: Abhishek Kumar Mishra, Aline Carneiro Viana, Nadjib Achir.

Bluetooth has become critical as many IoT devices are arriving in the market. Most of the current literature focusing on Bluetooth simulation concentrates on the network protocols' performances and completely neglects the privacy protection recommendations introduced in the BLE standard. Indeed, privacy protection is one of the main issues handled in the Bluetooth standard. For instance, the current standard forces devices to change the identifier they embed within the public and private packets, known as MAC address randomization. Although randomizing MAC addresses is intended to preserve device privacy, recent literature shows many challenges that are still present. One of them is the correlation between the public packets and the emitters. Unfortunately, existing evaluation tools such as NS-3 are not designed to reproduce this Bluetooth standard's essential functionality. This makes it impossible to test solutions for different device-fingerprinting strategies as there is a lack of ground truth for large-scale scenarios with the majority of current BLE devices implementing MAC address randomization. In this paper, we first introduce a solution of standard-compliant MAC address randomization in the NS-3 framework, capable of emulating any real BLE device in the simulation and generating real-world Bluetooth traces. In addition, since the simulation run-time for trace-collection grows exponentially with the number of devices, we introduce an optimization to linearize public-packet sniffing. This made the large-scale trace collection practically feasible. Then, we use the generated traces and associated ground truth to do a case study on the evaluation of a generic MAC address association available in the literature 16. Our case study reveals that close to $90\%$ of randomized addresses could be correctly linked even in highly dense and mobile scenarios. This prompts the BLE standard to be revisited on privacy-related provisions. We provide privacy recommendations based on our case study. Finally, we discuss the consequences that real randomized traces bring to different scientific research domains and how our proposed solution helps in overcoming new challenges.

This work is related to ANR MITIK project (2020-) and a preprint work is availalbe at HAL-inria 37 and a paper was published in 19.

8.12.3 Detecting and simulating bypass SIMBox frauds in cellular networks

Participants: Anne Josiane Kouam, Aline Carneiro Viana, Alain Tchana.

Due to their complexity and opaqueness, cellular networks have been subject to numerous attacks over the past few decades. These attacks are  a real problem to telecom operators and cost them about USD 28.3 Billion annually, as reported by the Communications Fraud Control Association. SIMBox fraud, which is one of the most prevalent of these telephone frauds, is the main focus of this work. SIMBox fraud consists of diverting international calls on the VoIP network and terminating them as local calls using an off-the-shelf device, referred to as SIMBox.

In this work, we first survey both the existing literature and the major SIMBox manufacturers to provide comprehensive and analytical knowledge on SIMBox fraud, fraud strategies, fraud evolution, and fraud detection methods. We provide the necessary background on the telephone ecosystem while extensively exploring the SIMBox architecture required to understand fraud strategies. We provide a complete introductory guide for research on SIMBox fraud and stimulate interest for SIMBox fraud detection, which remains little investigated. We also present insights into tomorrow's SIMBox fraud detection challenges. This survey is published at the IEEE Communication and Tutorial Surveys journal and a technical report can be found at Hal Inria under the reference: hal-03105845 4.

Currently, we are working on the SIMBoxFraudSimulator, an open-source simulator of SIMBox fraud strategies and detection methods in LTE networks. It is based on the well-known and broadly used LTE-SIM tool in which we added all the required components to simulate SIMBox fraud. Besides, we inserted various traffic generators and realistic mobility modeling, providing lifelike CDR data and ground-truth for comprehensive fraud detection analysis. The aim is to deploy existing detection methods of the literature for extensive evaluations to highlight their limitations and allow to propose more accurate and rapid-evolving detection approaches. This simulator is mentioned in the Software section and can be found at Inria GitLab.

Nokia (ADR): Network Information Theory

Participants: Cedric Adjih, Iman Hmedoush.

Through the common Inria-Nokia laboratory, the team is involved in the action "Network Information Theory" (ADR, "Action De Recherche"). In collaboration with Nokia, and Inria EPI MARACAS, and EPI EVA, we are working on the subject of optimization and evaluating communications for IoT networks. This includes 5G and beyond, medium-access level/random access techniques protocols, modern random access and applying machine learning techniques to wireless communications.

10.1.1 Inria associate team not involved in an IIL or an international program

10.1.2 Participation in other International Programs

10.2.1 Visits of international scientists

10.2.2 Visits to international teams

10.3.1 FP7 & H2020 projects

10.3.2 Other european programs/initiatives

Equipex FIT:

Participants: Cedric Adjih [Inria, SED], Alexandre Abadie [Inria, SED], Emmanuel Baccelli.

• Partners: Sorbonne Université, Inria (Lille, Sophia-Antipolis, Grenoble), INSA, Institut Telecom Paris, Institut Télécom Evry, LSIIT Strasbourg.

• Abstract: FIT (Future Internet of Things) aims to develop an experimental facility, a federated and competitive infrastructure with international visibility and a broad panel of customers. It provides this facility with a set of complementary components that enable experimentation on innovative services for academic and industrial users. The project gives french internet stakeholders a means to experiment on mobile wireless communications at the network and application layers thereby accelerating the design of advanced networking technologies for the future internet. FIT was one of 52 winning projects from the first wave of the French Ministry of Higher Education and Research's "Équipements d'Excellence" (Equipex) research grant program, in 2011.

  One component of the FIT platform is the sets of IoT-LAB testbeds (see the IoT-LAB web site). These were motivated by the observation that the world is moving towards an “Internet of Things”, in which most communication over networks will be between objects rather than people.

Project 5G-mMTC:

Participants: Cedric Adjih [Inria, SED], Alexandre Abadie [Inria, SED], Nadjib Achir, Emmanuel Baccelli.

• Funding instrument: AAP - Plan de relance « Souveraineté dans les réseaux de télécommunications afin d'accélérer les applications de la 5G » (France Relance)
• Project acronym: 5G-mMTC
• Duration: 2021–2024
• Partners: Amarisoft, EDF R&D, Fédération francaise de cyclisme, Inria Saclay, Institut Mines Telecom, IS2T, Sequans communications, Sparkling Tech, Université de Versailles, Webdyn
• Abstract: The 5G-mMTC project aims to provide software and hardware tools for the rapid implementation of a 5G solutions for the IoT. Two use cases will be implemented directly within the framework of this project: one developed in conjunction with the French Cycling Federation (FFC), which will enable real-time analysis of athletes' data and their performances; the other will be worked on jointly with EDF, to enable real-time management of the entire fleet of existing heterogeneous sensors

10.4.1 ANR

VehiCloud

Participants: Andrea Araldo [Télécom SudParis - SAMOVAR], Lila Boukhatem [LISN Univ. Paris Saclay], Nadjib Achir, Aline Carneiro Viana.

• Funding instrument/scientific committee: DigiCosme
• Project acronym: VehiCloud
• Project title: How can Vehicles increase Cloud intelligence?
• Duration: 2021–2024
• Coordinator: Andrea Araldo
• Other partners: Télécom SudParis - SAMOVAR, LISN Univ. Paris Saclay.
• Abstract: The objective of this PhD project is to answer to the questions: What is Vehicular Cloud (VCC) good for? And under which conditions?. We propose to evaluate the benefits of integrating VCC into future network architectures, together with CC (Cloud Computing) and EC (Edge Computing). We translate these high level questions into concrete operational objectives as follows. We assume mobile users generate tasks of different traffic classes, defined in terms of latency constraints (high vs. low responsiveness) and bandwidth requirements (data-intensive vs. data-light). The key challenges that we need to tackle are: (i) Extremely large scale of the Computation Ecosystem (CoE) and heterogeneity of its computation nodes, in terms of mobility, connectivity and amount of computation resources. (ii) The CoE is highly dynamic and stochastic: resources available and quality of connections change over time and cannot be exactly known. (iii)Applications diversity, in terms characteristics, structure and constraints.

Mobility-enhanced MEC

Participants: Nadjib Achir, Aline Carneiro Viana.

• Funding instrument/scientific committee: Inria ADT
• Project title: How mobile can a Mobile Edge Computing be?
• Duration: 2020-2022
• Coordinator: Aline Carneiro Viana
• Other partners: Federal Univeristy of Rio de Janeiro (UFRJ), Brazil
• Abstract: Distributed and in-network computation at the edges is considered a key factor to reduce energy consumption and the long and fluctuating access delay incurred when using far away clouds. A direct consequence of this computation at the edge is their exposure and sensitivity to user mobility, which may render the location of the used edge computation non-optimal in the long run : Implying extra latency and energy consumption. According to such considerations, our main objective is to set up an experimental platform in the Grid'5000 cloud computing facilities, in which it is possible to evaluate the impact of the mobility on the energetic loss and latency increase/variation when task offloading to a Mobile Edge Computing (MEC) system is considered. This platform should follow the recommendations of the ETSI MEC (Multi-Access Edge Computing) standards to guarantee the proper dissemination and adoption of the proposed platform by the research community. Finally, to be able to support large-scale scenarios, we aim to deploy this platform as part of the Grid’5000 cloud computing facilities, which allow us to take advantage of the Grid’5000 monitoring tools for resource usage and energy consumption.

11.1.1 Scientific events: organisation

• Emmanuel Baccelli and Koen Zandberg co-organized the SUIT hackathon at IETF 112, in November 2021.

11.1.2 Scientific events: selection

• Cédric Adjih: TPC co-chair of PEMWN 2021.

11.1.3 Journal

11.1.4 Invited talks

• Emmanuel Baccelli gave a keynote at the International Conference on Availability, Reliability and Security (ARES 2021) on "Cyber-Secure Building Blocks for Low-Power IoT".
• Aline C. Viana: (1) Keynote: LastMile2021 workshop (Jan/2021). (2) Invited speaker: Talk at Brazilian Computer Society conference (CSBC) joint with 13º Brazilian Symposium of Ubiquitous and Pervasive Computing (SBCUP) (Jun 2021). (3) Invited seminars: for Brazilian IEEE ComSoc researchers representing the Brasilia University (UnB, Brazil) and the Center-Nord region in Brazil (Jul 2021); and two seminars for master and PhD students of PUC-MG (Brazil), May 2021.

11.1.5 Research administration

• Aline C. Viana: (1) the leader of the TRiBE Project-Team of Inria since its creation (Jul.2019); She was the leader of the previous INFINE project-tem for one year 2018-2019; (2) the coordinator of ANR MITIk (since 2020-) and SafeCityMap Inria-Covid mission project (since 2020-);

11.2.1 Teaching

• Master : Emmanuel Baccelli, “IoT & Security”, summer semester seminar (April 2021 - June 2021), Freie Universitaet Berlin.
• Master : Emmanuel Baccelli, “Operating Systems for Small Connected Devices in the Internet of Things”, cours magistral, Formation PESTO Corps des Mines, Telecom ParisTech, Paris France. Sept. 2021.
• Nadjib Achir is associate professor at the Sorbonne Paris Nord University and do his full service at the university.
• Team members continuously encourage, teach, and mentor students:
  • to perform international short visits;
  • to engage collaborations on related thesis subject;
  • to advise master students during their thesis;
  • to review articles, when possible;
  • to the culture of open-source code, of ethical/privacy issues in data collection, and/or of standardization, according to their thesis subject;
  • to give scientific talks (e.g., project meetings, Working Groups of associated communities, team meetings, etc);
  • to participate to general activities such as Fête de la Science, popularization committee, shadow TPCs, etc
  • for the ones interested in teaching, the good pointers at campus and when to start
  • to find opportunities post-thesis: CV and personal website's advising; facilitating contacts with other Institutions/Researchers; recommendation letters

11.2.2 Supervision

• PhDs supervision
  • PhD, defended on 22 Nov. 2021: Hirah Malik, “Efficient COding of Meta-information in Information-CEntric NEtworks”, since Oct. 2017. Advisors: C. Adjih, Michel Kieffer, and Claudio Weidmann.
  • PhD, defended on 15 Dec. 2021: Célia Tazibt, “ Data collection through UAV”, Advisors: Nadjib Achir and Tounsia Djamah.
  • PhD in progress: Iman Hmedoush, “Connection protocols for the 5G IoT”, since Oct. 2018. Advisors: C. Adjih and Paul Mühlethaler.
  • PhD in progress: Hadi Yakan (UVSQ), “Security of V2X Communications in 5g networks”, since Sep. 2019. Advisors: N. Aitsaadi, I. Fajjari, and C. Adjih.
  • PhD in progress: Najoua Benalya (ENSI), “Agriculture de précision dans l'ère des drones et d'intelligence artificielle”, since Nov. 2021. Advisors: I. Amdouni, A. Laouiti, L. Saidane, C. Adjih.
  • PhD in progress: Khouloud Hwerbi (ENSI), “Optimized Architectures and Algorithms for Blockchain and IoT-based Applications”, since Nov. 2021. Advisors: I. Amdouni, A. Laouiti, L. Saidane, C. Adjih.
  • PhD in progress: Koen Zandberg, “Cybersecure Multi-Tenant Microcontroller-based IoT”, since March 2020. Advisor: Emmanuel Baccelli.
  • PhD in progress: Rosario Patane, “VehiCloud: How can Vehicles increase Cloud intelligence?”, since Dec. 2021. Advisors: Lila Boukhatem, Andrea Araldo, Nadjib Achir.
  • PhD in progress: IPP, Abhishek K. Mishra, “Detection of bypass frauds in cellular network datasets”, since Nov 2019. Advisor: Aline C. Viana and Nadjib Achir.
  • PhD in progress: IPP, Anne Josiane Kouam Djuigne, “Detecting and simulating bypass SIMBox frauds in cellular networks”, since Nov 2019. Advisor: Aline C. Viana and Alain Tchana.
  • PhD in progress: LNCC (Brazil), Haron C. Fantecele , “Mobility modeling and prediction through flow model”, since Feb. 2020. Advisor: Aline C. Viana and Antonio Tadeu (LNCC).
  • PhD defended in Nov. 2021: IPP (Institut Polytechnique de Paris), Licia Amichi, “From movement purpose to perceptive spatial mobility prediction”. Advisor: Aline C. Viana. Collaborators: Mark Crovella (Boston Univ.) and Antonio F. Loureiro (UFMG).
  • PhD defended in Aug. 2021: Jointly IPP and Federal University of Minas Gerais. Douglas Teixeira, “Predictability in Human Mobility: Interpretability, Extensions and Applications”, since April 2018. Advisor: Aline C. Viana and Jussara Almeida (UFMG).
  • PhD defended in Aug. 2021: Jointly IPP and Federal University of Bahia. Rafael Costa, “Tactful Networking as a Cornerstone for Opportunistic Human-Aware D2D Communication”, since May 2017. Advisor: Aline C. Viana and Leobino Sampaio (UFBA).
• Master supervision: the team regularly hosts master students and PhD interns for periods of 3 to 6 months. The list of students/interns concerned by this report year is mentioned in team members list.
• Mentoring program implication: A. C. Viana is a co-coordinator of this program at Inria Saclay, sibling the mentoring of permanent researchers/associate professors and post-docs of the center; P. Jacquet and A. C. Viana act as “researcher mentors” in this program; N. Achir participates as “mentoree” in the same program.

11.2.3 Juries

• Nadjib Achir (Reviewer): PhD defence of Giacomo Quadrio, Delivery of High Quality and Interactive Multimedia Contents over the Internet, University of Padova, jnavier 2021.
• Nadjib Achir (Reviewer): PhD defence of Fouzi Boukhalfa, Low Latency Radio And Visible Light Communications for Autonomous Driving, October 2021.
• Aline C. Viana was a reviewer (rapporteur) for the committees of the following PhD student: Noudéhouénou Lionel J.Houssouv (Univ. de La Rochelle Jul. 2021);
• Aline C. Viana was an examiner for the mi-term committees of the following PhD student: Ludovic Javet (UPSaclay-Univ. de Verasille, Nov. 2021)
• Aline C. Viana was an examiner for the mi-term committees of the following Master student: Marcos Felipe Barboza (Univ. Federal of Goias, July 2021)

11.3.1 Internal or external Inria responsibilities

• Philippe Jacquet has contributed to the organization of the Telecom pole in the EPP research team grouping
• For Aline C. Viana:
  • At Inria:
    • (since 2019) member of the Evaluation Commitee (CE) of Inria. CE exercises the skills that are devolved to it by the staff of Inria. It prepares the work of the Scientific Council by contributing in particular to defining the orientations of the Institute's activities.
    • (since Sep. 2017) Aline C. Viana is a member of BCEP, evaluating Inria teams in process of creation, discussing main issues related to different scientific commissions, discussing changes in the institution.
    • (since Dec. 2018) Aline C. Viana is co-Coordinator of the mentoring program for researchers at Inria Saclay (Inria Saclay mentoring program). The program goal is the coach of junior researchers by more experienced ones in order to provide them with a complementary perspective for their career, independently of any hierarchical link.
    • member of Inria researcher selection and promotion juries committees: CRCN (admission jury); DR0 2020, DR2 2021; DR1 2021; CRHC 2022.
  • At the regional eco-system:
    • (Oct 2020-Oct 2021): Co-chair of the “Distributed Networks’’ subject at the Digicosme COMEX (2020-): Digicosme is an Excellence Laboratory Center in digital science funded by the Ministry of Higher Education and Research as part of Future Investment Program (FIP), and supported by Université Paris-Saclay.
  • Other committees:
    • (2022) Member of the Selection Committee for an Assistant Professor position at the ENS-Lyon'.
    • (2021) Member of the Selection Committee for an Assistant Professor position at the IUT/Université de La Rochelle”.
• Cédric Adjih is member of the "Formation" commission of Digicosme. He is also member of the Hub "Digital Infrastructure and IoT" of Systematic.

11.3.2 Education

• Aline C. Viana: co-chair of Shadow TPC of Algotel/Cores in 2020 and 2021, aiming to train PhD students and Por-Doc fellows to the reviewing process in conferences.

11.3.3 Interventions

• Philippe Jacquet has made a presentation to the CISS workshop organized by John Hopkins university on the limitation of learning in AI.
• Aline C. Viana: General public: Inria Unithé au Café (Jan/2021).
• Worth to highlight: a pupularization talk (on “Qu’est-ce que le métier de chercheur ?”) and a video production (on "Wireless Communications") were performed by the PhD student Anne Josianne Kouam, as member of the scientific popularization team of INRIA Saclay.

International journals

• 1 articleC.Charles Dumas, L.Lou Salaün, I.Iman Hmedoush, C.Cédric Adjih and C.Chung Shue Chen. Design of Coded Slotted ALOHA with Interference Cancellation Errors.IEEE Transactions on Vehicular Technology2021
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• 2 articleV.VINÍCIUS GONÇALVES BRAGA, S. L.Sand Luz CORREA, K.KLEBER VIEIRA CARDOSO and A.Aline Carneiro Viana. Data-Driven Characterization and Modeling of Web Map System Workload.IEEE Access2021
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• 3 articleI.Iman Hmedoush, C.Cedric Adjih, K.Kinda Khawam and P.Paul Mühlethaler. A Game Theoretic Approach to Irregular Repetition Slotted Aloha.IEEE Access10January 2022, 4600-4614
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• 4 articleA. J.Anne Josiane Kouam, A.Aline Carneiro Viana and A.Alain Tchana. SIMBox bypass frauds in cellular networks: Strategies, evolution, detection, and future directions.Communications Surveys and Tutorials, IEEE Communications Society2021
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• 5 articleH.Hirah Malik, C.Cedric Adjih, C.Claudio Weidmann and M.Michel Kieffer. MICN: a Network Coding Protocol for ICN with Multiple Distinct Interests per Generation.Computer Networks1872021, 107816,
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• 6 articleL.Lucas Santos De Oliveira, P.Pedro Olmo Vaz de Melo and A.Aline Carneiro Viana. Assessing Large-Scale Power Relations Among Locations From Mobility Data.ACM Transactions on Knowledge Discovery from Data (TKDD)July 2021
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• 7 articleC. Y.Celia Yasmine Tazibt, N.Nadjib Achir and T.Tounsia Djamah. Online Drone-based Data Gathering Strategies for Ground Sensor Networks.International Journal of Sensor Networks2022
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• 8 articleD.Douglas Teixeira, J.Jussara Almeida and A.Aline Carneiro Viana. On estimating the predictability of human mobility: the role of routine.EPJ Data Science101September 2021
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• 9 articleD. D.Douglas Do Couto Teixeira, A.Aline Carneiro Viana, J. M.Jussara Marques Almeida and M. S.Mário S. Alvim. Revealing challenges in human mobility predictability.ACM Transactions on Spatial Algorithms and SystemsApril 2021
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International peer-reviewed conferences

• 10 inproceedingsL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A. A.Antonio A F Loureiro. From movement purpose to perceptive spatial mobility prediction.ACM SIGSPATIAL 2021Beijing, ChinaNovember 2021
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• 11 inproceedingsI.Ibrahim Ayoub, I.Iman Hmedoush, C.Cedric Adjih, K.Kinda Khawam and S.Samer Lahoud. Deep-IRSA: A Deep Reinforcement Learning Approach to Irregular Repetition Slotted ALOHA.PEMWN 2021 - 10th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired NetworksOttawa / Virtual, CanadaIEEENovember 2021, 1-6
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• 12 inproceedingsR.Roudy Dagher, F.-X.Francois-Xavier Molina, A.Alexandre Abadie, N.Nathalie Mitton and E.Emmanuel Baccelli. An Open Experimental Platform for Ranging, Proximity and Contact Event Tracking using Ultra-Wide-Band and Bluetooth Low-Energy.CNERT 2021 - IEEE INFOCOM Workshop on Computer and Networking Experimental Research using TestbedsVirtual, FranceMay 2021
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• 13 inproceedingsS.Sanaullah Faiz, N.Nadjib Achir and K.Khaled Boussetta. Increasing vehicles perception using cooperative relaying and priority-based beaconing.HPSR 2021 - IEEE 22nd International Conference on High Performance Switching and RoutingParis, FranceIEEEJune 2021, 1-6
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• 14 inproceedingsB.Berit Greinke, G.Giorgia Petri, P.Pauline Vierne, P.Paul Biessmann, A.Alexandra Börner, K.Kaspar Schleiser, E.Emmanuel Baccelli, C.Claas Krause, C.Christopher Verworner and F.Felix Biessmann. An Interactive Garment for Orchestra Conducting: IoT-enabled Textile & Machine Learning to Direct Musical Performance.TEI 2021 - 15th ACM International Conference on Tangible, Embedded and Embodied InteractionVirtual, FranceFebruary 2021
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• 15 inproceedingsP.Philippe Jacquet and L.Liubov Tupikina. Geometry of Outdoor Virus Avoidance in Cities.GSI 2021 - 5th International Conference on Geometric Science of InformationGeometric Science of InformationParis, FranceJuly 2021, 869-877
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• 16 inproceedingsL.Loïc Jouans, A.Aline Carneiro Viana, N.Nadjib Achir and A.Anne Fladenmuller. Associating the Randomized Bluetooth MAC Addresses of a Device.CCNC 2021 - IEEE Consumer Communications & Networking ConferenceLas Vegas, United StatesJanuary 2021
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• 17 inproceedingsA.Abhishek Kumar Mishra, A.Aline Carneiro Viana, N.Nadjib Achir and C.Catuscia Palamidessi. Public Wireless Packets Anonymously Hurt You.2021 IEEE 46th Conference on Local Computer Networks (LCN)IEEE LCN 2021 (Doctoral-track - Promising ideas)Edmonton / Virtual, CanadaOctober 2021
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• 18 inproceedings R.Rafael Lima Costa, A.Aline Carneiro Viana, A.Artur Ziviani and L. N.Leobino Nascimento Sampaio. Tactful opportunistic forwarding: What human routines and cooperation can improve? Proceedings of the 35th International Conference on Advanced Information Networking and Applications (AINA-2021), Volume 3 AINA2021 - 35th International Conference on Advanced Information Networking and Applications Toronto, Canada May 2021
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• 19 inproceedings A. K.Abhishek K Mishra, A.Aline Carneiro Viana and N.Nadjib Achir. SimBle: Comment générer des traces réelles Bluetooth conformes aux recommandations de préservation de la vie privée ? ALGOTEL 2021 - 23èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications La Rochelle, France 2021
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• 20 inproceedingsD.Djob Mvondo, M.Mathieu Bacou, K.Kevin Nguetchouang, S.Stéphane Pouget, J.Josiane Kouam, R.Renaud Lachaize, J.Jinho Hwang, T.Tim Wood, D.Daniel Hagimont, N.Noël de Palma, B.Bernabé Batchakui and A.Alain Tchana. OFC: an opportunistic caching system for FaaS platforms.EuroSys '21: Proceedings of the Sixteenth European Conference on Computer SystemsEUROSYS 2021 - 16th European Conference on Computer SystemsEdinburgh (online), United KingdomAssociation for Computing Machinery (ACM)April 2021, 228-244
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• 21 inproceedingsW.Wassim Seifeddine, C.Cédric Adjih and N.Nadjib Achir. Dynamic Hierarchical Neural Network Offloading in IoT Edge Networks.PEMWN 2021 - 10th IFIP International Conference on Performance Evaluation and Modeling in Wireless and Wired NetworksOttawa / Virtual, CanadaIEEENovember 2021, 1-6
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Scientific books

• 22 bookE.Emmanuel Baccelli. Internet des Objets: Défis sociétaux et domaines de recherche scientifique pour l'Internet des Objets (IoT).December 2021
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• 23 bookE.Emmanuel Baccelli. Internet of Things (IoT): Societal Challenges & Scientific Research Fields for IoT.December 2021
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Doctoral dissertations and habilitation theses

• 24 thesisL.Licia Amichi. Understanding individuals’ proclivity for novelty-seeking in human mobility.Polytechnic School - IPPNovember 2021
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• 25 thesisR.Rafael Costa. Tactful Networking as a Cornerstone for Opportunistic Human-Aware D2D Communication.Institut Polytechnique de Paris; Universidade federal da BahiaAugust 2021
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• 26 thesisD.Douglas Do couto teixeira. Predictability in Human Mobility : Interpretability, Extensions and Applications.Institut Polytechnique de Paris; Universidade federal de Minas GeraisAugust 2021
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Reports & preprints

• 27 reportL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A.Antonio Loureiro. An inherent limiting factor of human mobility prediction.INSTITUT POLYTECHNIQUE DE PARIS; INRIA Saclay, équipe TribeFebruary 2021
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• 28 reportL.Licia Amichi, A.Aline Carneiro Viana, M.Mark Crovella and A.Antonio Loureiro. Impacts of novelty seeking on predictability in human mobility.InriaDecember 2021
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• 29 reportA. C.Ana Claudia B L Monção, S.Sand Luz Correa, A.Aline Carneiro Viana and K.Kleber Vieira Cardoso. Optimizing Content Caching and Recommendations with Context Information in Multi-Access Edge Computing.INRIA Saclay - Ile de France (INRIA); Universidade Federal de GoiásAugust 2021
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• 30 miscG.Gustavo Banegas, K.Koen Zandberg, A.Adrian Herrmann, E.Emmanuel Baccelli and B.Benjamin Smith. Quantum-Resistant Security for Software Updates on Low-power Networked Embedded Devices.June 2021
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• 31 reportS. L.Sand Luz Correa, K. V.Kleber Vieira Cardoso, F. F.Felipe F Fonseca, L.Lefteris Mamatas and A. C.Aline Carneiro Viana. Itinerary Recommendation Algorithm in the Age of MEC.Inria Saclay Ile de FranceFebruary 2021
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• 32 miscL. S.Lucas Santos De Oliveira, P. O.Pedro O S Vaz-De-Melo and A.Aline Carneiro Viana. Analysing locations power in large-scale mobility data.February 2021
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• 33 reportH. C.Haron C Fantecele, S.Solohaja Rabenjamina, A.Aline Carneiro Viana, R.Razvan Stanica and A.Artur Ziviani. SafeCityMap (1st phase) – COVID INRIA mission: Investigating population mobility habits in metropolitan zones and the lockdown impact using mobile phone data.InriaJuly 2021
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• 34 miscP.Philippe Jacquet and S.Svante Janson. Variations on depth-first search performance in random digraphs.January 2022
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• 35 miscP.Philippe Jacquet, G. .Gil I. Shamir and W.Wojciech Szpankowski. Precise Minimax Regret for Logistic Regression.July 2021
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• 36 reportP.Panagiota Katsikouli, D.Diego Madariaga, M.Marco Fiore, A.Aline Carneiro Viana and A.Alberto Tarable. A Novel Sensor-Free Location Sampling Mechanism.University of Copenhagen, Faculty of Science, Denmark; Inria Saclay; NIC Chile Research Labs, University of Chile; IMDEA Software Institute; CNR - IEIITFebruary 2021
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• 37 miscA. K.Abhishek Kumar Mishra, A. C.Aline Carneiro Viana and N.Nadjib Achir. SimBle: Generating privacy preserving real-world BLE traces with ground truth.January 2021
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• 38 miscD.Douglas Teixeira, J. M.Jussara M Almeida and A.Aline Carneiro Viana. Understanding routine impact on the predictability estimation of human mobility.February 2021
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• 39 miscK.Koen Zandberg and E.Emmanuel Baccelli. Femto-Containers: DevOps on Microcontrollers with Lightweight Virtualization & Isolation for IoT Software Modules.November 2021
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