7.1.1 PrivaMovApp

• Keyword:
  Crowd-sensing
• Functional Description:
  Agora is leading the development of an Android application for user data collection purposes. The application is based on the Funf framework.
• Contact:
  Razvan Stanica
• Participants:
  Stéphane d'Alu, Hervé Rivano, Razvan Stanica, Solohaja Rabenjamina

7.1.2 urpolsens

• Name:
  UrPolSens Platform
• Keywords:
  Wireless Sensor Networks, Air Quality
• Functional Description:
  A micro-controller is integrated into a lab-designed printed circuit which includes among others: a high precision ADC, a micro-SD card reader and a radio communication module. The designed nodes measure the nitrogen dioxide (NO2) pollutant in addition to temperature and humidity, and transmit data using LoRa to a gateway, which is connected to our servers using a 4G connection. The sensors are also equipped with solar panels in order to extend their lifetime when their batteries are drained. Our platform has been operational in the downtown of the city of Lyon with 12 sensor nodes deployed in the Garibaldi street from mid-July to mid-October 2018. It has then been improved and generalized to match the need of the collaboration with Total LQA lab. It is now a versatile multi-sensors platform able to run autonomously on solar energy for months.
• Contact:
  Walid Bechkit
• Participants:
  Walid Bechkit, Hervé Rivano
• Partner:
  Intelligence des Mondes Urbains

7.1.3 3M’Air

• Name:
  3M’Air Platform
• Keywords:
  Wireless Sensor Networks, Air Quality
• Functional Description:
  We have built our own nodes equipped with multiple sensors measuring Nitrogen-Dioxide (NO2), Particulate Matter (PM1, PM2.5, PM10), temperature and humidity. They are battery-powered and equipped with a GPS module to have the position of the measurements. Data are stored on a micro SD card and at the same time sent over LoRa to a server we have developed that is responsible to store these data for future analysis. A web platform has also been developed to display the collected concentration measurements in real time. This developed solution is used in several participatory planned measurement campaigns in Lyon city.
• Contact:
  Walid Bechkit
• Participants:
  Walid Bechkit, Hervé Rivano
• Partner:
  Intelligence des Mondes Urbains

7.1.4 eSmartCity

• Name:
  eSmartCity Smart Lighting platform
• Keywords:
  Lighting, Sensors
• Functional Description:
  The objective is to analyze the pedestrian mobility on a street and to evaluate the opportunity to deploy a Smart Lighting solution. Mobility is monitored using PIR sensors and correlated with light, environment and noise sensor measurements. We are discussing with a company the opportunity to adapt our sensors to their own public lighting products.
• Contact:
  Oana Iova
• Participants:
  Oana Iova, Hervé Rivano

7.1.5 Dense LoRaSim

• Name:
  Extension to support dense LPWAN in LoRaSim
• Keyword:
  LoRaWAN
• Functional Description:
  In the settings of our dense networks research topic, we have modified the LoRaSim simulator so that it supports up to a million devices, while keeping a realistic modelisation of the channel. This will allow us to evaluate the scalability of different algorithms and protocols in a realistic scenario. We also created a fork to support ultra dense network emulation.
• Contact:
  Fabrice Valois
• Participants:
  Fabrice Valois, Oana Iova, Hervé Rivano

7.1.6 FLoRaSat

• Name:
  Extension of FLoRa for Direct-to-Satellite IoT
• Keywords:
  Iot, Satellite, LoRaWAN
• Functional Description:
  Direct to Satellite IoT (DtS-IoT) is a promising approach to deliver data transfer services to IoT devices in remote areas where deploying terrestrial infrastructure is not appealing or feasible. In this context, low-Earth orbit (LEO) satellites can serve as passing-by IoT gateways to which devices can offload buffered data to. However, transmission distances and channel dynamics, combined with highly constrained devices on the ground makes DtS-IoT a very challenging problem. To explore DtS-IoT, we propose to extend the Flora simulator based on Omnet++: i) to support Class B end-devices ii) to support LEO orbits iii) to support large scale satellite constellation. It allows us to model and simulate realistic DtS-IoT scenarios to measure the expected performance of LoRaWAN in a satellite context. Available on: https://gitlab.inria.fr/jfraire/florasat
• Contact:
  Juan Andres Fraire
• Participants:
  Juan Andres Fraire, Oana Iova, Fabrice Valois

PPAIR Plateforme LoRa - Campus Connecté 

The project aims at providing a platform that offers connectivity through a long-range, low-energy network to smart objects. The platform uses LoRa technology, which offers a wide connectivity, covering the entire INSA Lyon campus and providing a data collection service to all campus users. The main purpose of the LoRaWAN plateform is: (i) research (researchers can use it for studying reliability and capacity problems, privacy related challenges, etc.), and (ii) teaching (several courses from INSA, especially in the Telecom department can use this platform as a pedagogical tool).

Since 2019, this platform is used in the European Project Interreg Med ESMARTCITY and for the PHC Ulysses (joint collaboration with Nimbus Center, Ireland).

UrPolSens Platform 

We designed from scratch an energy efficient air pollution sensor network using Atmega micro-controllers and electrochemical air pollution probes. The micro-controller is integrated into a lab-designed printed circuit which includes among others: a high precision ADC, a micro-SD card reader and a radio communication module. The designed nodes measure the nitrogen dioxide (NO2) pollutant in addition to temperature and humidity and transmit data using LoRa to a gateway, which is connected to our servers using a 4G connection. The sensors are also equipped with solar panels in order to extend their lifetime when their batteries are drained. Our platform has been operational in the downtown of the Lyon city with 12 sensor nodes deployed in the Garibaldi street from mid-July to Mid-October 2018.

3M'air sensor platform 

We developed the 3M'Air sensor platform to be used in participatory sensing of temperature and air quality. We have built our own nodes equipped with multiple sensors measuring Nitrogen-Dioxide (NO2), Particulate Matter (PM1, PM2.5, PM10), temperature and humidity. They are battery-powered and equipped with a GPS module to have the position of the measurements. Data are stored on a micro SD card and at the same time sent over LoRa to a server we have developed that is responsible to store these data for future analyses. A web platform has also been developed to display the collected concentration measurements in real time. This developed solution is used in several participatory planned measurement campaigns in Lyon city.

The AgoraLTEplateform (ALP) 

The AgoraLTEplateform is leveraging the opensource srsLTE software suite and Software Defined Radio (SDR) to perform experimental research about autonomous and flexible cellular networks. This platform emulates an entire cellular network thanks to three software components of srsLTE. The cellular network is composed of three main elements: the core network (srsEPC), the radio access network (srsENB) ans the user equipment (srsUE). Those elements can be run on several remote machines and operate an LTE connection by the means of SDR elements: USRP-2901 from National Instrument in our case. We also have at our disposal custom sim cards usable by any off-the-shelf smartphone that permit them to access the experimental network. This platform being run by a regularly updated open source code allows us to modify its structure, tune, add or remove network parameters and run scientific experiments that are yet to few in the literature. This testbed is issued to investigate new user association mechanisms considering quality of service requirements, or emergency situations.

Leveraging Predictability for Global Optimization of IoT Networks

 We consider IoT networks where nodes are able to move to change the network topology and improve area coverage and network performance. In 27, we focus on the problem of global optimization where the nodes make use of the predictability of circumstances that affect network operations, such as the communication and sensing ranges, to anticipate future actions that need to be taken so that the correct operation of the network continues to be guaranteed with a minimum global cost. We provide a Mixed Integer Quadratic Program (MIQP)-based solution that minimizes the overall energy consumed over the entire deployment period while maintaining network connectivity and full area coverage. Results show that significant performance enhancement can be obtained when taking predictability into account compared to the case where nodes make decisions based only on their current observations.

Network Size Estimation for Direct-to-Satellite IoT

 The worldwide adoption of the Internet of things (IoT) depends on the massive deployment of sensor nodes and timely data collection. However, installing the required ground infrastructure in remote or inaccessible areas can be economically unattractive or unfeasible. Cost-effective nanosatellites deployed in low Earth orbits (LEO) are emerging as an alternative solution: on-board IoT gateways provide access to remote IoT devices, according to direct-to-satellite IoT (DtS-IoT) architectures. One of the main challenges of DtS-IoT is to devise communication protocols that scale to thousands of highly constrained devices served by likewise constrained orbiting gateways. In 23, we tackle this issue by first estimating the (varying) size of the device set underneath the (mobile) nanosatellite footprint. Then, we demonstrate applicability of the estimation when used to intelligently throttle DtS-IoT access protocols. Since recent works have shown that MAC protocols improve the throughput and energy efficiency of a DtS-IoT network when a network size estimation is available, we present here a novel and computationally-efficient network size estimator in DtS-IoT: our optimistic collision information (OCI) based estimator. We evaluate OCI's effectiveness with extensive simulations of DtS-IoT scenarios. Results show that when using network size estimations, the scalability of a frame slotted Aloha-based DtS-IoT network is boosted 8-fold, serving up to 4 × 10${}^3$ devices, without energy efficiency penalties. We also show the effectiveness of the OCI mechanism given realistic detection ratios and demonstrate its low computational cost implementation, making it a strong candidate for network estimation in DtS-IoT.

Ring Road Networks: Access for Anyone

 Several billion people currently lack reliable access to the Internet and thus to a tremendous source of knowledge. In 21, we describe a field-tested communication approach combining CubeSat platforms and delay-tolerant networking (DTN) solutions to provide asynchronous connectivity to populations and regions that are underserved by the Internet. The resulting class of networks is known as ring road networks (RRNs), a networking approach that is built on technology developed for the construction of a solar system Internet. The necessary self-sufficiency of DTN nodes enables network access to be deployed incrementally at low cost, supporting communities that cannot be profitably served by Internet satellite constellations. We present the RRN architecture and evaluate the expected performance by means of simulations. Based on the latter, we discuss mD3TN: a lightweight and open source DTN protocol stack for RRNs and other DTN classes. mD3TN has been flight-tested in ESA's OPS-SAT in low Earth orbit during December 2020 and May 2021. This work discusses the experiment results as we validated the RRN approach in concrete application use cases. The reported outcomes motivate a new application domain.

Simulating LoRa-Based Direct-to-Satellite IoT Networks with FLoRaSat

 Direct-to-Satellite-IoT (DtS-IoT) is a promising approach for data transfer to/from IoT devices in remote areas where deploying terrestrial infrastructure is not appealing or feasible. In this context, Low-Earth Orbit (LEO) satellites can serve as passing-by IoT gateways to which devices can offload buffered data to. However, transmission distance and orbital dynamics, combined with highly constrained devices on the ground makes DtS-IoT a very challenging problem. In fact, existing IoT medium access control protocols, negotiations schemes, etc. need to be revised and/or extended to scale up to these challenging conditions. The intricate time-dynamic aspects of DtS-IoT networks require of adequate simulation environments to assess the expected performance of enabling technologies. To make up for the lack of such tools, we present a novel event-driven open-source end-to-end simulation tool coined FLORASAT 29. The simulator leverages Omnet++ and includes a benchmarking DtS-IoT scenario comprising 16 cross-linked LEO satellites and 1500 IoT nodes on the surface. Satellites and devices are connected using the standard LoRaWAN Low-Power Wide Area (LPWAN) protocol (Class A and B). FLoRaSat allows the easy implementation and study of DtS-IoT radio access and core network protocols, and we take advantage of this flexibility to investigate expected network metrics and non-intuitive phenomena emerging from the resulting multi-gateway setup.

Space-Terrestrial Integrated Internet of Things: Challenges and Opportunities

 Large geographical regions of our planet remain uncovered by terrestrial network connections. Sparse and dense constellations of near-Earth orbit satellites can bridge this gap by providing Internet of Things (IoT) connectivity on a worldwide scale in a flexible and cost-effective manner. In 22, we present STEREO: a novel Space-Terrestrial Integrated IoT Architecture spanning direct-and indirect-to-satellite access from IoT assets on the surface. Framed on the identified requirements, we analyze NB-IoT and LoRa/LoRaWAN features to put these technologies forward as appealing candidates for future satellite IoT deployments. Finally, we list and discuss the key open research challenges to be addressed in order to achieve a successful space-terrestrial IoT integration.

Sparse Satellite Constellation Design for Global and Regional Direct-to-Satellite IoT Services

 In 18, we introduce and design sparse constellations for Direct-to-Satellite Internet of Things (DtS-IoT). DtS-IoT does not require a ground infrastructure, because the devices are directly connected to Low Earth Orbit satellites acting as orbiting gateways. The key idea of sparse constellations is to significantly reduce the number of in-orbit DtS-IoT satellites by (i) a proper dimensioning of the delivery delay anyway present in resource-constrained IoT services, and (ii) an optimal positioning of the orbiting gateways. First, we analyze LoRa/LoRaWAN and NB-IoT standards and derive realistic constraints on the maximum gap time between two consecutive passing-by satellites. Then, we introduce and optimize an algorithm to design quasi-optimal topologies for sparse IoT constellations. Finally, we apply our design to both global and regional coverage and we analyze the trade-off between latency, number of orbit planes and total number of satellites. Results show that sparse constellations can provide worldwide IoT coverage with only 12.5% and 22.5% of the satellites required by traditional dense constellations considering 3-hour and 2-hour gaps. Also, we show that region specific coverage of Africa and Europe can be achieved with only 4 and 3 satellites for LoRa/LoRaWAN and NB-IoT, respectively.

FAM: A frame aggregation based method to infer the load level in IEEE 802.11 networks

 In many environments, connected devices are exposed to and must choose between multiple Wi-Fi networks. However, the procedure for selecting an access point is still based on simple criteria that consider the device to be unique in the network. In particular, the network load is not taken into account even though it is a key parameter for the quality of service and experience. In 16, we investigate how an unmodified vanilla device could estimate the load of a network in the user space with no interventions from the access points. In this regard, we propose a novel and practical method, FAM (Frame Aggregation based Method). It leverages the frame aggregation mechanism introduced in recent IEEE 802.11 amendments to estimate the network load through its channel busy time fraction. FAM combines an active probing technique to measure the actual packet aggregation and Markovian models that provide the expected rate as a function of the volume and nature of the traffic on the network. We validate the effectiveness of FAM against both ns-3 simulations and test-bed experiments under several scenarios. Results show that our method FAM is able to infer the network load with a granularity based on six different levels of network loads for the considered scenarios.

Integrating LoRa Collision Decoding and MAC Protocols for Enabling IoT Massive Connectivity

 One major goal of Beyond 5G and 6G networks is to provide connectivity for a massive number of Internet-of-Things (IoT) devices. Towards that goal, Long Range (LoRa) is a promising physical layer technology which features low data-rates and large communication ranges, while requiring only low power. However, as the number of devices increases, more and more collisions occur, hence severely degrading LoRa system performances. To cope with this critical drawback, several LoRa collision decoding algorithms and MAC protocols have been proposed. In 26, we present how collision decoding algorithms interact with MAC layer protocols, and we discuss the potential of such integrated approaches. To do so, we first classify the collision decoding algorithms according to their principles and distinctive features, and compare some reference algorithms in a single simulation setup, using a Software Defined Radio (SDR) hardware. Then, we analyze how each class of MAC protocols can benefit from each category of collision decoding algorithms. Finally, we discuss longterm perspectives and open issues in this active research area.

Inference of Wi-Fi busy time fraction based on Markov chains

 IEEE 802.11 has emerged as a vital wireless network access technology for mobile devices. By providing the potential for high connectivity speeds, this technology has led to a huge rise in the number of access points (APs). In such environments, mobile devices have the choice to join several Wi-Fi networks. Despite its importance to user Quality of Experience (QoE), the AP selection is still trivial since it focuses at best on the received signal strength if not only on the user’s history. Crucial metrics that capture the overall dynamics of the AP load condition, such as the network load, are not taken into account. In 17, we propose to use the Busy Time Fraction (BTF) as a metric to choose the best AP to attach to. The BTF level of a given channel is inferred based on the frame aggregation scheme proposed since the 802.11n standard. In this regard, we build a proof of concept system, FAM (Frame Aggregation based method), that leverages the theoretical frame aggregation levels of a probe traffic returned by two analytical Markovian models and the measured ones in order to estimate not only the BTF but also the nature of the traffic. We validate the accuracy of our proposed approach against ns-3 simulations under several scenarios.

Rendez-vous Based Drift Diagnosis Algorithm For Sensor Networks Towards In Situ Calibration

 In recent years, low-cost sensors have raised strong interest for environmental monitoring applications. These instruments often suffer from degraded data quality. Notably, they are prone to drift. It can be mitigated with costly periodic calibrations. To reduce this cost, in situ calibration strategies have emerged, enabling the recalibration of instruments while leaving them in the field. However, they rarely identify which instruments actually need a calibration because of drift, so that in situ calibration may instead degrade performances. Therefore, we present novel drift detection algorithm, exploiting the concept of rendezvous between measuring instruments 19. Its originality lies mainly in the comparisons of values determining the state of the instruments, for which the quality of the measurement results is taken into account. It defines the concept of compatibility between measurement results. A case study is developed, showing an accuracy of 88% for correct detection of drifting instruments. The results of the diagnosis algorithm are then combined with calibration approaches. Results show a significant improvement of the measurement results. Notably, an increase of 15% of the coefficient of determination of the linear regression between their true values and the measured values is observed with the correction and the error on the slope and on the intercept respectively is reduced by 50% and 60% at least.

Uplink Transmission Policies for LoRa-Based Direct-to-Satellite IoT

 Direct-to-Satellite IoT (DtS-IoT) is a promising approach to deliver data transfer services to IoT devices in remote areas where deploying terrestrial infrastructure is not appealing or feasible. In this context, low-Earth orbit (LEO) satellites can serve as passing-by IoT gateways to which devices can offload buffered data to. However, transmission distances and channel dynamics, combined with highly constrained devices on the ground makes of DtS-IoT a very challenging problem. In 14, we propose LoRa-based approaches to realize scalable and energy-efficient DtS-IoT. Our work includes the Long Range-Frequency Hopping Spread Spectrum (LR-FHSS) physical layer, currently on the roadmap of future space IoT projects. Specifically, we propose uplink transmission policies that exploit satellite trajectory information. These schemes are framed with a theoretical Mixed Integer Linear Programming (MILP) model providing an upper bound on performance as well as inspiration for scheduled DtS-IoT solutions. Simulation results provide compelling evidence that trajectory based policies can duplicate the amount of IoT nodes, while specific variants can further boost the scalability by 30% without incurring energy penalties. We also quantify that LR-FHSS can improve the deployment scalability by a factor of 75x at the expenses of 30% higher device's power consumption compared to the legacy LoRa modulation.

Comparison of User Presence Information from Mobile Phone and Sensor Data

 Data collected from mobile phones or from motion detection sensors are regularly used as a proxy for user presence in networking studies. However, little attention was paid to the actual accuracy of these data sources, which present certain biases, in capturing actual human presence in a given geographical area. In 33, we conduct the first comparison between mobile phone data collected by an operator and human presence data collected by motion detection sensors in the same geographical area. Through a detailed spatio-temporal analysis, we show that a significant correlation exists between the two datasets, which can be seen as a cross validation of the two data sources. However, we also detect some significant differences at certain times and places, raising questions regarding the data used in certain studies in the literature. For example, we notice that the most important daily mobility peaks detected in mobile phone data are not actually detected by on ground sensors, or that the end of the workday activities in the considered area is not synchronised between the two data sources. Our results allow to distinguish the metrics and the scenarios where user presence information is confirmed by both mobile phone and sensor data.

Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population

 The idea of using mobile phone data to understand the impact of the Covid-19 pandemic and that of the sanitary constraints associated with it on human mobility imposed itself as evidence in most countries. In 20, we use spatiotemporal aggregated mobile phone data provided by a major French telecom operator, covering a geographical region centered on Paris for early 2020, i.e., periods before and during the first French lockdown. An essential property of this data is its fine-grained spatial resolution, which, to the best of our knowledge, is unique in the COVID-related mobility literature. Contrarily to regions or country-wide resolution, it describes population mobility flows among zones ranging from 0.025 km${}^2$ to 5.40 km${}^2$ , corresponding to 326 aggregated zones over the total area of 93.76 km${}^2$ of the city of Paris. We perform a data-driven mobility investigation and modeling to quantify (in space and time) the population attendance and visiting flows in different urban areas. Second, when looking at periods both before and during the lockdown, we quantify the consequences of mobility restrictions and decisions on an urban scale. For this, per zone, we define a so-called signature, which captures behaviors in terms of population attendance in the corresponding geographical region (i.e., their land use) and allows us automatically detect activity, residential, and outlier areas. We then study three different types of graph centrality, quantifying the importance of each zone in a time-dependent weighted graph according to the habits in the mobility of the population. Combining the three centrality measures, we compute per zone of the city, its impact-factor, and employ it to quantify the global importance of zones according to the population mobility. Our results firstly reveal the population's daily zone preferences in terms of attendance and mobility, with a high concentration on business and touristic zones. Second, results show that the lockdown mobility restrictions significantly reduced visitation and attendance patterns on zones, mainly in central Paris, and considerably changed the mobility habits of the population. As a side effect, most zones identified as mainly having activity-related population attendance in typical periods became residential-related zones during the lockdown, turning the entire city into a residential-like area. Shorter distance displacement restrictions imposed by the lockdown increased visitation to more "local" zones, i.e., close to the population's primary residence. Decentralization was also favored by the paths preferences of the still-moving population. On the other side, "jogging activities" allowing people to be outside their residences impacted parks visitation, increasing their visitation during the lockdown. By combining the impact factor and the signatures of the zones, we notice that areas with a higher impact factor are more likely to maintain regular land use during the lockdown.

Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network

 Biking is gaining in popularity all around the world as a healthy and environmentally friendly mode of transportation. Urban policies tend to encourage citizens to use bicycles. This can be done by creating new cycling infrastructures, the renovation of old ones or the deployment of bike-sharing systems (BSS). These policies having a cost, understanding and predicting the behavior of cyclists has become a necessity in order to optimize them. Classical methods analyzing cyclists’ route choices use external factors and generated choice sets of paths along with a logit model to create a discrete route choice model. Nevertheless, few studies focus on the predictive capacity that this type of model can offer. In 24, we developed a prediction-centered bicycle route choice model. Our model is created without using external factors or choice sets of paths as in the more classical methods. The idea of our method is to use deep and machine learning algorithms on GPS tracks. These algorithms learn representations from the data which replace explicit factors. To build the model, we clustered the GPS tracks using DBSCAN. The clusters allow to identify the cyclists’ preferred road segments and are used to create paths using them. A method weighting the road graph weights is developed to create paths passing through the preferred road segments of a given cluster. A LSTM is finally trained in order to retrieve a cluster from a shortest path between an origin/destination pair. Tracks created by our model are more similar to the original GPS tracks than the shortest paths or tracks generated by a prominent path computation service.

In-depth Study of RNTI Management in Mobile Networks: Allocation Strategies and Implications on Data Trace Analysis

 The advance of mobile network technologies and components heavily relies on data-driven techniques. This is especially true for fifth generation (5G) and the upcoming sixth generation (6G) networks, as the optimization of network components and protocols is expected to be fueled by artificial intelligence (AI) based solutions. When using real-world radio access measurement traces, the identity of individual users is not directly accessible because at runtime operation Base Stations (BSs) assign Radio Network Temporary Identifiers (RNTIs) to users. RNTIs are not bound to a user but are reused upon expiration of an inactivity timer, whose duration is operator dependent. This implies that, over time, multiple users are mapped to the same RNTI. In fact, the allocation of RNTIs to users is implemented in diverse and proprietary ways by operators and equipment vendors. Distinguishing individual users within the RNTI space is a non-trivial task and key to analyze traffic traces properly. In 15, we make the following contributions: i) we propose and validate two complementary methodologies to identify the RNTI inactivity threshold, and we characterize ii) the RNTI allocation process of network operators, and iii) the user traffic patterns given the specific RNTI allocation process. Our study is based on a large dataset we collected from production BSs of several mobile network operators across five different countries. We find that there exist heterogeneous strategies for RNTI allocation that BSs dynamically use depending on the traffic load and daytime. We further observe that the RNTI expiration threshold is in the order of minutes, and demonstrate how using thresholds around 10 seconds, as in the vast majority of the literature, can bias subsequent analyses. Overall, our work provides an important step towards dependable mobile network trace analysis, and lays solid foundations to research relying on traffic traces for data-driven analysis.

10.1.1 Associate Teams in the framework of an Inria International Lab or in the framework of an Inria International Program

Participants: Oana Iova, Razvan Stanica, Fabrice Valois.

10.1.2 STIC/MATH/CLIMAT AmSud projects

10.1.3 Participation in other International Programs

• Walid Bechkit was co-coordinator of the digital work-package of the FSPI project LISEN: L'Intelligence Sociale Et le Numérique led by the French Embassy in Algeria (2020-2022).

10.1.4 Visits to international teams

10.2.1 H2020 projects

• Program: H2020-ICT-2018-2020
  • Project acronym: BUGWRIGHT2
  • Project title: Autonomous Robotic Inspection and Maintenance on Ship Hulls and Storage Tanks
  • Duration: 01/2020-03/2025
  • Coordinator: GeorgiaTech Lorraine / UMI2958GT-CNRS
  • Other partners: 9 academics partners(CNRS, UPORTO, UIB, INSA, RWTH, UNI-KLU ,NTNU, UT, WMU) and 11 industrial partners (CETIM, LSL, RBP, BEYE, RINA, GLM, APDL, AASA, TRH, IEIC, DANAOS, SBK).
  • Abstract: The objective of BUGWRIGHT2 is to bridge the gap between the current and desired capabilities of ship inspection and service robots by developing and demonstrating an adapt- able autonomous robotic solution for servicing ship outer hulls. By combining the survey capabilities of autonomous Micro Air Vehicles (MAV) and small Autonomous Underwater Vehicles (AUV), with teams of magnetic-wheeled crawlers operating directly on the surface of the structure, the project inspection and cleaning system will be able to seamlessly merge the acquisition of a global overview of the structure with performing a detailed multi-robot visual and acoustic inspection of the structure, detecting corrosion patches or cleaning the surface as necessary – all of this with minimal user intervention.
• Program: Horizon2020 Research and Innovation Staff Exchange(RISE)
  • Project acronym: MISSION
  • Project title: Models in Space Systems: Integration, Operation, and Networking
  • Duration: 2021->ongoing (4y ears)
  • Coordinator: University of Twente, Netherlands
  • Other partners: Netherlands RWTH Aachen University, Germany Saarland University, Universidad Nacional de Córdoba, Argentina Universidad Nacional de Río Cuarto, Argentina D3TN,Germany GOMspace, Luxembourg Ascentio, Argentina INVAP, Argentina Skyloom, Argentina Institute of Intelligent Software, Guangzhou (IISG), China
  • Abstract: Spacecraft must work robustly in the presence of uncertainties such as random hardware faults, operator mistakes, space debris, and radiation. Classic space missions address uncertainty via large safety margins and built-in redundancy, leading to a spiral of increasing cost and complexity. A recent trend is the small-business commercialisation of space using commercial-off-the-shelf components for networked constellations of small satellites. This "New Space" approach reduces component weight, size, price, and lead time, and makes innovation increasingly driven by software. This pertains especially to resource management and data handling, while simpler components and new interactions increase uncertainty, and come with less reliable parts. Thus, overall mission connectivity, efficiency, dependability and safety in the New Space needs to be achieved on a system level - for which there is no systematic approach yet. This is partly rooted in the empirical focus of many teams, and partly in a lack of easy-to-use methods to model, analyse, and guarantee system-level dependability. This interdisciplinary project sets out to solve this space engineering problem by exploiting highly advanced techniques from the forefront of computing science research, especially model-based algorithmics. We strive for sound and efficient software tools for the development of dependable, networked, and resource-aware New Space missions. For this, the MISSION project will develop an integrated model-based technology to establish and maintain system-level properties of critical space mission parameters. A strong consortium of excellent academic and industrial partners in Europe, Argentina and China have agreed on a joint research and knowledge sharing agenda that will foster a shared culture of research and innovation, to finally deliver an ecosystem of easy-to-use methods and software tools to the New Space industry.

10.2.2 Other european programs/initiatives

• Program: CHIST-ERA 2020
  • Project acronym: ECOMOME
  • Project title: Measurement and Optimisation of Energy Consumption in Cellular Networks
  • Duration: 02/2022-01/2025 (accepted in 09/2021)
  • Coordinator: INSA Lyon
  • Otherpartners: ETS Montréal, Québec, Canada ; IMDEA, Madrid, Spain ; Politehnica University of Timisoara, Romania.
  • Abstract: This project addresses the problem of accurately modelling and optimising the energy consumption of a mobile network, with a focus on 4G and 5G technologies. This will be achieved through three main research axes. The first contribution will be represented by the first independent measurement study of energy consumption in a mobile network. The second objective of the project is to use this measurement data in order to design accurate energy consumption models for mobile networks. Finally, the project also targets the proposal of energy efficient networking solutions. Indeed, the measurement data and the energy consumption models will allow us to detect the most energy-hungry phases in a mobile network. To reduce their impact, we will propose network intelligence solutions, which are based on observing the traffic transported by the network, detecting whenever the network settings are over-consuming, and adapting the network configuration with energy efficiency metrics in mind.

10.3.1 Inria Mission Covid

• SafeCityMap project: mobile network data analysis covering the first Covid lockdown to map the epidemic risk factor.

  Coordinators: Aline Viana (Inria Tribe), Razvan Stanica (Inria Agora).

  Participants: Solohaja Rabenjamina (Inria Agora), Haron Calegari Fantecele (LNCC, Brazil), Artur Ziviani (LNCC, Brazil).

  Human mobility properties have been investigated at length using mobile phone data. However, the COVID-19 pandemic highly perturbed our mobility patterns and use of urban spaces. This raises two important questions, addressed in the SafeCityMap project. First of all, we investigate how mobility patterns at an urban scale were affected by the pandemic, and especially by harsh lockdown conditions in Spring 2020. Second, we argue that the modeling of such patterns can provide a clear association with the epidemic spread, such as for COVID-19, in different areas of a city. By understanding how different types of areas are visited in a city, SafeCityMap aims to track the temporal evolution of the potential risk of virus spread in these areas.

10.3.2 ANR

• ANR CANCAN 2019 - 2022

  Participants: Solohaja Rabenjamina, Razvan Stanica.

  The partners in this project are: CEDRIC, Inria, Orange Labs, with Thalès Communications & Security leading the project.

  The ANR CANCAN (Content and context based adaptation in mobile networks) targets the following objectives: i) collecting novel measurement datasets that describe mobile network data traffic at unprecedented spatial and temporal accuracy levels, and for different mobile services separately. The datasets will be gathered in an operational nationwide network, ii) evaluating existing analytics for classification, prediction and anomaly detection within real-world high-detail per-service mobile network data, and tailoring them to the specifications of the management of resources at different network levels, and iii) demonstrating the integration of data analytics within next-generation cognitive network architectures in several practical case studies.

• ANR CoCo5G (Traffic Collection, Contextual Analysis, Data-driven Optimisation for 5G), 2023-2027, accepted in 2022

  Participants: Hervé Rivano, Razvan Stanica.

  The partners in this project are: Thales (leader), Orange, CNAM, Inria Agora, IMDEA Networks.

  The objective of CoCo5G is to collect the first-of-its-kind longitudinal nationwide measurements dataset combining 4G and 5G data traffic. This dataset will then be used for an extensive analysis of the evolution (in France) and the dynamics of 5G traffic for various mobile services usages. This will represent a unique opportunity for the evaluation and tailoring of existing analytics for classification, prediction and anomaly detection within real-world high-detail per-service mobile network data. Finally, CoCo5G targets to demonstrate the integration of data analytics within next-generation cognitive network architecture in three practice case studies: energy-prudent 5G NR control, URLLC service support, and automated anomaly response in edge computing.

• ANR JCJC Demon (De​ployment of ​Mo​bile base stations in cellular N​etworks), 2021-2024 (accepted in 2020)

  Participants: Thibault Bellanger, Razvan Stanica (leader), Fabrice Valois, Zhiyi Zhang.

  The main objective of the DEMON project is ​to enable an architectural shift and provide dedicated solutions for rapidly deployable mobile base stations. Three main challenges can be outlined in this new approach. The first problem is ​the initial deployment of mobile base stations in the target geographical area​. The second challenge is ​the configuration of the radio access network to provide the required capacity​. Finally, ​the permanent reconfiguration of the network needs to be considered, accounting ​not only for UE, but also for base station movement​.

• ANR JCJC Doll (Efficient DOwnLink Communication for Increased LoRaWAN Capacity), 2022-2025 (accepted in 2021)

  Participants: Alexandre Guitton, Gwendoline Hochet Derevianckine, Oana Iova (leader), Fabrice Valois.

  The goal of this project is to propose a downlink strategy that will unleash the full potential of LoRaWAN networks and push the deployment of new applications that until now could not properly take advantage of the downlink communication available in LoRaWAN. In order to increase network capacity under confirmed traffic, while maintaining a reliable uplink communication and a low energy consumption for the end devices, we set the following objectives: i) understand and quantify the consequences of overlapping uplink and downlink communications, ii) evaluate and improve gateway selection algorithm for downlink communication, and iii) propose an energy efficient scheduling for handling acknowledgements.

• ANR JCJC Dron-Map (Réseau de drones pour le suivi de panaches de pollution dans les situations d'urgence), 2021-2024 (accepted in 2021)

  Participants: Mohamed Sami Assenine, Walid Bechkit (leader), Ichrak Mokhtari, Hervé Rivano, Alexandros Sidiras Galante.

  The DRON-MAP project focuses on the use of cooperative UAV networks for pollution plume monitoring in emergency situations (industrial accidents, natural disasters, deliberate terrorist releases, etc.). The deployment of a UAV network in these situations face different scientific and technical challenges such as taking into account the strong plume dynamics, the timely data analysis, the reliable communication and coordination between UAVs and the planning of optimal trajectories. The objective of DRON-MAP project is to address these challenges while proposing a new global and systemic approach. Based on reliable communications and coordination between drones, our approach will federate an instantaneous estimation and a prediction of the plume evolution with efficient anticipatory algorithms of optimal path planning. A network testbed of few communicating UAVs will be set up in order to assess real-world feasibility and performance at a small scale.

• ANR MAESTRO 5G 2019 - 2022

  Participants: Hervé Rivano, Razvan Stanica.

  The partners in this project are: CEDRIC, Inria, L2S, LIA, Nokia Bell Labs, TSP, with Orange Labs leading the project.

  The ANR MAESTRO 5G (Management of slices in the radio access of 5G networks) is expected to provide: i) a resource allocation framework for slices, integrating heterogeneous QoS requirements and spanning on multiple resources including radio, backhauling/fronthauling and processing resources in the RAN, ii) a complete slice management architecture including provisioning and re-optimization modules and their integration with NFV and SDN strata, iii) a business layer for slicing in 5G, iv) a demonstrator showing the practical feasibility as well as integration of the major functions and mechanisms proposed by the project, on a 5G Cloud RAN platform. The enhanced platform is expected to support the different 5G services.

• ANR Stereo (Space-Terrestrial Integrated IoT), 2023-2027, accepted in 2022

  Participants: Juan A. Fraire, Oana Iova, Fabrice Valois.

  The partners in this project are: Inria (leader of the project), IRIT / ENSEEIHT (UMR CNRS 5505), Kinésis, LAAS (CNRS UPR 8001), LIG / UGA (UMR CNRS 5217).

  The objective of this project is to achieve a Space-Terrestrial Integrated Internet of Things (STEREO) network, in which IoT devices can seamlessly hook to gateways on ground or directly to low-Earth orbit (LEO) satellites when no network infrastructure is present. The feasibility and expected performance will be assessed by objectives described in this section: O.1) defining new network architectures, O.2) evaluating the enabling IoT technologies, O.3) designing the software components, and O.4) prototyping the hardware modules.

• ANR Plan de relance - mesure de préservation de l'emploi de R&D

  Participants: Walid Bechkit, Hervé Rivano, Razvan Stanica.

  Agora started a collaboration with Rtone in December 2021, for a duration of 2 years. The objective of this collaboration is to integrate artificial intelligence solutions in the control of a fleet of drones for pollution monitoring purposes.

10.3.3 GDR CNRS RSD - Pôle ResCom

• Ongoing participation (since 2006)

  Communication networks, working groups of GDR ASR/RSD, CNRS. Oana Iova is member of the steering committee of the GDR RSD. Razvan Stanica is member of the scientific council of the GDR RSD. Fabrice Valois is member of the steering committee of the GDR RSD and also chair of the Networking axis of the GDR RSD. All the members of Agora are regular participants to the GDR RSD.

11.1.1 Scientific events: organisation

11.1.2 Scientific events: selection

11.1.3 Journal

11.1.4 Invited talks

• Walid Bechkit gave a research seminar about "Réseaux de capteurs sans fil au service du suivi des phénomènes physiques : de la théorie à la pratique", February 2022, ESI Alger, Algeria.
• Walid Bechkit was panelist at the scientific day of theLabex IMU, "La transition urbaine dans tous ses états", November 2022, Lyon.
• Oana Iova gave a talk about Research Challenges in LoRaWAN, I-Cube, Université de Strasbourg.

11.1.5 Leadership within the scientific community

• Oana Iova is member of the steering committee of the GDR RSD.
• Oana Iova is in charge of the mentorship actions (e.g., seminars) for the GDR RSD.
• Razvan Stanica is member of the scientific council of the GDR RSD.
• Fabrice Valois is chair of the scientific committee of the Labex IMU.
• Fabrice Valois is member of the steering committee of the GDR RSD.
• Fabrice Valois is chair of the Networking axis of the GDR RSD.

11.1.6 Scientific expertise

• Walid Bechkit was president of the recruitment committee for an ATER position (CITI research Laboratory, Telecommunications Department, INSA Lyon).
• Walid Bechkit is member of "Club des experts" of the "Conseil Économique, Social et Environnemental Régional - CESER - ATMO-AURA".
• Alexandre Guitton was the member of the Hcérés committee for the FEMTO-ST (UMR CNRS 6174).
• Razvan Stanica was member of the associate professor recruitement committee at CNAM Paris.
• Razvan Stanica was reviewer for the ANR, APP 2023, CE48 (Fondements du numérique : informatique, automatique, traitement du signal).

11.2.1 Teaching

11.2.2 Supervision

• Masters
  • Esther Allain, from 05/2022 until 08/2022, Network Server Deployment for LoRaWAN. Advisors: Oana Iova, Alexandre Guitton, Fabrice Valois.
  • Mohamed Sami Assenine, until 06/2022, Apprentissage par renforcement pour l’optimisation de la mobilité dans les réseaux de capteurs sans fil. Advisors: Walid Bechkit, Hervé Rivano.
  • Thibaut Bellanger, from 03/2022 until 08/2022, Slicing in LoRaWAN networks. Advisors: Alexandre Guitton, Razvan Stanica, Fabrice Valois.
  • Anais Boumendil, until 06/2022, Réduction de la consommation énergétique des modèles d’apprentissage automatique. Advisor: Walid Bechkit.
  • Carlos Fernandez Hernandez, from 04/2022 until 07/2022, Experimentations of LoRa 2.4 GHz. Advisors: Alexandre Guitton, Gwendoline Hochet Derevianckine, Oana Iova, Fabrice Valois.
  • Lise Jacquot , until 02/2022, Energy consumption of RAN mechanisms. Advisor: Razvan Stanica.
  • Diego Maldonado Munoz, from 03/2022 until 08/2022, Convergence of Satellite - IoT networks. Advisor: Juan A. Fraire.
  • Rachida Saroui , until 02/2022, Impact du lien descendant sur la performance de LoRaWAN. Advisors: Alexandre Guitton, Oana Iova, Fabrice Valois.
  • Yuxuan Song, from 05/2022 until 07/2022, Gateway selection algorithm for LoRaWAN networks. Advisors: Oana Iova, Alexandre Guitton, Fabrice Valois.
• PhD, defended in 2022
  • Mohamed Anis Fekih, Urban pollution using wireless sensor networks, from 11/2018 until 10/2022. Advisors: Walid Bechkit, Hervé Rivano.
  • Romain Pujol, User association in self-deployable cellular network, from 11/2018 until 07/2022. Advisors: Razvan Stanica, Fabrice Valois.
• PhD, started in 2022
  • Mohammed Sami Assenine, Apprentissage par renforcement pour l'optimisation de la mobilité dans les réseaux de capteurs sans fil : application au suivi de la pollution, since 10/2022. Advisors: Walid Bechkit, Hervé Rivano.
  • Youssef Badra, Measuring and modelling energy consumption in cellular networks, since 03/2022. Advisor: Razvan Stanica.
  • Anais Boumendil, Vers des modèles d’apprentissage automatique à faible consommation d'énergie pour les plateformes à ressources limitées, since 11/2022. Advisor: Walid Bechkit.
  • Diego Maldonado Munoz, Adaptations, Optimizations, and Learning Approaches for Direct-to-Satellite Internet of Things, since 12/2022. Adivsors: Juan A. Fraire, Hervé Rivano.
  • Sekinat Yahya, Energy consumption optimisation in cellular networks, since 02/2022. Advisor: Razvan Stanica.
• Phd on going
  • Gwladys Djuikom, Mobility analysis using cellular data, since 10/2021. Advisors: Diala Naboulsi (ETS Montréal, Québec, Canada), Razvan Stanica.
  • Mohamed El Emary,Task offloading in airborne-assisted networks, since 07/2021. Advisors: Diala Naboulsi (ETS Montréal, Québec, Canada), Razvan Stanica.
  • Gwendoline Hochet Derevianckine,Faisabilité et performances d’un réseau LoRaWAN dans la bande ISM 2.4GHz, since 04/2021. Advisors: Alexandre Guitton, Oana Iova, Baozhu Ning (Semtech), Fabrice Valois.
  • Kawtar Lasri, Data collection and distributed spatial coordination in LPWAN networks, since 01/2019. Advisors: Loubna Echabbi (INPT Rabat, Morocco), Oana Iova, Yann Ben Maissa (INPT Rabat, Morocco), Fabrice Valois.
  • Lucas Magnana, De la ville intelligente à la ville prédictive, application aux modes de transport actifs, since 10/2020. Advisors: Nicolas Chiabaut (LICIT, ENTPE / IFSTTAR), Hervé Rivano.
  • Ichrak Mokhtari, Spatio-temporal analysis of pollution data from low cost sensors, since 11/2019. Advisors: Walid Bechkit, Hervé Rivano.
  • Camille Moriot, DDos Attacks and their impacts on the Internet Architecture, since 09/2020. Advisors: François Lesueur (CITI) Nicolas Stouls (CITI), Fabrice Valois.
  • Hnin Pann Phyu, End-to-end mobile network slicing, since1 0/2020. Advisors: DialaNaboulsi (ETS Montréal, Québec Canada), Razvan Stanica.
  • Mihai Popescu, Connectivity constrained mobility in fleets of robots, since 11/2015. Advisors: Olivier Simonin (CITI, INSA Lyon), Anne Spalanzanni (LIG, UGA), Fabrice Valois.
  • Solohaja Rabenjamnia,Data analysis of cellular trafic, since 11/2018. Advisors: HervéRivano, Razvan Stanica.
  • Zhiyi Zhang, Deployment and management of mobile base stations, since 10/2021. Advisors: Razvan Stanica, Fabrice Valois.

11.2.3 Juries

• Oana Iova was a examiner in the following PhD defense committees:
  • M. Hamnache, Performances et gestion de l'itinérance dans les réseaux LoRaWAN, IRIT, ENSEEIHT.
• Hervé Rivano was a reviewer in the following PhD defense committees:
  • N. Okafor, Artificial Intelligence Enabled IoT-based System for Environmental Monitoring: Design and Evaluation, University College Dublin.
• Fabrice Valois was a reviewer in the following HDR defense committees:
  • J. Montavont, Mobility Support in Low Power Asynchronous Wireless Networks, I-Cube, Université de Strasbourg.
• Fabrice Valois was a reviewer in the following PhD defense committees:
  • R. C. Juacaba-Neto, Privacy-aware Aggregation of IoT Streams in Multi-Owner Networks, ICube, Université de Strasbourg.
  • R. Barbau, Performances des réseaux NB-IoT terrestres et satellite, IRIT, ENSHEEIT.
  • J. Hérard, Structuration autonome des réseaux IoT de type LoRa / LoRaWAN, Crestic, URCA.
• Fabrice Valois was a examiner in the following PhD defense committees:
  • A. Amoordon, Méthodes de détection d’attaques cybernétiques par une surveillance multicouches de communication, Université Gustave Eiffel.

11.3.1 Internal or external Inria responsibilities

• Oana Iova is in charge of Sustainable and socially responsible development (DDRS) for Agora.
• Hervé Rivano is the best ever REP.
• Hervé Rivano was in charge of the IoT panel discussion for the Networking Prospective of Inria.

11.3.2 Interventions

• Lucas Magnana and Hervé Rivano was interviewed on the concept of virual bicycle lanes, La Tribune de Lyon, June 2022.
• Hervé Rivano was interviewed on the urban heat island phenomena for TV News, M6, July 2022.
• Hervé Rivano was involved on the topic Les enjeux environnementaux du numérique, for Fête de la science, Vienne, France, October 2022.

International journals

• 14 articleG.Guido Álvarez, J. A.Juan A Fraire, K. A.Khaled Abdelfadeel Hassan, S.Sandra Céspedes and D.Dirk Pesch. Uplink Transmission Policies for LoRa-Based Direct-to-Satellite IoT.IEEE AccessJuly 2022, 1-1
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• 15 articleG.Giulia Attanasio, C.Claudio Fiandrino, M.Marco Fiore, J.Joerg Widmer, N.Norbert Ludant, B.Bastian Bloessl, K.Konstantinos Kousias, Ö.Özgü Alay, L.Lise Jacquot and R.Razvan Stanica. In-depth Study of RNTI Management in Mobile Networks: Allocation Strategies and Implications on Data Trace Analysis.Computer Networks219October 2022, 109428
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• 16 articleN. E.Nour El Houda Bouzouita, A.Anthony Busson and H.Hervé Rivano. FAM: A frame aggregation based method to infer the load level in IEEE 802.11 networks.Computer Communications191July 2022, 36-52
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• 17 articleN. E.Nour El Houda Bouzouita, A.Anthony Busson and H.Herve Rivano. Inference of Wi-Fi busy time fraction based on Markov chains.Ad Hoc NetworksJuly 2022, 102963
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• 18 articleG. M.Gabriel Maiolini Capez, S.Santiago Henn, J.Juan A. Fraire and R.Roberto Garello. Sparse Satellite Constellation Design for Global and Regional Direct-to-Satellite IoT Services.IEEE Transactions on Aerospace and Electronic SystemsJune 2022, 1-16
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• 19 articleF.Florentin Delaine, B.Bérengère Lebental and H.Hervé Rivano. Rendez-vous Based Drift Diagnosis Algorithm For Sensor Networks Towards In Situ Calibration.IEEE Transactions on Automation Science and EngineeringJune 2022
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• 20 articleH.Haron Fanticelli, S.Solohaja Rabenjamina, A.Aline Carneiro Viana, R.Razvan Stanica, L.Lucas Santos de Oliveira and A.Artur Ziviani. Data-driven Mobility Analysis and Modeling: Typical and Confined Life of a Metropolitan Population.ACM Transactions on Spatial Algorithms and Systems83September 2022, 1-33
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• 21 articleM.Marius Feldmann, J.Juan Fraire, F.Felix Walter and S.Scott Burleigh. Ring Road Networks: Access for Anyone.IEEE Communications Magazine6042022, 38-44
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• 22 articleJ. A.Juan Andrés A Fraire, O.Oana Iova and F.Fabrice Valois. Space-Terrestrial Integrated Internet of Things: Challenges and Opportunities.IEEE Communications MagazineSeptember 2022
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• 23 articleP.Pablo Ilabaca, S.Samuel Montejo-Sánchez, J.Juan Fraire, R. D.Richard Demo Souza and S.Sandra Céspedes. Network Size Estimation for Direct-to-Satellite IoT.IEEE Internet of Things JournalNovember 2022
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• 24 articleL.Lucas Magnana, H.Hervé Rivano and N.Nicolas Chiabaut. Implicit GPS-based bicycle route choice model using clustering methods and a LSTM network.PLoS ONEMarch 2022, 1-20
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• 25 articleM.Mina Rady, Q.Quentin Lampin, D.Dominique Barthel and T.Thomas Watteyne. Bringing life out of diversity: Boosting network lifetime using multi‐PHY routing in RPL.Transactions on emerging telecommunications technologiesJuly 2022, e4592
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• 26 articleW.Weixuan Xiao, M.Megumi Kaneko, N.Nancy El Rachkidy and A.Alexandre Guitton. Integrating LoRa Collision Decoding and MAC Protocols for Enabling IoT Massive Connectivity.IEEE Internet of Things Magazine2022
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International peer-reviewed conferences

• 27 inproceedingsD.Djawhara Benchaira, O.Okba Tibermacine, W.Walid Bechkit and A.Abdelmalik Bachir. Leveraging Predictability for Global Optimization of IoT Networks.ICC 2022 - IEEE International Conference on CommunicationsICC 2022 - IEEE International Conference on CommunicationsSeoul, South KoreaIEEE2022, 3586-3591
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• 28 inproceedingsP. R.Pedro R D’argenio, J. A.Juan A Fraire, A.Arnd Hartmanns and F.Fernando Raverta. Comparing Statistical and Analytical Routing Approaches for Delay-Tolerant Networks.QEST 2022: Quantitative Evaluation of Systems.QEST 2022: Quantitative Evaluation of Systems13479Lecture Notes in Computer ScienceWarsaw, PolandSpringer International PublishingSeptember 2022, 337-355
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• 29 inproceedingsJ. A.Juan A. Fraire, P.Pablo Madoery, M.Mehdi Ait Mesbah, O.Oana Iova and F.Fabrice Valois. Simulating LoRa-Based Direct-to-Satellite IoT Networks with FLoRaSat.2nd Workshop on Non-Terrestrial Networks in 6G Wireless (NTN-6G), Satellite Workshop of IEEE WoWMoMBelfast, United KingdomJune 2022
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• 30 inproceedingsA.Alexandre Guitton and M.Megumi Kaneko. Multi-Gateway Demodulation in LoRa.GLOBECOM 2022 - IEEE Global Communications ConferenceGLOBECOM 2022 - IEEE Global Communications ConferenceRio de Janeiro, BrazilDecember 2022
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• 31 inproceedingsC.Camille Moriot, F.Francois Lesueur, N.Nicolas Stouls and F.Fabrice Valois. How to build socio-organizational information from remote IP addresses to enrich security analysis?LCN 2022 - IEEE 47th Conference on Local Computer NetworksEdmonton, CanadaIEEESeptember 2022, 287-290
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• 32 inproceedingsH.Hnin Pann Phyu, D.Diala Naboulsi and R.Razvan Stanica. Mobile Traffic Forecasting for Network Slices: A Federated-Learning Approach.PIMRC 2022 - IEEE 33rd International Symposium on Personal, Indoor and Mobile Radio CommunicationsVirtual, JapanSeptember 2022
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• 33 inproceedingsS.Solohaja Rabenjamina, R.Razvan Stanica, O.Oana Iova and H.Hervé Rivano. Comparison of User Presence Information from Mobile Phone and Sensor Data.MSWiM 2022 – 25th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile SystemsMontreal, CanadaOctober 2022
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• 34 inproceedings R.Rachida Saroui, A.Alexandre Guitton, O.Oana Iova and F.Fabrice Valois. Uplink and downlink are not orthogonal in LoRaWAN! IEEE VTC 2022 Fall - IEEE Vehicular Technology Society Londres, United Kingdom September 2022
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• 35 inproceedingsG.Gregory Stock, J. A.Juan A Fraire and H.Holger Hermanns. Distributed On-Demand Routing for LEO Mega-Constellations: A Starlink Case Study.ASMS/SPSC 2022 - 11th Advanced Satellite Multimedia Systems Conference and the 17th Signal Processing for Space Communications WorkshopGraz, AustriaIEEESeptember 2022, 1-8
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• 36 inproceedingsW.Weixuan Xiao, N.Nancy El Rachkidy and A.Alexandre Guitton. SF-DS: A Slot-Free Decoding Scheme for Collided LoRa Transmissions.VTC 2022 - IEEE 95th Vehicular Technology ConferenceHelsinki, FinlandJune 2022
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• 37 inproceedingsW.Weixuan Xiao, G.Gil de Sousa, N.Nancy El Rachkidy and A.Alexandre Guitton. An Open-Source GNU Radio Framework for LoRa Physical Layer and Collision Resolution.IEEE VTC Fall 2022 - IEEE Vehicular Technology ConferenceLondres, United KingdomSeptember 2022
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National peer-reviewed Conferences

• 38 inproceedingsJ. A.Juan Andrés A Fraire, M.Mehdi Mesbah, P.Pablo Madoery, O.Oana Iova and F.Fabrice Valois. FLoRaSat : Le simulateur pour la communication LoRa avec les satellites LEO.CORES 2022 – 7ème Rencontres Francophones sur la Conception de Protocoles, l’Évaluation de Performance et l’Expérimentation des Réseaux de CommunicationSaint-Rémy-Lès-Chevreuse, FranceMay 2022, 1-4
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• 39 inproceedings R.Rachida Saroui, A.Alexandre Guitton, O.Oana Iova and F.Fabrice Valois. La rumeur disait faux : ils ne sont pas orthogonaux ! CORES 2022 – 7ème Rencontres Francophones sur la Conception de Protocoles, l’Évaluation de Performance et l’Expérimentation des Réseaux de Communication Saint-Rémy-Lès-Chevreuse, France May 2022
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Scientific book chapters

• 40 inbookM.Matthieu Adam, N.Nathalie Ortar, L.Luc Merchez, G.-H.Georges-Henry Laffont and H.Hervé Rivano. Conducting Interviews with Maps and Videos to Capture Cyclists’ Skills and Expertise.Becoming Urban Cyclists: From Socialization to SkillsUniversity of Chester PressJanuary 2022, 18-43
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• 41 inbookH.Hervé Rivano. Smart cities ou human city, gouverner la ville par les data.Maîtriser l’IA au service de l'action publiqueUne responsabilité individuelle et collectiveAu fil du débat-Action publiqueBerger LevraultJanuary 2023
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Doctoral dissertations and habilitation theses

• 42 thesisM. A.Mohamed Anis Fekih. Low-cost wireless sensor networks in participatory air quality monitoring.Université de LyonFebruary 2022
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• 43 thesisR.Romain Pujol. User association in flexible and agile mobile networks.Université de LyonJuly 2022
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Reports & preprints

• 44 reportN. E.Nour El Houda Bouzouita, A.Anthony Busson and H.Hervé Rivano. Inference of Wi-Fi Busy Time Fraction based on Markov Chains.Inria LyonApril 2022, 1-35
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Other scientific publications

• 45 inproceedingsH.Hnin Pann Phyu, D.Diala Naboulsi and R.Razvan Stanica. Poster: Privacy-Aware Decentralized Multi-Slice Traffic Forecasting.MobiSys 2022 - The 20th ACM International Conference on Mobile Systems, Applications, and ServicesPortland, United StatesJune 2022
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Scientific popularization

• 46 article H.Hervé Rivano and J.Joanna Jongwane. Quelles villes intelligentes pour demain ? Interstices October 2022
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