PhD position in Low Power Wide Area Networks at Agora team (CITI Lab, INRIA Grenoble)


We live in a world where technology is advancing at a very fast pace. The current wireless scene includes a multitude of technologies that co-exist in the same environment. New long-range technologies (e.g., Sigfox, LoRa, NB-IoT) manage to transmit small data packets over several kilometers, while consuming just a few mA. Due to the low price of these radio chips, and the simplicity of the network architecture, network operators around the world are deploying these new technologies, as it helps them avoid the classical multi-hop wireless networks that are expensive to build and maintain. However, if deployments continue at the current rate, it will considerably increase the density of devices, which will become very challenging both from a network infrastructure and data collection perspectives. This thesis tackles the problem of how to create a reliable and energy efficient long-range network of millions of devices.

For more details and application, please use this link:

Applications received outside this system will not be considered.


Oana Iova
Associate Professor (Maître de conférences)
INSA Lyon – INRIA Agora research group

CITI Talk: “IF Neuron: theoretical study and application to digital communication”, Anne Savard (Associate Professor, IMT Lille), July 9th, at 10:30 am in TD-D room


IF Neuron: theoretical study and application to digital communication


In the context of digital communication, one main mechanism proposed in the literature to overcome the large consumption of MAC layers when establishing communications is called wake-up radio: The main processor is only waking up when receiving a specific signal, as for instance the node ID in the network. Unfortunately, since most of the wake-up receivers rely on standard micro-controller, they suffer a large decrease of energy efficiency. Nevertheless, if the wake-up receivers was designed with neuromorphic circuits, one could achieve high energy efficiency for IoT and ad hoc networks.

The main question that is tackled in this presentation is whether a neuro-inspired detection scheme using an Integrate-and-Fire neuron is reliable enough when one needs to detect a weak signal surrounded by noise.


Anne Savard received the Eng. degree in Electrical Engineering with specialization in Multimedia Systems from the Ecole Nationale Supérieure de l’Electronique et de ses Applications (ENSEA), Cergy-Pontoise, France, and the M.Sc. degree in Intelligent and Communicating Systems from Univeristé Cergy-Pontoise, both in 2012.

From October 2012to September 2015,she was a PhD student at ETIS Laboratory/ENSEA, under the supervision of Claudio Weidmann and David Declercq. Her research interests include modern channel coding, cooperative communication and multi-user information theory.

She defended her PhD entitled ‘Coding for cooperative communications: Topics in distributed source coding and relay channels’ on September, 22th, 2015.

PhD Position on Scatter Radio and RFID Tag-to-tag communications for IoT at CITI Lab

Scientific context, research program and objectives

With the emergence of cognitive sensor networks, and notably the Internet of Things, the passive UHF RFID (Ultra-High Frequency Radiofrequency IDentification) technology is evolving with new functionalities and new types of applications outstripping typical logistics, security and traceability applications. Always benefiting of unitary identification, new types of tags are emerging, so-called augmented tags, because they become sensor-tags with new capabilities as environment sensibility, cognitive behavior, data processing, communication between tags, etc.
In this context, the PhD thesis objective is to propose original strategies in order to ensure the communication between two future augmented tags. Recent works demonstrated the sensor-tag concept with tags enable to sense temperature, humidity, crack, vibration, gas, etc. Furthermore, several prototypes of augmented tags have also been proposed integrating until to three sensors, sometimes an actuator, private or shared memory, and microcontroller [1-4]. Otherwise and in parallel, the tag-to-tag communication concept has been introduced also allowing new functionalities, but also requiring additional energy sources (ambient or dedicate ones) especially when the distance between tags is higher than some centimeters [5-6]. Reversely, depending on the application, this very short-range behavior could be used to increase security or ID pairing. More widely, the concept of scatter radio is a hot topic because of its ability to establish communications without emitting any additional radiowaves in the environment. The combination of these two approaches open the way for multiple perspectives but one of main challenges will be to conserve the passive characteristic of tags.
In this context, the objective of the proposed project focuses on the proposition of strategies and methods in order to optimize the tag-to-tag communication depending on the target application. Particularly, the studied scenarios will be discussed in the framework of the Spie ICS – INSA Lyon chair on IoT. It is worth noting that the solutions must consider the EPC Gen 2 standard in order to accelerate their eventual deployment in industrial fields. The expected contributions cover theoretical concepts until experimental aspects associating new strategies, new tag capabilities, new data communication protocols, etc.

Profile of potential candidate

Master in Electrical Engineering or Telecommunication Engineering with excellent skills in microwave, RF systems, and applied signal processing and strong interest for the proposed topic. Good English language skills are also required.

Skills and professional project after PhD

Expert in microwave, specifically in RFID and sensor network, distributed optimization, with strong experience in: modeling, simulation and design of RF circuits / components; software such as ADS, CST, HFSS, Matlab; RF measurement.

Funding and location

This PhD thesis will be fully funding by the Spie ICS – INSA Lyon chair on IoT ( The PhD candidate will be hosted in the CITI Lab (, a research lab associated to INSA Lyon ( and Inria (


Guillaume Villemaud (HDR, 50%), Florin Hutu (50%)


[1] S. Kim, C. Mariotti, F. alimenti, P. Mezzanote, A. Georgiadis, A. Collado, L. Roselli, M.M. Tentzeris, “No battery required: perpetual RFID-enabled wireless sensors for cognitive intelligence applications,” IEEE Microwave Magazine, vol. 14, no. 5, pp. 66-77, July-August 2013.
[2] B.S. Cook, R. Vyas, S. Kim, T.Thai, T. Le, A. Traille, H. Aubert, M.M. Tentzeris, “RFID-based sensors for zero-power autonomous wireless sensor networks,” IEEE Sensors Journal, vol. 14, no. 8, pp. 2431-2419, August 2014.
[3] C. Occhiuzzi, G. Marrocco, “Constrained-design of passive RFID sensor antennas,” IEEE Transactions on Antennas and Propagation, vol. 61, no. 6, pp. 2972-2980, June 2013.
[4] R. Colella, L. Tarricone, L. Catarinucci, “SPARTACUS: self-powered augmented RFID tag for autonomous computing and ubiquitous sensing,” IEEE Transactions on Antennas and Propagation, vol. 63, no. 5, pp. 2272-2281, May 2015.
[5] P.V. Nikitin, S. Ramamurthy, R. Martinez, K.V.S. Rao, “Passive tag-to-tag communication,” in Proc. IEEE International Conference on RFID, Orlando, US, April 2012.
[6] L. Zhou, F. Hutu, G. Villemaud, Y. Duroc, “Simulation framework for performance evaluation of passive RFID tag-to-tag communication,” European Conference on Antennas and Propagation, France, April 19-24, 2017.

CITI Talk: “Eye Tracking Algorithms”, Prof Radu Gabriel Bozomitu (“Gheorghe Asachi” Technical University, Romania), June 12th, at 11 am in “salle TD-C” ( Claude Chappe Building)


Eye Tracking Algorithms


In recent years, the interest in eye detection applications has increased considerably. There are a lot of eye detection methods used in different applications such as neuroscience, psychology, assistive technologies, in order to communicate with disabled patients, computer gaming, monitoring technologies for driver’s fatigue (in commercial and public transport), advertising industry, people identification based on face recognition and eye (iris) detection and in different military applications to help pilots to aim weapons just by looking at a target. A head-mounted eye tracking interface consists of an infrared video camera mounted on a frame glasses right underneath the eye, connected to a PC (or laptop), for eye pupil image acquisition and processing. This device is used to measure the point of gaze or the motion of an eye relative to the head. The presentation will focus on the software component used in eye tracking interfaces for real-time applications, which includes the algorithms for eye image binarization, pupil center detection, system calibration, mapping and ideogram selections. Different types of pupil detection algorithms are comparatively presented: the least squares fitting of ellipse, the RANdom SAmple Consensus (RANSAC) paradigm, the circular/elliptical Hough transform- based approaches, the projection method algorithm, the detection of the maximum dark area centroid in the eye image and the STARBURST algorithm.


Radu Gabriel Bozomitu received the degree in communications and electronic engineering; the master degree in the field of digital radio-communications; and the Ph.D. degree from the “Gheorghe Asachi” Technical University of Iaşi, Faculty of Electronics, Telecommunications and Information Technology in 1995, 1996 an  2005, respectively. R. G. Bozomitu obtained the PhD advisor position in 2017 and works as professor at the Department of Telecommunications and Information Technologies from Faculty of Electronics, Telecommunications and Information Technology from the “Gheorghe Asachi” Technical University of Iaşi. His present interests are in the areas of radio communications, analog integrated circuit design and assistive technology. Courses taught at “Gheorghe Asachi” Technical University of Iasi: “Radio communications“, “VLSI implementation of the radiofrequency circuits” and “Advanced radio communications”. He has edited or co-authored five books on analog VLSI circuits design, radiocommunications and assistive technology.

CITI Talk: “Maximising the Utility of Virtually Sliced Millimetre-Wave Backhauls via a Deep Learning Approach”, Rui Li, PhD student at the University of Edinburgh, Inria antenne


Maximising the Utility of Virtually Sliced Millimetre-Wave Backhauls via a Deep Learning Approach


Advances in network programmability enable operators to ‘slice’ the physical infrastructure into independent logical networks. By this approach, each network slice aims to accommodate the demands of increasingly diverse services. Precise allocation of resources to slices across future 5G millimetre-wave backhaul networks, so as to optimise their utility, is however challenging. This is because the performance of different services often depends on conflicting requirements, including bandwidth, sensitivity to delay, or the monetary value of the traffic incurred. In this talk, I will present our recent work in which we propose a general rate utility framework for slicing mm-wave backhaul links, which encompasses all known types of service utilities, i.e. logarithmic, sigmoid, polynomial, and linear. We then employ a deep learning solution to tackle the complexity of optimising non-convex objective functions built upon arbitrary combinations of such utilities. Specifically, using a stack of convolutional blocks, our approach can learn correlations between traffic demands and achievable optimal rate assignments. The proposed solution can be trained within minutes, following which it computes rate allocations that match those obtained with state-of-the-art global optimisation algorithms, yet orders of magnitude faster. This confirms applicability to highly dynamic traffic regimes and we demonstrate up to 62% network utility gains over a baseline greedy approach.

PhD position on Mobile Crowd Sensed Data Analysis (Agora team – CITI Lab – INRIA/INSA-Lyon)


Mobile Crowd Sensed Data Analysis: Application to Participatory Environmental Crowd Sensing in Smart Cities

Thesis Description

The growing emergence of low-cost environmental monitoring systems combined with the recent advances in the design of Internet-of-Things architectures and protocols has given a new impetus to smart cities applications which is expected to significantly enhance the fine-grained characterization of different physical quantities in our cities (air quality, temperature, noise, etc.).

In this perspective, a promising approach is to involve citizens in the monitoring process using low-cost platforms and built-in sensors in order to collectively monitor different physical quantities. While relying on very high number of people to gather data is promising in accumulating large volumes of data, issues such as dealing with the variation in data accuracy due to the heterogeneity of sensing hardware and conditions, space-time continuity of measures, phenomena dynamics, impact of mobility on sensor quality, etc. arise and make it challenging to efficiently analyse the mobile crowd sensed data [1].

The aim of this thesis is to propose and evaluate novel solutions for efficient fine-grained mapping of physical phenomena based on mobile crowd sensed data with a focus on air quality and temperature.  Two directions will be explored. The first direction is based on data interpolation using techniques such as log-normal regression [2], deep learning [2,3], generalized additive modelling [4,5], Kriging-based modelling [6], etc. The second direction concerns data assimilation where the measures are incorporated into numerical models of the studied phenomena [7,8].

The Ph.D. student is expected to design novel solutions and conduct mathematical analysis on them. The validation and evaluation of the proposed solutions should include comparisons with state-of-the-art proposals. Data used in these evaluations is expected to come from the results of several participatory planned measurement campaigns.

Thesis context

This thesis is part of the 3M’Air multidisciplinary project, funded by the cluster of excellence IMU (LabEx Intelligence of Urban Worlds).  The 3M’Air project aims to study the potential of participatory crowd sensing to improve fine-grained knowledge of air quality and temperature while dealing with main scientific, technological, geographical and sociological issues. For that purpose, 3M’Air brings together the scientific and technical skills of three research laboratories: CITI (on wireless communications and data analysis), LMFA (on fluid mechanics and urban atmospheric dispersion models) EVS (on geographical and sociological issues) as well as five other operational partners: ATMO-Aura (the regional air quality observatory), Meteo France  (The French national meteorological service), le Grand Lyon (Greater Lyon urban community), Ville de Lyon (City of Lyon) and Lyon Meteo (a local company working on meteorological services).

The successful candidate will join the INRIA research group Agora located in Lyon, which is part of the CITI laboratory. The thesis will be mainly co-supervised by Dr. Walid Bechkit and Prof. Hervé Rivano of the CITI lab with a strong collaboration with the LMFA and the EVS laboratories.


Interested candidates should send a detailed CV with information on education, obtained degrees and qualification, as well as a cover letter detailing the motivation and scientific background of the candidate. Applications should also include the names and contact details of two referees.

Applications should be submitted by email to: cc hervé, a rolling deadline applies.

Some references

[1] M. Fiore, A. Nordio and C-F. Chiasserini, “Driving Factors Toward Accurate Mobile Opportunistic Sensing in Urban Environments”, IEEE Transactions on Mobile Computing, Vol. 15, pp. 2480–2493, 2016

[2] A. Marjovi, A. Arfire, and A. Martinoli, “Extending Urban Air Quality Maps Beyond the Coverage of a Mobile Sensor Network: Data Sources, Methods, and Performance Evaluation”, in proc. of EWSN, pp. 12-23, 2017.

[3] M. D. Adams and P. S. Kanaroglou “Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models”, Journal of environmental management, vol. 168, pp. 133-141, 2016.

[4] D. Hasenfratz, O. Saukh, C. Walser, C. Hueglin, M. Fierz, T. Arn, J. Beutela and L. Thielea, “Deriving high-resolution urban air pollution maps using mobile sensor nodes”, Pervasive and Mobile Computing, vol. 16, pp. 268-285, 2015.

[5] M. Mueller, D. Hasenfratz, O. Saukh, M. Fierz, and C. Hueglin, “Statistical modelling of particle number concentration in Zurich at high spatio-temporal resolution utilizing data from a mobile sensor network”, Atmospheric Environment, vol. 126, pp. 171-181, 2016.

[6] V. Singh, C. Carnevale, G. Finzi, E. Pisoni, and M. Volta, “A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations”, Environmental Modelling & Software, vol. 26, pp. 778-786, 2011.

[7] A. Tilloy, V. Mallet, D. Poulet, C. Pesin, and F. Brocheton, “BLUE-based NO2 data assimilation at urban scale”, Journal of Geophysical Research: Atmospheres,  vol. 118, pp 2031-2040, 2013.

[8] A. Boubrima, W. Bechkit, H. Rivano and L. Soulhac. “Leveraging the Potential of WSN for an Efficient Correction of Air Pollution Fine-Grained Simulations”, to appear in proc. of IEEE ICCCN 2018.

CITI Talk: “Recycler les ondes radio ambiantes pour connecter les objets”, Dinh-Thuy PHAN-HUY (Orange, Chatillon), 22 May (10h30 in TD-C)


Recycler les ondes radio ambiantes pour connecter les objets


o   Lors de l’édition 2017 du Salon de la recherche Orange, du 5 au 7 décembre (, Orange a réalisé, pour la première fois, une transmission de données sans fil,  effectuée grâce aux seules ondes déjà diffusées par… la tour Eiffel ! Aucune onde supplémentaire n’a été émise. Cette technologie dite de rétro-diffusion ambiente découverte par l’Université de Washington en 2013, a une sobriété énergétique exceptionnelle. Elle permet de fournir de nouveaux services sans dépenser plus en spectre et en puissance rayonnée, ouvre d’énormes possibilités en termes d’utilisation massive d’objets connectés pour les villes, les maisons et les usines intelligentes.

o   Aujourd’hui, pour la  première fois, le projet ANR SpatialModulation ( dirigé par Orange, tentera une démonstration en temps réel d’une communication utilisant les ondes TV de la Fourvière, entre un « émetteur » (qui n’émet pas) développé par Orange et un récepteur développé par l’Institut Langevin sur GNU Radio.

CITI Talk: “Optimization Algorithms for Solving Problems Arising from Large Scale Machine Learning”, Vyacheslav Kungurtsev (Czech Technical University, Prague), May 7th, at 2pm in “salle TD-C” ( Claude Chappe Building)


Optimization Algorithms for Solving Problems Arising from Large Scale Machine Learning


In the contemporary “big data” age, the use of Machine Learning models for analyzing large volumes of data has been instrumental in a lot of current technological development. These models necessitate solving very large scale optimization problems, presenting challenges in terms of developing appropriate solvers. In addition, especially for problems arising from Deep Neural Network architectures, the resulting problems are often nonconvex, and sometimes nonsmooth, giving additional difficulty.

In this talk I present the standard structural elements of this class of problems, and how these structures can be handled with appropriate parallel architectures. I discuss the state of the art in terms of optimization algorithms for this setting and summarize the prognosis for ongoing and future research.