CITI seminar – Howard Yang (Zhejiang University, China) – 26/11 at 14:00

Title: Spatiotemporal Modeling of Wireless Networks

Date and Place: 26 / 11 / 2020 14:00 –

Speaker: Howard Yang (Zhejiang University, China)

Host: Maracas



The rapid growth of wireless applications has brought along new challenges for the next generation network, which is expected to manage a massive number of devices in real-time under a highly dynamic environment. To give an adequate response, it is of necessity to develop an analytical model with which designers can build intuitions, grasp insights, and identify critical issues. In this talk, I will describe a framework for the analysis of large-scale wireless networks in which the transceivers interact with each other through the interference they caused and hence are correlated in both space and time. The analysis straddles stochastic geometry and queueing theory to cope with the issues of spatially interacting queues, and arrive at handy expressions for the SINR distribution. As a result, a wide variety of systems/architecture can be devised based on this theoretical foundation. Specifically, I will demonstrate how to adopt such a mathematical model to the analysis of two particular network metrics, i.e., the packet delay and age of information, and the subsequent network deployment guidelines based on the analytical results.



Howard Hao Yang received the B.Sc. degree in Communication Engineering from Harbin Institute of Technology (HIT), China, in 2012, and the M.Sc. degree in Electronic Engineering from Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2013. He earned the Ph.D. degree in Electrical Engineering from Singapore University of Technology and Design (SUTD), Singapore, in 2017, and stayed three years as a postdoc. He is now an assistant professor with the ZJU-UIUC Institute, Zhejiang University. Dr. Yang’s background also features appointments at the Princeton University in 2018 – 2019, and the University of Texas at Austin in 2015 – 2016. His research interests cover various aspects of wireless communications, networking, and signal processing, currently focusing on the modeling of next-generation wireless networks, age of information, and federated learning.


PhD Defence: “Étalonnage in situ de l’instrumentation bas coût pour la mesure de grandeurs ambiantes : méthode d’évaluation des algorithmes et diagnostic des dérives”, Florentin Delaine, 4th of December 2020 at 10:30AM

The defense will be streamed live here: link



Étalonnage in situ de l’instrumentation bas coût pour la mesure de grandeurs ambiantes : méthode d’évaluation des algorithmes et diagnostic des dérives


In various fields going from agriculture to public health, ambient quantities have to be monitored in indoors or outdoors areas. For example, temperature, air pollutants, water pollutants, noise and so on have to be tracked. To better understand these various phenomena, an increase of the density of measuring instruments is currently necessary. For instance, this would help to analyse the effective exposure of people to nuisances such as air pollutants. The massive deployment of sensors in the environment is made possible by the decreasing costs of measuring systems, mainly using sensitive elements based on micro or nano technologies. The drawback of this type of instrumentation is a low quality of measurement, consequently lowering the confidence in produced data and/or a drastic increase of the instrumentation costs due to necessary recalibration procedures or periodical replacement of sensors. There are multiple algorithms in the literature offering the possibility to perform the calibration of measuring instruments while leaving them deployed in the field, called in situ calibration techniques.

The objective of this thesis is to contribute to the research effort on the improvement of data quality for low-cost measuring instruments through their in situ calibration. In particular, we aim at 1) facilitating the identification of existing in situ calibration strategies applicable to a sensor network depending on its properties and the characteristics of its instruments; 2) helping to choose the most suitable algorithm depending on the sensor network and its context of deployment; 3) improving the efficiency of in situ calibration strategies through the diagnosis of instruments that have drifted in a sensor network. Three main contributions are made in this work. First, a unified terminology is proposed to classify the existing works on in situ calibration. The review carried out based on this taxonomy showed there are numerous contributions on the subject, covering a wide variety of cases. Nevertheless, the classification of the existing works in terms of performances was difficult as there is no reference case study for the evaluation of these algorithms. Therefore in a second step, a framework for the simulation of sensors networks is introduced. It is aimed at evaluating in situ calibration algorithms. A detailed case study is provided across the evaluation of in situ calibration algorithms for blind static sensor networks. An analysis of the influence of the parameters and of the metrics used to derive the results is also carried out. As the results are case specific, and as most of the algorithms recalibrate instruments without evaluating first if they actually need it, an identification tool enabling to determine the instruments that are actually faulty in terms of drift would be valuable. Consequently, the third contribution of this thesis is a diagnosis algorithm targeting drift faults in sensor networks without making any assumption on the kind of sensor network at stake. Based on the concept of rendez-vous, the algorithm allows to identify faulty instruments as long as one instrument at least can be assumed as non-faulty in the sensor network. Across the investigation of the results of a case study, we propose several means to reduce false results and guidelines to adjust the parameters of the algorithm. Finally, we show that the proposed diagnosis approach, combined with a simple calibration technique, enables to improve the quality of the measurement results. Thus, the diagnosis algorithm opens new perspectives on in situ calibration.



  • M. Jean-Luc Bertrand-Krajewski, Professeur des Universités, Université de Lyon, INSA Lyon, DEEP (Rapporteur)
  • M. Romain Rouvoy, Professeur des Universités, Université de Lille, Spirals (Rapporteur)
  • Mme Nathalie Redon, Maître de conférences, IMT Lille Douai, SAGE (Examinatrice)
  • M. Gilles Roussel, Professeur des Universités, Université du Littoral Côte d’Opale, LISIC (Examinateur)
  • Mme Bérengère Lebental, Directrice de recherche, Institut Polytechnique de Paris, École Polytechnique, LPICM (Directrice de thèse)
  • M. Hervé Rivano, Univeristé de Lyon, INSA Lyon, CITI Lab (Co-directeur de thèse)
  • M. Éric Peirano, Directeur général adjoint en charge de la R&D, Efficacity (Invité)
  • M. Matthieu Puigt, Maître de conférences, Université du Littoral Côte d’Opale, LISIC (Invité)