Title: Three Branch Diversity Systems for Multi-Hop IoT Networks Date: May 4, 2022 at 7:30 pm Thesis Examination Committee: Abstract: Internet of Things (IoT) is an emerging technological paradigm connecting numerous smart objects for advanced applications ranging from home automation to industrial control to healthcare. The rapid development of wireless technologies and miniature embedded devices has enabled IoT systems for such applications, which have been deployed in a variety of environments. One of the factors limiting the performance of IoT devices is the multipath fading caused by reflectors and attenuators present in the environment where these devices are deployed. Leveraging polarization diversity is a well-known technique to mitigate the deep signal fades and depolarization effects caused by multipath. However, neither experimental validation of the performance of polarization diversity antenna with more than two branches nor the potency of existing antenna selection techniques on such antennas in practical scenarios has received much attention. The objectives of this dissertation are threefold. First, to demonstrate the efficacy of a tripolar antenna, which is specifically designed for IoT devices, in harsh environments through simulations and experimental data. Second, to develop antenna selection strategies to utilize polarized signals received at the antenna, considering the restrictions Plus d'infos
In the context of team Cornet’s seminars, Jannis Kurtz (University of Amsterdam) will present his research work on April 29, 2022, at 11:35 in the room S5.
In the context of team Cornet’s seminars, Lucas Potin (LIA) will present his research work on Anomaly Detection on Attributed Networks with GCN and autoencoder, on April 15, 2022, at 11:35 in the meeting room.
In the context of team Cornet’s seminars, Francesco De Pellegrini (LIA) will present his research work on Coflow Fair Scheduling via Dynamic Progress, on April 1, 2022, at 11:35 in the meeting room. Abstract : The average coflow completion time (CCT) is the standard performance metric in coflow scheduling. However, standard CCT minimization may introduce unfairness between the data transfer phase of different computing jobs. Static progress guarantees have been introduced in the literature to mitigate this fairness issue, but the trade-off between fairness and efficiency of data transfer seems hard to control. In this paper we introduce a new fairness framework for coflow scheduling based on the concept of slowdown to measure the performance degradation experienced by a coflow compared to isolation. This framework provides more flexible means to control the progress of coflows while minimizing the average CCT. We design an algorithmic solution chosen in the class of the sigma-order schedulers to solve the fair coflow scheduling problem in polynomial time. The algorithm is proved to be a 4-approximation w.r.t. an optimal scheduler. Our numerical results validate the proposed scheme and demonstrate that this approach can trade off average CCT for per-coflow slowdown.
In the context of team Cornet’s seminars, Rachid Elazouzi (LIA) will present his research work on Deadline-aware scheduling algorithm for coflows in datacenters, on March 3, 2022, at 11:35 in the meeting room.
In the context of team Cornet’s seminars, Rachid Elazouzi (LIA) will present his research work on Coflow scheduling in data centers: Background and motivation, on December 17, 2021, at 11:35 in the meeting room.
I will defend my HDR titled “Resource optimization and mobility management for wireless networks” on Thursday 16 December 2021 at 9:00 AM. For all those who want to attend, the presentation will be broadcast online at the following URL: https://bbb.univ-avignon.fr/b/had-gi3-dem
In the context of team Cornet’s seminars, Michael Poss (LIRMM) will present his research work on Optimization problems in graphs with location uncertainty on November 26, 2021, at 11:35 in the meeting room. Abstract: Many discrete optimization problems amount to select a feasible subgraph of least weight. We consider in this paper the context of spatial graphs where the positions of the vertices are uncertain and belong to known uncertainty sets. The objective is to minimize the sum of the distances in the chosen subgraph for the worst positions of the vertices in their uncertainty sets. We will present some of the results we obtained for these problems, including a numerical illustration on Steiner tree problems.
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