Communication and Distributed Systems

Seminar Autumn Semester 2019

Organization

Prerequisite

Basic knowledge in computer networks as e.g., obtained in the bachelor lecture Computernetzwerke.

Target Group

BA/MSc students, Ph.D. students, and Postdoc of the group.

Content

The seminar is composed of presentations about current research topics that are investigated in the context of Ph.D./Postdoc research activities.

Remarks

  • The seminar is composed of presentations about current research topics that are investigated in the context of bachelor, master or Ph.D. works.
  • For BA/MSc students who need credits, they may take the seminar as „Proseminar“ or a Master seminar respectively. In this case, the students must give at least one talk and a topic must be agreed with the seminar supervisor beforehand. The participation of 75% of the seminar talks is also needed to qualify for ECTS points (For instance, with probably 12 seminars this will require 9 attended ones). Excuses for important reasons (according to article 23 RSL) are accepted, proof required in such case.
  • For each seminar you do not attend, you can compensate as follows: search for each seminar talk 3 journal or conference papers (in either IEEExplore or ACM digital library) that are related to the seminar talk. This means 6 papers for a normal seminar with 2 talks. Please summarize each paper on one page using Springer LNCS format. The report should be sent to Prof. Braun within 1 week after the last seminar talk.
  • Presenters may announce their preferred presentations dates to the coordinator. Presentation titles should be announced prior to the presentation. After the presentation, the presenters may forward their slides to the coordinator for publication on this website. Please use the Uni Bern PPT template to prepare your presentation.

  • Please limit seminar talks to 25-30 minutes such that there is enough time for discussion/questions (10-15 min).

  • It is recommended to all group members to attend the seminar to see what kind of research is done in the group.

Schedule of the Autumn Semester 2019

 Date Name  Title 
16.09.2019 Dr. Nhu Ngoc Dao

Balz Aschwanden
Multitier edge computing: From energy efficiency and system stability perspectives
Management of SDN/NFV based Mobile Networks
23.09.2019 Negar Emami
Hugo Santos
Human Trajectory Tracking By Smartphone-Based Pedestrian Dead Reckoning
Fog2Video: A Video Flow Management Mechanism based on Multi-tier Fog Computing with Quality of Experience Support
30.09.2019 Jose Carrera

Aamir Cheema
Ph.D. Thesis Defense rehearsal:
Indoor Positioning and Tracking Methods for Mobile Wireless Devices
Indoor Location-based Services
07.10.2019 No seminars
14.10.2019 Jakob Schaerer
Christoph Nötzli
Codeword Translation
Phone orientation prediction for tile-based live 360-video streaming
21.10.2019 No seminars
28.10.2019 Samuel Schwegler
Aless Esposito
Density prediction in urban areas using LSTM
Reinforcement learning designed CNN to estimate density of moving objects in urban areas
04.11.2019 Diego Oliveira
Dimitris Xenakis
FMEC-Enhanced Mobile Applications in Urban Environments
Placement Optimization of Nodes used in RSS-based Indoor Positioning: Maximizing Localization
11.11.2019 Alisson Medeiros
Mikael Gasparyan
An Elasticity Control Approach to Cloud-Network Slicing Defined-Systems
Ph.D. Thesis Defense rehearsal: Service-Centric Networking
18.11.2019 Mostafa Karimzadeh Traffic Flow Estimation by Applying High-Order Convolution Operators on Graph-Structured Data.
25.11.2019 Gaetano Manzo

Dave Meier
Floating Content Support for Software-defined Named-Data Vehicular Networks
A comparative study of route update strategies in SDN based WSN
02.12.2019 Emily Croxall
Luca Luceri
Machine Learning in Networks
Ph.D. Thesis Defense rehearsal:
The Abuse of Online Social Networks: Privacy Leakage and the Influence of Public Opinion
09.12.2019 Christoph Nötzli

Eirini Kalogeiton
Phone orientation prediction based on neural networks for tile-based live 360-video streaming
Applying SDN in NDN-VANETS: Can it improve the communication?
16.12.2019 Samuel Schwegler
Aless Esposito
Density prediction in urban areas using LSTM
Reinforcement learning designed CNN to estimate density of moving objects in urban areas