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