nameJulien Gedeon
positionDoctoral Researcher
eMailgedeon (AT)tk(DOT)tu-darmstadt(DOT)de
phone+49 (6151) 16 - 23196
fax+49 (6151) 16 - 23202
officeS2|02 A124
postal addressTU Darmstadt - FB 20
FG Telekooperation
Hochschulstraße 10
D-64289 Darmstadt
Germany

 

 

Research Interests

Education

Since 03/2016: Doctoral researcher at Telecooperation Lab, TU Darmstadt

10/2015: M.Sc Computer Science, TU Darmstadt

03/2013: B.Sc Computer Science, TU Darmstadt

Project Work

Activity Lead for the EIT Digital Activity CC4BA - Certification Center for Business Acceleration for SDN and NFV

Conferences & Travel

  • 06/2017: ICDCS, Atlanta, GA (USA)  [undefinedPresentation]
  • 03/2017: Netsys, Göttingen (Germany) [undefinedPhD Forum Paper] [undefinedPoster]
  • 11/2016: Internetdagarna, Stockholm (Sweden)
  • 04/2016: EIT Digital Conference & Partner Event, Brussels (Belgium)
  • 10/2015: LCN, Clearwater Beach, FL (USA) [undefinedPresentation]

Publications

Understanding Spatial and Temporal Coverage in Participatory Sensor Networks

Author Julien Gedeon, Immanuel Schweizer
Date October 2015
Kind Inproceedings
Book titleLocal Computer Networks Conference Workshops (LCN Workshops)
KeyTUD-CS-2015-1237
Research Areas Telecooperation, - SUN - Smart Urban Networks
Abstract Sensor coverage is a well established problem in sensor networks. Most of the work is focused on optimizing coverage in stationary networks or by controlling the movement of mobile nodes (e.g. robots) in order to maximize their coverage. In participatory sensor networks, we are faced with noncontrollable mobility. Humans move freely and there is no central nor distributed algorithm that optimizes coverage. There is no work in literature yet that explores coverage in the context of non-controllable mobility. To this end, we report results of a study applying an adapted greedy coverage algorithm onto three different data sets. Given these datasets, we report results studying the effect of different mobility characteristics on the spatial and temporal coverage. Our results show that high coverage can be achieved by a relatively small subset of nodes. Also, given a real-world participatory sensing system, turn-around times are relevant for continuous temporal coverage.
Full paper (pdf)
[Export this entry to BibTeX]

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Teaching

WinterTerm 2017/2018

  • TK1: Distributed Systems - Programming & Algorithms
    Excercise organization

Summer Term 2017

  • Projektpraktikum Telekooperation

Winter Term 2016/2017

  • Seminar Telecooperation
    Advisor for the topics Blockchain beyond Cryptocurrencies, Offloading for Mobile Computing and Publish/Subscribe Protocols for the IoT
  • TK1: Distributed Systems - Programming & Algorithms
    Excercise organization
  • Bachelor Students Traineeship / Bachelorpraktikum
    Project owner "Visualisierung und Interaktion von SmartCity-Berechnungskomponenten"

Summer Term 2016

  • Seminar Telecooperation
    Advisor for the topic Northbound APIs for SDN

Theses

4 Entries found


15.05.2017

Cloudlet Coverage in Urban Spaces

Bachelor Thesis

in progress


The new paradigm of edge computing has proposed Cloudlets to offload data and computations from mobile, resource-constrained devices. In the context of future smart city applications, Cloudlets can offer a unique opportunity to provide locally available, low-latency services. However, deploying new infrastructures to host cloudlets for their widespread coverage is costly. This thesis will explore how existing access points in an urban area can be leveraged for the deployment of Cloudlets on a city-wide scale.

30.11.2017

Data Collection and Communication Patterns in Highly Mobile Urban Scenarios

Master Thesis

finished


The amount of data produced in urban areas is steadily increasing. This is due to the increasing capabilities of devices and the desire to provide citizen advanced services based on the analysis of such data. Data is transmitted from a producer or publisher to a gateway access node, where it is further processed by in-network processing topologies to extract information and act in a timely manner. Contrary to stationary sensors, today’s data producers are highly mobile - examples include mobile phones and cars. These two sources of data will be the primary focus of this thesis. With a variety of highly mobile data publishers and different kinds of data, the question arises which communication patterns should be used to transfer the data to gateway nodes. This becomes especially relevant when publishers do not have an a-priori knowledge of subscribers and messages have different priorities.

30.09.2017

V-Storage: A Virtual Storage Framework for Android

Bachelor Thesis

finished


Mobile devices such as smartphones today feature a variety of different apps, each one serving one particular purpose. Data captured or sent by those apps is usually stored at a distant server, i.e., in the cloud, but in any case at a location predetermined by that particular application.
Also, many users use different apps that serve the same or a similar purpose. The existing app landscape therefore hinders sharing data between different applications.
More importantly, the way these apps store and access data is completely decoupled to how the data is acutally used, what the current usage context and the user's intention is. In this thesis, we will provide a framework that abstracts from concrete storage locations and decides where to best place the data based on these factors. Furthermore, the emerging concept of Edge Computing provides an opportunity to place data close to the source, as to save latency and bandwidth in the core network.

31.10.2017

Scheduling Strategies for Apache Storm

Master Thesis

finished


Data Stream Processing (DSP) systems have emerged as a way for the timely processing of real-time data generated by a variety of sources. Continuous streams of data from these sources are passed through different operators, each of which performs some computations on the data. The operators are chained and together form the overall logic of a stream processing application.
With new concepts such as In-network processing or edge computing, more and more devices become available to process data in the core network or at the extreme edge of the network, rather than in distant cloud computing architectures. Given this scenario, the question arises where to place and schedule operators. Strategies for placement and scheduling can optimize for different metrics (e.g. latency, resource utilization, user-defined QoS etc.). Furthermore, the problem is NP-hard, so efficient heuristics are needed to solve it in reasonable time.
This thesis examines the problem using Apache Storm as a DSP. For this system, several schedulers have been proposed in literature, however, they each employ a different approach and have a different focus for optimization. This makes it very hard to compare them and identify means to efficiently solve the placement problem.


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