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Der Campus im Frühling. Bildinformationen anzeigen

Der Campus im Frühling.

Foto: Universität Paderborn, Kamil Glabica.

Haitham Afifi, M.Sc.

Kontakt
Profil
Vita
Publikationen
 Haitham Afifi, M.Sc.

Rechnernetze

Wissenschaftlicher Mitarbeiter

Telefon:
+49 5251 60-5373
Büro:
O3.164
Sprechzeiten:

Di. 12:30 - 13:30 Uhr
oder bei Email

Web:
Besucher:
Pohlweg 51
33098 Paderborn
Projekts

Currently, I am involved in Project: Acousitic Sensor Networks

Research Area

My current research focuses on wireless networks in the following areas:

  • Network optimization and Resource allocation
  • Wireless Network Virtualization
  • Reinforcement learning
  • Wireless MAC protocols for adhoc networks

 

Studentische Arbeit und Projekte

Student Thesis: 

  • Bachelor (2017): Topology discovery in wireless ad hoc networks
  • Bachelor (2018): Task Placement in a Wireless Acoustic Sensor Network using a Genetic Algorithms
  • Bachelor (2019): Wireless Virtual Network Embedding using Reinforcement Learning
  • Master (2019): Distributed Virtual Network Embedding for Wireless Multi-Hop Networks

Student Projects:

  • Team project (SS2017): Distributing Acoustic Functions in Wiereless Sensor Networks
  • Team project (SS2018): WiNe for monitoring wireless adhoc networks
  • Team project (WS2019): FISSION --  Failover in wIreleSS dIstributed cOmputiNg

If you are intersted in doing your thesis in wireless related topics, please check the current open thesis or feel free to drop me an Email to discuss your interests. 

 Haitham Afifi, M.Sc.
02/2017 - heute

Research Associate, Computer Networks Group, Paderborn University

09/2015 - 02/2017

Network Engineer, Orange Business Services

10/2014 - 07/2015

Studentische Hilfskraft, Fraunhofer Heinrich Hertz Institute


Liste im Research Information System öffnen

2019

A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO

H. Afifi, H. Karl, S. Eikenberg, A. Mueller, L. Gansel, A. Makejkin, K. Hannemann, R. Schellenberg, in: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019), 2019

One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework.


A Genetic Algorithm Framework for Solving Wireless Virtual Network Embedding

H. Afifi, K. Horbach, H. Karl, in: 2019 International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (WiMob 2019), 2019

Given the recent development in embedded devices, wireless senor nodes are no longer limited to data collection but they can also do processing (e.g., smartphones). Accordingly, new types of applications take an advantage of the processing and flexibility provided by the wireless network. A common property between these applications is that the processing is not running on only one single node, but it is broken-down into smaller tasks that can run over multiple nodes, i.e., exploiting the in-network processing. We study a special variant of in-network processing, where the application is given by a graph; the processing tasks have predefined connections to be executed in a predefined sequence. The problem of embedding an application graph into a network is commonly known as Virtual Network Embedding (VNE). In this paper, we present a Genetic Algorithm (GA) solution to solve this wireless VNE problem, where we take into account the interference and multi-cast properties. We show that the GA has a good performance and fast execution compared to the optimization problem.


Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network Embedding

H. Afifi, H. Karl, in: 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019), IEEE, 2019


Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks

M. Guenther, H. Afifi, A. Brendel, H. Karl, W. Kellermann, in: 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019), 2019

By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations.


An Approximate Power Control Algorithm for a Multi-Cast Wireless Virtual Network Embedding

H. Afifi, H. Karl, in: 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC) (WMNC'19), 2019

Internet of Things (IoT) applications witness an exceptional evolution of traffic demands, while existing protocols, as seen in wireless sensor networks (WSNs), struggle to cope with these demands. Traditional protocols rely on finding a routing path between sensors generating data and sinks acting as gateway or databases. Meanwhile, the network will suffer from high collisions in case of high data rates. In this context, in-network processing solutions are used to leverage the wireless nodes' computations, by distributing processing tasks on the nodes along the routing path. Although in-network processing solutions are very popular in wired networks (e.g., data centers and wide area networks), there are many challenges to adopt these solutions in wireless networks, due to the interference problem. In this paper, we solve the problem of routing and task distribution jointly using a greedy Virtual Network Embedding (VNE) algorithm, and consider power control as well. Through simulations, we compare the proposed algorithm to optimal solutions and show that it achieves good results in terms of delay. Moreover, we discuss its sub-optimality by driving tight lower bounds and loose upper bounds. We also compare our solution with another wireless VNE solution to show the trade-off between delay and symbol error rate.


2018

MARVELO: Wireless Virtual Network Embedding for Overlay Graphs with Loops

H. Afifi, S. Auroux, H. Karl, Proc. of IEEE Wireless Communications and Networking Conference (WCNC), 2018


MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks

H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, H. Karl, in: Speech Communication; 13th ITG-Symposium, 2018, pp. 1-5

Signal processing in WASNs is based on a software framework for hosting the algorithms as well as on a set of wireless connected devices representing the hardware. Each of the nodes contributes memory, processing power, communication bandwidth and some sensor information for the tasks to be solved on the network. In this paper we present our MARVELO framework for distributed signal processing. It is intended for transforming existing centralized implementations into distributed versions. To this end, the software only needs a block-oriented implementation, which MARVELO picks-up and distributes on the network. Additionally, our sensor node hardware and the audio interfaces responsible for multi-channel recordings are presented.


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