Alia GHADDAR

Alia GHADDAR

Co-Advisor at Lebanese university

Location
Lebanon
Industry
Research

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Alia GHADDAR's Overview

Current
  • Campus coordinator and advisor at Lebanese International University
  • Instructor at Lebanese International University
  • Co-Advisor at Lebanese university
Past
Education
  • Lebanese University
Connections

54 connections

Websites

Alia GHADDAR's Experience

Campus coordinator and advisor

Lebanese International University

Educational Institution; 501-1000 employees; Higher Education industry

September 2012Present (2 years 1 month) Lebanon

Educational Institution; 501-1000 employees; Higher Education industry

2012Present (2 years) Lebanon

- OOP using Java.
- Introduction to programming (Java).
- Introduction to computers.
- Software Engineering.
- Database systems.
- Data Structure.

Co-Advisor

Lebanese university

Educational Institution; 1001-5000 employees; Higher Education industry

2012Present (2 years) Lebanon

1) Co-Advisor for Master2 thesis (2012 - 2013)
Thesis intitled: "MAS: A New Floating Point Compression Algorithm for Wireless Sensor Networks".

2) Co-Advisor for Master2 thesis (2011 - 2012)
Thesis intitled: "In-sensor data processing in the wireless sensor networks".

Instructor

Islamic University Of Lebanon

20062012 (6 years)

I was instructor at the faculty of engineering and the faculty of Sciences for the following courses: Computer graphics(OpenGL), 'Networking Essentials', computer security, Operating system.

Public Company; 1001-5000 employees; Higher Education industry

December 2008November 2011 (3 years) INRIA Lille - Nord Europe

PhD student in POPS-project (System and Networking for Portable Objects Proved to be Safe). Internship: University of Lille 1 and the Lebanese University

Lab assistant

Lebanese University

Educational Institution; 1001-5000 employees; Higher Education industry

January 2006June 2011 (5 years 6 months)

I worked as lab assistant in the Computer Science department at the Lebanese University-Faculty of Sciences, Branch I, for the following courses:
OpenGL, programmation web (php&MYSQL, Javascript, HTML, XML), programmation imperative II en C++, programmation avancée en Java, Bureautique, Systèmes Informatiques I.

Educational Institution; 501-1000 employees; Higher Education industry

20062010 (4 years)

Instructor at the Lebanese International University (LIU) - Lebanon, for the following courses: C++, Computer Graphics (OpenGL), introduction to computers.

Web developer

Lebanese University

Educational Institution; 1001-5000 employees; Higher Education industry

20052009 (4 years)

Web Developer (Database analysis, intranet sites design & developpement) at the "ACC" (Administration Computer Center) of the Lebanese University, Faculty Of Sciences, Branch I; using PHP, MS SQL, C# & asp.Net.

Information Technology and Services industry

20042005 (1 year)

I was web developer trainee at the Software Development Department of the ACT (Automation and Computer Technologies), Hamra, Beirut, Lebanon.

Alia GHADDAR's Skills & Expertise

  1. Java

Alia GHADDAR's Languages

  • Arabic

    (Native or bilingual proficiency)
  • English

  • French

Alia GHADDAR's Publications

  • Algorithm for temporal anomaly detection in WSNs

    • IEEE Wireless Communication and Networking Conference (WCNC'11)
    • March 28, 2011
    Authors: Alia GHADDAR, Tahiry Razafindralambo, Isabelle Simplot-Ryl, Samar Tawbi, Abbas Hijazi

    Knowledge discovery and data analysis in resource constrained wireless sensor networks faces different challenges. One of the main challenges is to identify misbehaviors or anomalies with high accuracy while minimizing energy consumption in the network. In this paper, we extend a previous work of us and we present an algorithm for temporal anomalies detection in wireless sensor networks. Our experiments results show that our algorithm can efficiently and accurately detect anomalies in sensor measurements. It also produces low false alarm rate for slow variation time series measurements without harvesting the source of energy.

  • Algorithm for data similarity measurements to reduce data redundancy in Wireless Sensor Networks

    • IEEE International Symposium on Wireless Sensor, Actuator and Robot Networks (WiSARN), World of Wireless Mobile and Multimedia Networks (WoWMoM)
    • June 14, 2010
    Authors: Alia GHADDAR, Tahiry Razafindralambo, Isabelle Simplot-Ryl, Samar Tawbi, Abbas Hijazi

    Extending the lifetime of wireless sensor networks remains the most challenging and demanding requirement that impedes large-scale deployments. The basic operation in WSNs is the systematic gathering and transmission of sensed data to a base station for further processing. During data gathering, the amount of data can be large sometimes, due to redundant data combined from different sensing nodes in the neighborhood. Thus the data gathered need to be processed before being transmitted, in order to detect and remove redundancy, which can impact the communication traffic and energy consumption of the network in a negative way. In this paper, we propose an algorithm to measure similarity between the data collected toward the base station(relative to a specific event monitoring), so that an aggregator sensor sends a minimum amount of information to the base station in a way that the latter can deduce the source information of sensing neighbors nodes. Further, our experimental results demonstrate that the communication traffic and the number of bits transmitted can be minimized while preserving accuracy on the base station estimations.

  • Towards Energy-Efficient Algorithm-Based Estimation in Wireless Sensor Networks

    • On Sixth International Conference Mobile Ad-hoc and Sensor Networks (MSN)
    • December 20, 2010
    Authors: Alia GHADDAR, Tahiry Razafindralambo, Isabelle Simplot-Ryl, David Simplot-Ryl, Samar Tawbi

    A primary purpose of sensing in a sensor network is to collect and aggregate information about a phenomenon of interest. The batteries on today's wireless sensor barely last a few days, and nodes typically expend a lot of energy in computation and wireless communication. Hence, the energy efficiency of the system is a major issue. Different representative mechanisms has been proposed to achieve a long lived sensors such as “clustering mechanisms” as well as Aggregation techniques to reduce the amount of data communication generated by sensors. Depending on the data type, ARMA series and forecasting are possible ways to reduce data transmission. In this work, we adopt single-hop clustering mechanism where all sensor nodes in a cluster communicate with their Cluster-Head (or sink) via single hop (such as In/On body sensors for personal health monitoring,..). We propose different data aggregation algorithms based on the AutoRegressive model, to predict local readings and reduce the communication traffic. We evaluate the performance of our work in terms of communication cost and energy consumption. We also extend our work to enhance the prediction accuracy by estimating dynamic prediction threshold. Our simulation shows that depending on data type, communication overhead and rate can be reduced and a considerable accuracy prediction can be obtained.

  • Algorithmes pour l'estimation des données dans les réseaux de capteurs

    • 11ème édition des « Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications(Algotel)
    • May 2009
    Authors: Alia GHADDAR, Tahiry Razafindralambo,, Isabelle Simplot-Ryl, David Simplot-Ryl, Samar Tawbi

    Abstract: La collecte des données est un des enjeux majeurs dans les réseaux de capteurs. En effet, les communications induites par la transmission de données réduisent considérablement la durée de vie du réseau. Une des techniques utilisées pour réduire la quantité de données transférées est l'agrégation et selon le type des données étudiées, une des possibilités est l'utilisation de série temporelle ARMA. Dans cet article, nous proposons quatre algorithmes d'agrégation de données s'appuyant sur le modèle AR permettant ainsi la diminution de la consommation d'énergie dans les réseaux de capteurs et augmentant la durée de vie de ceux-ci.

  • Investigating Data Similarity and Estimation through Spatio-Temporal Correlation to enhance Energy Efficiency in WSNs

    • Ad Hoc & Sensor Wireless Networks (AHSWN) journal (to appear)
    • 2011
    Authors: Alia GHADDAR, Tahiry Razafindralambo, Isabelle Simplot-Ryl, David Simplot-Ryl, Samar Tawbi, Abbas Hijazi
  • Investigating Data Similarity and Estimation through Spatio-Temporal Correlation to enhance Energy Efficiency in WSNs.

    • International Journal of Ad Hoc & Sensor Wireless Networks
    • 2012
    Authors: Alia GHADDAR, Tahiry Razafindralambo, Isabelle Simplot-Ryl,, David Simplot-Ryl, Samar tawbi, Abbas Hijazi

    Vol. 16, pp. 273-295

  • MAS: A New Floating Point Compression Algorithm for Wireless Sensor Networks.

    • Ocean & Coastal Observation : Sensors and observing systems, numerical models & information Systems (OCOSS 2013)
    Authors: maher alassi, Alia GHADDAR, samar tawbi, ali jaber, rami tawil

    (under publishing).

  • Resource-efficient floating-point data compression using MAS in WSN

    • October 2013
    Authors: Maher Assi, Alia GHADDAR, ,

    International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC), Vol.4, No.5, Pages 13-28

Alia GHADDAR's Education

Lebanese University

Bachelor, Master1 and Master2 degrees, Computer Science

20012005

Alia GHADDAR's Additional Information

Websites:
Interests:

Data communication in Wireless Sensor and Actuator Networks. Knowledge discovery and QoS in Wireless Sensor Networks. Mobile sensors. Internet Of Things

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