Accepted paper for presentation in AP-S/URSI 2020

Invasive Weed Optimization for Antenna Design: Case of 4G/5G Multi-band Antenna by Mira Bou Saleh, Elias A. Doumith, and Rémi Sarkis.

Abstract: 5G is the latest generation of mobile communication and promises considerably higher data rates, lower latency, and increased number of instantaneous connections. This could not be achieved without the use of antennas that operate on several frequencies including those already-used by the current 4G technology. In this paper, we propose a new approach for the design of 4G/5G antennas based on the Invasive Weed Optimization algorithm. We show that our proposed algorithm outperforms the built-in optimizer of the CST simulator when applied to several 4G/5G antenna designs.


Accepted paper for presentation in ISC2 2019

Dynamic AED Allocation and Reallocation for SCA Rescue Using Modified MCLP by Charbel Metrot, Rony Darazi, Abderrahim Benslimane, and Elias A. Doumith.

Abstract: Sudden Cardiac Arrest (SCA) is a condition in which the heart suddenly and unexpectedly stops beating. SCA usually causes death if it is not treated within minutes. A defibrillator is a device that can return the disorganized heart back into a normal rhythm by delivering a life-saving shock, while taking into consideration the critical time before complete damage of the brain occurs.
To minimize the out-of-hospital SCA consequences and optimize the coverage of the population, placement and access to Automated External Defibrillator (AED) in mission critical intervention, are the main objectives of our paper. The fast access to the nearest AED within a short delay is very crucial and important for increasing the survival rates. For this reason, our study in this paper consists in developing two new methods for the deployment of AEDs in a given area in order to cover the maximum number of existing population during different times of the day. We propose two new techniques for optimizing the coverage, inspired from the Maximal Covering Location Problem (MCLP) method called Real-Time to Destination MCLP (RTDMCLP) and Dynamic Real-Time to Destination MCLP (DRTDMCLP). Those two techniques show an improved result in term of covered population comparing to the MCLP method.


Accepted paper for presentation in IFIP Networking 2018

An M:N Shared Regenerator Protection Scheme in Translucent WDM Networks by Elias A. Doumith and Sawsan Al Zahr.

Abstract: Most studies addressing translucent network design targeted a tradeoff between minimizing the number of deployed regenerators and minimizing the number of regeneration nodes. The latter highly depends on the carrier’s strategy and is motivated by various considerations such as power consumption, maintenance and supervision costs. However, concentrating regenerators into a small number of nodes exposes the network to a high risk of data loss in the eventual case of regenerator pool failure. In this paper, we address the problem of survivable translucent network design taking into account the simultaneous effect of four transmission impairments. We propose an exact approach based on a mathematical formulation to solve the problem of regenerator placement while ensuring the network survivability in the hazardous event of a regenerator pool failure. For this purpose, for each accepted request requiring regeneration, we determine several routing paths along with associated valid wavelengths going through different regeneration nodes. In doing so, we implement an M:N shared regenerator protection scheme. Simulation results highlight the gain obtained by reducing the number of regeneration nodes without sacrificing network survivability.


Accepted paper for presentation in Globecom 2017

Advanced Demand Response Considering Modular and Deferrable Loads Under Time-Variable Rates by Sawsan Al Zahr, Elias A. Doumith, and Philippe Forestier.

Abstract: As the global energy policy is changing from a demand-driven to a supply-driven approach, demand side management (DSM) is becoming a key component of future energy systems. Indeed, it helps power grids’ operators to balance the demand for power with intermittent renewable energy sources such as wind and solar units. DSM consists in optimizing/adapting the power consumption to meet the production through various methods such as improving the energy efficiency by using better equipment and materials, implementing demand response (DR) solutions, etc. DSM mechanisms do not necessarily reduce the total power consumption, but reshape the consumption pattern. Hence, DSM is expected to reduce the need for investments in networks and power plants in order to meet peak demands. In this paper, we propose an advanced DR solution for individual households. Considering a household equipped with various domestic loads, we aim at optimally scheduling the day-ahead power consumption under time-variable rates while taking advantage of modular and deferrable loads, e.g. electric vehicle. For this purpose, we propose an exact approach to solve the problem of energy management within a household under both system’s and user’s constraints. Our proposal is numerically validated through real-life scenarios, elaborated using an existing simulator of human behavior regarding power consumption.


Accepted paper for presentation in GECON 2012

An Economic Agent Maximizing Cloud Provider Revenues Under a Pay-as-you-Book Pricing Model by Felipe Díaz Sánchez, Elias A. Doumith, Sawsan Al Zahr, and Maurice Gagnaire.

Abstract: The Cloud computing paradigm offers the illusion of infinite resources accessible to end-users anywhere at anytime. In such dynamic environment, managing distributed heterogenous resources is challenging. A Cloud workload is typically decomposed into advance reservation and on-demand requests. Under advance reservation, end-users have the opportunity to reserve in advance the estimated required resources for the completion of their jobs without any further commitment. Thus, Cloud service providers can make a better use of their infrastructure while provisioning the proposed services under determined policies and/or time constraints. However, estimating end-users resource requirements is often error prone. Such uncertainties associated with job execution time and/or SLA satisfaction significantly increase the complexity of the resource management.


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