Department of Computer Engineering and Computational Sciences

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    Neural Networks Architecture for COVID-19 Early Detection
    ( 2021) Zgheib, Rita ; Chahbandarian, Ghazar ; Kamalov, Firuz ; Labban, Osman El
    Coronavirus fight seems far from being won. Governments are trying to balance the necessity to enforce restrictions on travel outside the home and the impact of these restrictions on the economy. Healthcare workers are overloaded, a considerable number of unnecessary and costly PCR tests are performed to serve as a certificate to go to work. At this stage, going back to everyday life safely requires the companies and public places to adopt AI-based solutions to assist the public authorities and the hospitals with the COVID detection. The most important issue that we tackle in this paper is the prediction to be very accurate. As a result, we propose an AI system based on Neural Networks (NN) method to predict whether a person has caught COVID19 disease or not. In this study, we used a real data set of 9416 patients tested for COVID19 at a hospital in Dubai. After training the NN model, the average error function of the neural network was equal to 0.01, and the accuracy of the prediction of whether a person has COVID or not was 97.6%. © 2021 IEEE.
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    Intercept Probability and Secrecy Capacity Analysis of RIS-based Wireless Communication with Full-duplex receiver
    (Institute of Electrical and Electronics Engineers Inc., 2021) Zaghdoud, Oussama ; Mnaouer, Adel Ben ; Boujemaa, Hatem
    In this paper, we investigate the physical layer security of a wireless communication with a reconfigurable intelligent surface (RIS) and a full-duplex (FD) legitimate receiver over Rayleigh fading channel in the presence of an eavesdropper. In addition, we examine the scenario where the FD receiver is sending a jamming signal while receiving the required message to confuse the eavesdropper. We derive closed form expressions of the intercept probability of two scenarios with and without jamming signal and we also use the strictly positive secrecy capacity (SPSC) metric to gain more insight on the security of our proposed system. To illustrate the analytical analysis, numerical results are provided under different network parameters to study the effect of RIS and jamming technique on the security of FD receiver. © 2021 IEEE
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    AI-based Energy Model for Adaptive Duty Cycle Scheduling in Wireless Networks
    (Institute of Electrical and Electronics Engineers Inc., 2021) Charef, Nadia ; Mnaouer, Adel Ben ; Bouachir, Ouns
    The vast distribution of low-power devices in IoT applications requires robust communication technologies that ensure high-performance level in terms of QoS, light-weight computation, and security. Advanced wireless technologies (i.e. 5G and 6G) are playing an increasing role in facilitating the deployment of IoT applications. To prolong the network lifetime, energy harvesting is an essential technology in wireless networks. Nevertheless, maintaining energy sustainability is difficult when considering high QoS requirements in IoT. Therefore, an energy management technique that ensures energy efficiency and meets QoS is needed. Energy efficiency in duty cycling solutions needs novel energy management techniques to address these challenges and achieve a trade-off between energy efficiency and delay. Predictive models (i.e., based on AI and ML techniques) represent useful tools that encapsulate the stochastic nature of harvested energy in duty cycle scheduling. The conventional predictive model relies on environmental parameters to estimate the harvested energy. Instead, Artificial Intelligence (AI) allows for recursive prediction models that rely on past behavior of harvested and consumed energy. This is useful to achieve better precision in energy estimation and extend the limit beyond predictive models directed solely for energy sources that exhibit periodic behavior. In this paper, we explore the usage of a ML model to enhance the performance of duty cycle scheduling. The aim is to improve the QoS performance of the proposed solution. To assess the performance of the proposed model, it was simulated using the INET framework of the OMNet++ simulation environment. The results are compared to an enhanced IEEE 802.15.4 MAC protocol from the literature. The results of the comparative study show clear superiority of the proposed AI-based protocol that testified to better use of energy estimation for better management of the duty cycling at the MAC sublayer. © 2021 IEEE.
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    Enhanced Fuzzy logic-based Cluster Stability in Vehicular ad hoc Network
    (Institute of Electrical and Electronics Engineers Inc., 2021) Aissa, Mohamed ; Bouhdid, Badia ; Mnaouer, Adel Ben
    VANET nodes are characterized by their high mobility and they exhibit different mobility patterns. Therefore, VANET clustering schemes should take into consideration the mobility parameters among neighboring nodes to produce relatively stable clustering structure. This paper proposes a novel cluster head selection Fuzzy Logic-based k-hop distributed clustering scheme for VANETs. This scheme considers the safe inter-distance between vehicles as one of important metrics for cluster head selection. Our contribution deals better with the scalability and stability issues of VANETs and can achieve a high stable cluster topology compared to other schemes. These features make the proposed scheme more suitable for VANETs networks. Simulation proves the effectiveness of the new proposed scheme to create stable clusters by reducing re-clustering overhead and prolonging cluster lifetime compared to other existing clustering schemes for VANET. © 2021 IEEE.
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    Effect of Power Saving Techniques on the Quality of VoIP
    (Springer Science and Business Media B.V., 2017) Alakhras, Mohammad Adnan
    Power saving techniques in wireless networks have an effect on the quality of VoIP applications. These techniques are used to reduce the consumption of power when mobile terminals are used for VoIP. This paper will introduce a study on the effect of different techniques (used to reduce power consumption at various layers of the wireless network) on voice quality of service (QoS). The study will concentrate on jitter and delay of voice packets. Current standards for power saving techniques, at each network layers, will be discussed, and their effectiveness will be analyzed. WLAN is used in this study to fully analyze the effect of these power saving techniques on the voice packets jitter and delay. Simulation results are presented to demonstrate the analysis. © 2017, Springer International Publishing Switzerland.