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Mostafa, Nour
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Electrical & Electronics Engineering

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Two-way differential strategy for wireless sensor networks Alabed, Samer; Alsaraira, Amer; Mostafa, Nour; Al-Rabayah, Mohammad; Shdefat, Ahmed; Zaki, Chamseddine; A. Saraereh, Omar; Al-Arnaout, Zakwan
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6121

Abstract

In this paper, a novel optimal two-way amplify and forward (AF) differential beamforming method for wireless sensor network is proposed. The proposed method is an advanced signal processing technique used to enhance the performance and reliability of the communication link by exploiting the diversity provided by multiple antennas. Unlike current state-of-the-art methods which require the knowledge of channel state information (CSI) at both transmitting and receiving antennas or at least at the receiving antennas, the suggested method does not need CSI at any transmitting or receiving antenna. Moreover, the proposed method enjoys high error performance with high diversity and coding gain and has a very low encoding and decoding complexity. Through our simulations, the proposed method is proved to outperform the best known non-coherent multi-antenna methods.
No binding machine learning architecture for SDN controllers Hosny Fouad Aly, Wael; Kanj, Hassan; Mostafa, Nour; Al-Arnaout, Zakwan; Harb, Hassan
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.8483

Abstract

Although software-defined networking (SDN) has improved the network management process, but challenges persist in achieving efficient load balancing among distributed controllers. Present architectures often suffer from uneven load distribution, leading to significant performance deterioration. While dynamic binding mechanisms have been explored to address this issue, these mechanisms are complex and introduce a significant latency. This paper proposes SDNCTRLML , a novel approach that applies machine learning mechanisms to improve load balancing. SDNCTRLML introduces a scheduling layer that dynamically assigns flow requests to controllers using machine learning scheduling algorithms. Unlike previous approaches, SDNCTRLML integrates with the standard SDN switches and adapts to different scheduling algorithms, minimizing disruption and network delays. Experimental results show that SDNCTRLML has outperformed static-binding controllers models without adding complexities of dynamic-binding systems.
Co-Authors A. Saraereh, Omar Al-Arnaout, Zakwan Al-Rabayah, Mohammad Alabed, Samer Alsaraira, Amer Harb, Hassan Hosny Fouad Aly, Wael Kanj, Hassan Shdefat, Ahmed Zaki, Chamseddine