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All Journal International Journal of Engineering, Science and Information Technology
Ismail, Laith S.
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Edge Computing Frameworks for Real-Time Optimisation in Autonomous Electric Vehicle Networks Ismail, Laith S.; Jamil, Abeer Salim; Ali, Taghreed Alaa Mohammed; Al-Dosari, Ibraheem Hatem Mohammed; Salman, Khdier; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1397

Abstract

Autonomous electric vehicles (AEVs) require real-time decision-making, low-latency computation, and energy-aware coordination to operate effectively. Traditional centralised cloud computing struggles to meet these demands due to inherent delays and scalability issues in large-scale AEV networks. This paper proposes a novel hybrid edge–fog computing architecture to address these challenges. Our framework utilises a three-tier system (vehicle-edge, roadside-fog, and cloud) governed by a deep reinforcement learning agent that manages energy-aware task offloading. Extensive simulations demonstrate the framework's effectiveness, achieving significant end-to-end latency reductions of up to 56% during urban peak hours and decreasing energy consumption by 20% under high-load conditions. The deep reinforcement learning agent successfully adapts control policies to dynamic road conditions, while the architecture proves highly scalable and resilient, maintaining high task success rates and recovering from node failures in seconds. These findings confirm that a hybrid edge–fog architecture, guided by reinforcement learning, is a highly effective solution for scalable, adaptive, and energy-efficient AEV operations. This study's primary contribution is an empirically validated framework that uniquely integrates predictive control and energy-aware scheduling at the edge, providing a deployable model for next-generation intelligent transportation systems.
Artificial Intelligence, Robotics, and Automation in Renewable Energy Systems Ismail, Laith S.; Faraj, Lydia Naseer; Mohammed, Doaa Thamer; Taher, Nada Adnan; Hafedh, Milad Abdullah; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1746

Abstract

The transition to clean energy requires intelligent solutions to mitigate resource intermittency, grid instability, and operational inefficiencies. This paper presents and validates an integrated framework that leverages Artificial Intelligence (AI), robotics, and automation to optimize the performance and sustainability of renewable energy assets. The study employs machine learning models (LSTM, SVM, ANN) for energy forecasting, autonomous robotic platforms for real-time inspection, and advanced algorithms (MPC, Reinforcement Learning) for grid control. The framework's transparency and ethical compliance were validated using explainability techniques (SHAP, LIME) and cybersecurity protocols. Experimental results demonstrate significant performance gains across all domains. The AI models achieved high forecasting accuracy, with the LSTM model for wind power reaching a Mean Absolute Percentage Error (MAPE) of just 2.41%. Robotic inspections improved system uptime by nearly 30% and accelerated fault detection. In grid management simulations, a Reinforcement Learning-based control strategy proved most effective, reducing energy losses by 10.6% and control costs by 17.5%. This cross-disciplinary research illustrates the powerful synergy between intelligent software and advanced hardware in creating more reliable, efficient, and ethically grounded energy systems. The findings establish a scalable and validated foundation for next-generation renewable energy operations and highlight future pathways for enhancing human-machine collaboration in the pursuit of global sustainability targets.
Co-Authors Al-Dosari, Ibraheem Hatem Mohammed Ali, Taghreed Alaa Mohammed Faraj, Lydia Naseer Hafedh, Milad Abdullah Jamil, Abeer Salim Maidin, Siti Sarah Mohammed, Doaa Thamer Salman, Khdier Taher, Nada Adnan