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Siti Sarah Maidin
International University
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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Energy-Aware Multimodal Biometric Authentication Systems for Mobile Yaser Issam Hamodi Aljanabi; Mohammed Fadhil Mahdi; Shahd Imad Hadi; Saif Kamil Shnain; Intesar Abbas; Siti Sarah Maidin
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1356

Abstract

As smartphones become central to personal identity verification, the need for secure, efficient, and power-conscious authentication methods is paramount. While multimodal biometric systems, combining features like face and fingerprint recognition, offer superior accuracy over unimodal approaches, their adoption on mobile platforms is severely hindered by high energy consumption and hardware variability. This paper introduces an energy-aware multimodal biometric authentication framework designed for Android smartphones that directly confronts this challenge. Our system features a novel adaptive fusion mechanism that intelligently balances recognition accuracy with power consumption by dynamically adjusting the weights of biometric modalities in real-time based on battery level and ambient environmental conditions. To validate our framework, we conducted an extensive experimental study involving 46 participants across 460 authentication sessions on five different smartphone models. The results demonstrate that our adaptive system significantly outperforms both unimodal and static fusion baselines. It achieves a high True Acceptance Rate (TAR) and a low Equal Error Rate (EER) while substantially reducing the Energy-Delay Product (EDP). A key feature is the system's ability to gracefully degrade to a secure, fingerprint-only mode when the battery is critically low, ensuring continuous availability without compromising security. This research proves that intelligent, context-aware modality adaptation is a viable strategy for creating robust, efficient, and sustainable biometric authentication solutions suitable for long-term use in consumer electronics.
Design and Deployment of a Secure Cyber-Physical System for Energy Monitoring in Smart Agriculture Dina Fallah; Elaf Sabah Abbas; Mohsen Ali Ahmed; Wafaa Adnan Sajid; Thamer Kadum Yousif Al Hilfi; Siti Sarah Maidin
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.v5i3.1398

Abstract

The growing need for sustainable agricultural practices has spurred the integration of cyber-physical systems (CPS) into modern farming. This paper presents the design, deployment, and evaluation of a modular CPS architecture for adaptive energy monitoring and control in smart agriculture. The system integrates environmental sensing, predictive modelling, and optimisation-guided actuation to enhance energy efficiency and operational resilience. Field tests on a 3-hectare site across six crop environments demonstrated significant performance gains, achieving energy savings of up to 25.8% and peak demand reductions of up to 19.8%. Our multi-layer architecture, featuring STM32 microcontrollers, LoRaWAN communication, and a cloud analytics dashboard, enables proactive control by anticipating energy demand using an LSTM-NARX predictive model. This approach reduced control actuation delay to 1.8 seconds and proved robust against cyber-physical faults, recovering from communication failures and data anomalies in under 15 seconds. The results validate that embedding energy-aware, predictive logic into CPS infrastructure creates scalable, efficient, and reliable agricultural solutions. We acknowledge limitations related to predictive model complexity and communication latency, and we propose future work focused on distributed CPS coordination, federated learning, and full lifecycle sustainability analysis to further advance intelligent, resource-efficient agriculture.
Swarm Intelligence Algorithms for Resource Allocation in Renewable-Powered Smart City Infrastructures Mustafa Nazar; Adil Abbas Majeed; Rafah Hassan Abdul Radhi; Qusay Mohammed Jafar; Baker Mohammed Khalil; Siti Sarah Maidin
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1355

Abstract

The increasing integration of renewable energy sources into urban systems necessitates the development of intelligent resource management strategies to ensure optimal and reliable power distribution. Swarm Intelligence (SI) algorithms have emerged as a promising solution for addressing the complex energy management challenges inherent in smart cities, such as generation variability, distributed loads, and the need for real-time decision-making. This paper conducts a rigorous comparative analysis of three prominent SI algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—within a simulated, renewable-powered smart city environment. Our model incorporates edge computing nodes, solar and wind generation systems, and heterogeneous urban load profiles, including residential, municipal, and electric vehicle charging demands. The study evaluates each algorithm against key performance metrics, including energy efficiency, task latency, convergence behavior, load balancing, and system fault tolerance. The results unequivocally demonstrate that PSO outperforms both ACO and ABC across most performance dimensions, exhibiting faster convergence, superior energy utilization, more effective latency management, and enhanced fault recovery capabilities. While ABC demonstrates competitive performance in flexibility and fairness, ACO shows significant limitations in time-sensitive and failure-prone scenarios. This research contributes a modular simulation framework suitable for real-time edge computing applications and offers practical guidance for deploying adaptive optimization strategies in urban energy systems. Ultimately, our findings underscore the critical importance of algorithm selection in smart city energy infrastructure and highlight the potential of swarm-based intelligence to enable scalable, resilient, and efficient resource management in the sustainable cities of the future.
Predictive Data Analytics for Fault Diagnosis and Energy Optimization in Industrial IoT Environments Dina Fallah; Bushra Jabbar Abdul-Kareem; Nada Mohammed Murad; Ammar Falih Mahdi; Ola Janan; Siti Sarah Maidin
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.v5i2.1392

Abstract

The fusion of predictive maintenance with energy optimization represents a critical advance for intelligent Industrial Internet of Things (IIoT) systems. In response to the growing industrial demand for highly reliable and efficient operations, this study introduces and validates a unified framework that couples fault diagnosis via deep learning with energy management via reinforcement learning. We utilize a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for multivariate fault detection, which demonstrates superior classification accuracy and robustness against data incompleteness. Simultaneously, a Deep Q-Network (DQN) performs dynamic energy scheduling based on predicted system health, achieving substantial energy reductions without compromising task deadlines. Extensive experimental results from real-world industrial datasets and simulations confirm the integrated framework's superiority over conventional approaches in both diagnostic precision and energy efficiency. Key performance indicators, including inference speed and cross-validation, affirm its suitability for real-time industrial applications. This work demonstrates that integrating predictive analytics into intelligent control paradigms is crucial for improving the reliability and sustainability of modern IIoT systems and offers a replicable blueprint for developing next-generation smart manufacturing solutions.
Edge Computing Frameworks for Real-Time Optimisation in Autonomous Electric Vehicle Networks Laith S. Ismail; Abeer Salim Jamil; Taghreed Alaa Mohammed Ali; Ibraheem Hatem Mohammed Al-Dosari; Khdier Salman; Siti Sarah Maidin
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.
Co-Authors Abeer Salim Jamil Adil Abbas Majeed Ammar Falih Mahdi Baker Mohammed Khalil Bushra Jabbar Abdul-Kareem Dina Fallah Elaf Sabah Abbas Ibraheem Hatem Mohammed Al-Dosari Intesar Abbas Khdier Salman Laith S. Ismail Mohammed Fadhil Mahdi Mohsen Ali Ahmed Mustafa Nazar Nada Mohammed Murad Ola Janan Qusay Mohammed Jafar Rafah Hassan Abdul Radhi Saif Kamil Shnain Shahd Imad Hadi Taghreed Alaa Mohammed Ali Thamer Kadum Yousif Al Hilfi Wafaa Adnan Sajid Yaser Issam Hamodi Aljanabi