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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Journal of Power, Energy, and Control
Muhammed, Aniru Abudu
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Design and Implementation of a Microcontroller-Based Adaptive Four-Way Traffic Light Control System for Traffic Optimization Muhammed, Aniru Abudu; Gregory, Omoruyi; Aigbodion, Emmanuella Osose
Journal of Power, Energy, and Control Vol. 1 No. 2 (2024)
Publisher : MSD Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62777/pec.v1i2.34

Abstract

This paper presents the design and construction of a microcontroller-based four-way traffic light control system aimed at optimizing traffic flow by automatically adjusting signal timing based on traffic density at each intersection. The system is built around an Arduino ATmega328 microcontroller inter-faced with break beam infrared (IR) sensors (transmitters and receivers) and LED displays. The IR sensors are installed on both sides of the lanes at regulated intervals to detect traffic density. The system is powered by a 12V DC battery and a 5V, 3A power supply is provided using a buck converter IC (LM2596), which steps down the 12V from the battery to 5V, 3A. This 5V power is used to run the Arduino microcontroller and the Darlington pair ICs for current sinking and sourcing. As vehicles pass through the areas monitored by the IR sensors, the traffic density is measured for each opposing lane, allowing the system to determine which lane should be prioritized for traffic flow. The corresponding LED indicators are then activated accordingly.
Investigation and Analysis of End-User Service Integrity in LTE/5G Fixed Wireless Access Networks in Macrocellular Environments Muhammed, Aniru Abudu; Emagbetere, Joy Omoavowere
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 7 No. 2 (2026): INJIISCOM: VOLUME 7, ISSUE 2, DECEMBER 2026 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

Maintaining service integrity in fixed wireless access (FWA) networks requires reliable connectivity and thorough performance testing. This study compares LTE and 5G FWA in macrocellular environments by analyzing signal propagation, throughput, and latency. Field tests were conducted on post-production networks using 5G NR at 3.5 GHz and 2.5 GHz (43 dBm) and LTE at 3.5 GHz (31 dBm). The setup included customer premises equipment, measurement software, and a laptop, with all tests performed under Guaranteed Bit Rate conditions to ensure validity. Performance was evaluated using provisioned LTE and 5G SIMs with iPerf, QXDM, and Ping tools. Signal strength was measured against user terminal distance from the base station, while TCP traffic to an FTP server assessed uplink and downlink throughput under controlled conditions. The findings offer real-world insights to support improvements in FWA service quality and network reliability.
Physics-Informed Artificial Intelligence for Adaptive Wireless Channel Modelling in Fifth-Generation (5G) Networks Muhammed, Aniru Abudu; Muhammed, Hibah
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 2 (2025): December 2025
Publisher : Universitas Komputer Indonesia

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Abstract

Accurate wireless channel modeling is fundamental to the design and optimization of fifth-generation (5G) communication systems. Traditional geometry-based stochastic models (GBSMs) and empirical formulations, while effective in static environments, often fail to capture the nonlinear, non-stationary, and environment-dependent propagation behaviours inherent in modern multi-antenna and millimeter-wave systems. This study introduces a physics-informed AI hybrid framework that fuses physical propagation principles with deep learning architectures, enabling channel modeling that is interpretable, adaptive, and data-efficient. Using large-scale datasets including DeepMIMO, COST (Cooperation in Science and Technology) 2100, and New York University (NYU) Wireless, the model integrates Physics-Informed Neural Networks (PINNs) and Convolutional Neural Networks (CNNs) to simultaneously capture spatial, temporal, and frequency-domain relationships under realistic propagation environments. Reinforcement and federated learning layers enable real-time adaptation and decentralized training across multiple base stations while preserving data privacy. Experimental results demonstrate substantial improvements over benchmark models such as 3GPP (3rd Generation Partnership Project) TR 38.901, COST 2100, and QuaDRiGa  (QUAsi Deterministic RadIo Channel GenerAtor), achieving an RMSE of 1.72 dB and NMSE of –20.6 dB, corresponding to a 25–30% accuracy gain. Visual analyses of power delay profiles, residual error distributions, and spatial correlation maps confirm the model’s robustness and physical consistency. The proposed framework offers a scalable, interpretable, and adaptive paradigm for next-generation wireless channel modeling, paving the way toward intelligent, self-optimizing, and 6G-ready communication networks that bridge the gap between physics-based theory and AI-driven modeling.
Blockchain-Enabled Cryptography for Intelligent Healthcare Systems. Muhammed, Aniru Abudu; Muhammed, Hibah Imuentinyanose
International Journal of Research and Applied Technology (INJURATECH) Vol. 6 No. 1 (2026): June 2026 (Online First)
Publisher : Universitas Komputer Indonesia

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Abstract

The ongoing digital transformation of healthcaredriven by artificial intelligence (AI), Internet of Things (IoT), and cloud-based serviceshas led to unprecedented volumes of sensitive health data and novel pathways for care delivery and analytics. However, the highly distributed, heterogeneous and mission-critical nature of modern health systems exposes them to elevated risks of data breaches, tampering, and privacy-erosion. In this paper we present a comprehensive exploration of how blockchain technology, combined with advanced cryptographic frameworks, can serve as the cryptographic backbone of intelligent healthcare ecosystems. We present the architectural foundations, identity and access control models, smart contractenabled compliance, federated learning integration, interoperability and auditability mechanisms. We then examine practical case studies and evaluate performance implications. Finally, we discuss the technical, operational and regulatory challenges and highlight avenues for future researchincluding post-quantum cryptography and hybrid architectures. Our findings demonstrate that blockchain-enabled cryptography offers a promising pathway toward secure, privacy-preserving, interoperable and trustworthy healthcare systems but realising this potential requires careful design, standardisation, and empirical validation.
Co-Authors Aigbodion, Emmanuella Osose Emagbetere, Joy Omoavowere Gregory, Omoruyi Muhammed, Hibah Muhammed, Hibah Imuentinyanose