Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding
Ajani, Ayodeji Akeem
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Public Health Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
GPON and V-band mmWave in green backhaul solution for 5G ultra-dense network Ajani, Ayodeji Akeem; Oduol, Vitalice Kalecha; Adeyemo, Zachaeus Kayode
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp390-401

Abstract

Ultra-dense network (UDN) is characterized by massive deployment of small cells which resulted into complex backhauling of the cells. This implies that for 5G UDN to be energy efficient, appropriate backhauling solutions must be provided. In this paper, we have evaluated the performance of giga passive optical network (GPON) and V-band millimetre wave (mmWave) in serving as green backhaul solution for 5G UDN. The approach was to first reproduce existing backhaul solutions in Very Dense Network (VDN) scenario which served as benchmark for the performance evaluation for the UDN scenario. The best two solutions, GPON and V-band solutions from the VDN were then deployed in 5G UDN scenario. The research was done by simulation in MATLAB. The performance metrics used were power consumption and energy efficiency against the normalized hourly traffic profile. The result revealed that GPON and V-band mmWave outperformed other solutions in VDN scenario. However, this performance significantly dropped in the UDN scenariodue to higher data traffic requirement of UDN compared to VDN. Thus, it can be concluded that GPON and V-band mmWave are not best suited to serve as green backhaul solution for 5G UDN necessitating further investigation of other available backhaul technologies.
IoTLSDT: IoT Anomaly Detection Using a Novel Hybrid Method Ali, Dan; Orifama, Dagogo; Olaleye, Olatunde; Ibe, Benedict Onochie; Ajani, Ayodeji Akeem; Oyeleke, Oluseun Damilola
Proceedings of Universitas Muhammadiyah Yogyakarta Graduate Conference Vol. 5 No. 1 (2025): Fostering Gen Z for Sustainable Development and Renewable Energy
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/grace.v5i1.686

Abstract

The increasing integration of smart devices into daily life has made the Internet of Things (IoT) essential across sectors such as manufacturing, transportation, healthcare, and smart homes. While IoT offers substantial benefits in automation and real-time monitoring, its pervasive connectivity exposes networks to significant security threats. Timely detection of anomalies is therefore critical to ensuring system resilience. This study presents IoTLSDT, a novel hybrid anomaly detection model that combines the temporal learning strengths of Long Short-Term Memory (LSTM) networks with the interpretability of Decision Trees. The model was trained and evaluated on three diverse and publicly available IoT datasets, including CICIoT2024, DAD, and IoT-23, which cover various attack types and traffic behaviours. Unlike existing methods, IoTLSDT utilises SoftMax probability outputs from the LSTM as input features for the Decision Tree, enhancing both performance and explainability. Experimental results demonstrate that IoTLSDT consistently outperforms conventional machine learning models, achieving classification accuracies ranging from 86% to 99% across all datasets. These results suggest that the proposed model is a robust and scalable solution for real-time anomaly detection in heterogeneous IoT environments.
Co-Authors Ali, Dan Ibe, Benedict Onochie Oduol, Vitalice Kalecha Olaleye, Olatunde Orifama, Dagogo Oyeleke, Oluseun Damilola Zachaeus Kayode Adeyemo