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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Bidang Pendidikan Dasar Journal of Information Systems and Informatics The Indonesian Journal of Computer Science
Esan, Omobayo Ayokunle
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Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Cost-effective internet of things privacy-aware data storage and real-time analysis Elegbeleye, Femi Abiodun; Mbodila, Munienge; Esan, Omobayo Ayokunle; Elegbeleye, Ife
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp247-255

Abstract

It has been estimated that about 20 billion internet of things (IoT) devices are currently connected to the Internet. This has led to voluminous data generation which makes storaging, managing, and decision making on data to be challenging. Hence, exposes users’ privacy to be vulnerable to unauthorized people. To address these issues, this research proposed cost-effective storage for keeping and processing the IoT data in real-time. The proposed Fframework utilized a reliable hybridised data privacy model to protect the personal information of users. An empirically evaluation was done to identify the best models using data k-anonymity (KA), l-diversity (LD), t-closeness (TC), and differential privacy (DP). The performance evaluation of cloud computing and fog computing was done through simulations. The results obtained show that the combination of two data privacy models: differential privacy and k-anonymity models performed better than any individual model and any other combined models in the protection of users’ personal information. Lastly, fog computing was found to perform better than the cloud in terms of latency, energy consumption, network usage and execution time. In conclusion, the current study strongly recommends the use of hybridised privacy model of differential privacy (DP) and k-anonymity (KA) for the protection of IoT generated data privacy.
Optimization of Network Performance in Complex Environments with Software Defined Networks Mbodila, Munienge; Esan, Omobayo Ayokunle; Elegbeleye, Femi Abiodun
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.818

Abstract

Software-defined networks (SDN) have emerged as a promising approach to address the limitations of conventional networks. Its architecture can be implemented using either a single controller or multiple controllers. Although a single controller is inadequate for managing networks, deploying multiple controllers introduces the challenge of controller placement (CPP) in a network environment. To address these issues, this study presents a Software Defined Networks-Fault-Tolerant Method (SDN-FTM) where, in the event of a network failure, the SDN controller automatically reroutes traffic through an alternate, pre-configured network path, thereby maintaining uninterrupted service. The proposed SDN-FTM was tested and evaluated in real-time using Mininet simulation tools on a real-life small scale network data from tracking unit department in Walter Sisulu University (WSU), with a focus on performance measures such as latency and throughput. From the result obtained, the proposed method produced throughput and latency on Ryu with 2.15m/s and 18408m/s respectively. Furthermore, the findings indicate that Ryu controllers generally outperform OpenFlow controllers in terms of throughput, while OpenFlow controllers exhibit lower latency. The proposed method demonstrates significant improvements in network management by providing a robust solution for maintaining high network availability and performance in the presence of faults
An ensemble framework augmenting surveillance cameras for detecting intruder clusters as potential mobs Esan, Omobayo Ayokunle; Osunmakinde, Isaac O.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4557-4571

Abstract

Many developing nations around the world curtail crimes through video surveillance technology, but the crime rate is still high. This is compounded by short-staffed security operatives and a deficiency of security infrastructure to assist security operatives with knowledge-driven decision support systems in the low-resource constraint environment. In a public environment, it is challenging to detect intruder clusters accurately as potential mobs for early warning. Previous research investigated some classical techniques, but their recommendations were insufficient. This research develops a machine learning 3-tiers ensemble framework, which integrates gray level co-occurrence matrices (GLCM) principles to enhance the capabilities of surveillance cameras and security operatives to effectively discern and respond to potential mob formations. The University of California San Diego (UCSD) pedestrian datasets that are publicly available were used for the experiments. With an improved overall average precision of 0.98, recall of 0.98, and accuracy of 98.52% on the UCSD dataset, the suggested framework outperforms the widely used methods for the detection of intruder clusters. The reduction in computational time on processors showcases the framework's significant advancements as a promising solution for robust real-time threat assessment applications.
Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks Mbodila, Munienge; Esan, Omobayo Ayokunle; Elegbeleye, Femi
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i6.4417

Abstract

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.    
The use of WhatsApp as a knowledge sharing platform in education- student’s perceptions Mbodila, Munienge; Esan, Omobayo Ayokunle; Mbodila, Muhandji
Jurnal Bidang Pendidikan Dasar Vol 9 No 1 (2025): January
Publisher : Universitas Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jbpd.v9i1.11066

Abstract

This study explores students’ perceptions of using WhatsApp as a knowledge-sharing platform in a rural university in the Eastern Cape. A total sample of 52 students participated in the study, with data collected through interviews and focus group discussions. The research examines how WhatsApp facilitates collaborative learning and academic resource sharing in a resource-constrained environment. Thematic analysis revealed that students perceive WhatsApp as a valuable tool for real-time communication, enhancing peer engagement, and fostering group collaboration. Its accessibility and ease of use were highlighted as significant advantages, particularly in overcoming geographic and infrastructural barriers. However, students also identified challenges, such as distractions, data costs, and occasional misuse for non-academic purposes. Despite these limitations, participants acknowledged WhatsApp's role in creating a supportive and interactive learning environment. The findings offer insights for students and lecturers seeking to optimize the use of social media platforms for educational purposes in rural settings.
Co-Authors Elegbeleye, Femi Elegbeleye, Femi Abiodun Elegbeleye, Ife Mbodila, Muhandji Mbodila, Munienge Osunmakinde, Isaac O.