Article Per Year (5 Year)
p-Index From 2021 - 2026
1.99
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All Journal IT JOURNAL RESEARCH AND DEVELOPMENT Journal of Information Systems and Informatics Studies in Learning and Teaching The Indonesian Journal of Computer Science
Isong, Bassey
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Other

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Computational Task Scheduling Across IoT-Edge-Fog-Cloud Continua: Algorithms, Adaptability and Research Gaps Isong, Bassey; Mamidza, Fulufhelo
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5104

Abstract

Internet of Things (IoT) deployments span heterogeneous infrastructure such as edge devices, fog nodes, and cloud servers, each with distinct computational capacity, energy constraints, and cost profiles. Scheduling across this three-tier stack requires satisfying four competing demands, including latency bounds, energy budgets, workload distribution, and cloud offloading cost. None of these can be optimized in isolation, and workload variability across deployment sites makes the problem even harder. In this paper, we review task scheduling strategies in edge-fog-cloud environments. It compares heuristic, metaheuristic, and machine learning-based approaches across deployment settings, adaptation capacity, and measured performance. Findings reveal metaheuristic methods reduce MK and energy consumption; learning-based approaches improve latency and task success rates, though under narrower conditions. Yet widespread reliance on simulation‑based evaluation and task-independence assumptions limits what these results actually demonstrate. Fixed objective weighting, unvalidated scalability, missing workflow dependency support, and static priority schemes each constrain deployment in practice. Future research should therefore prioritize shared or validated testbeds, workflow-aware/dependencies scheduling formulations, variable objective priorities, and scalability studies beyond small-to-medium topologies. Our study establishes a basis for designing scheduling strategies that hold under real deployment conditions across IoT, fog, and cloud applications and production settings.
Resource-Efficient Hybrid Ensemble ML Framework for Anomaly Detection in IoT Smart Homes Kgote, Otshepeng; Isong, Bassey
The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i2.5122

Abstract

The Internet of Things (IoT) technologies are used to support smart home systems through device and sensor connectivity for data exchange. However, the growth in adoption increases exposure to cyber-attacks and device faults, which puts system reliability and user safety at risk. This study proposes a framework that uses a pre-trained hybrid ensemble model to detect and separate attacks and faults while supporting timely mitigation. Firstly, the study evaluates models on the CICIoT2023 and IntelLab fault-injected datasets using ensemble learning methods and traditional supervised classifiers. Extreme Gradient Boosting shows the strongest intrusion detection performance. Random Forest shows the strongest fault detection performance. Secondly, both models were fine-tuned and combined within a hybrid meta-model. The results show high accuracy, strong F1 scores, and low false positive rates. The framework was implemented as a web application using Flask and Streamlit to support real-time simulations of attack, fault, and normal events. Evaluation reports latency under 5 seconds and memory use under 400 KB, which supports deployment on resource constrained IoT devices. It was optimized using quantisation and compression. The paper proposes a hybrid ensemble approach for joint fault and intrusion detection, a deployable prototype for constrained environments, and methods to enhance model performance.
Co-Authors Elegbeleye, Femi Igo, Ekoro Kgote, Otshepeng Lugayizi, Francis Mamidza, Fulufhelo Marivate, Vukosi Mbiada, Alain Mbiada, Alain Kabo Modupe, Abiodun Moeti, Kedibone Ndouvhada, Sedzani Ntshabele, Koketso Rananga, Seani Sebopelo, Rodney