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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Manickam, Selvakumar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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The fog computing for internet of things: review, characteristics and challenges, and open issues Al-Shareeda, Mahmood A.; Alsadhan, Abeer Abdullah; H. Qasim, Hamzah; Manickam, Selvakumar
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5555

Abstract

The internet of things (IoT) research envisions a world in which common place objects are linked to the internet and trade, store, process, and gather data from their surroundings. Due to their inherent resource limitations, IoT devices are typically unable to directly host application services, despite their increasing importance for facilitating the supply of data to enable electronic services. Since it can survive and work in tandem with centralized cloud systems and extends the latter toward the network edge, fog computing (FC) may be an appropriate paradigm to get around these restrictions. This paper reviews the overview of the IoT in terms of application and design parameters and FC. Meanwhile, this paper presents the architecture of fog computing for IoT (FC-IoT) in terms of communication, security, data quality, sensing and actuation management, codification, analysis, and decision-making. Additionally, this review provides several characteristics and challenges of FC-IoT. Finally, open issues for this paper have been discussed.
Proposed fog computing-enabled conceptual model for semantic interoperability in internet of things Nagasundaram, Devamekalai; Manickam, Selvakumar; Laghari, Shams Ul Arfeen; Karuppayah, Shankar
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5748

Abstract

Semantic interoperability has emerged as a key barrier amidst the major developments and challenges brought about by the rapid expansion of internet of things (IoT) applications. Establishing interoperability is essential for IoT systems to function optimally, especially across diverse organizations. Despite extensive research in achieving semantic interoperability, dynamic interoperability, a vital facet, remains inadequately addressed. This paper addresses this gap by presenting a fog-based conceptual model designed to facilitate dynamic semantic interoperability in IoT. The model incorporates a single-tier fog layer, providing the necessary processing capabilities to achieve this goal. The study conducts a comprehensive literature review on semantic interoperability, emphasizing latency, bandwidth, total cost, and energy consumption. Results demonstrate the proposed double skin façade (DSF) model’s remarkable 88% improvement in service delay over IoT-SIM and Open IoT, attributed to its efficient load-offloading mechanism and optimized fog layer, offering a 50% reduction in service delay, power consumption, and 86% reduction in network usage compared to existing approaches through data redundancy elimination via pre-processing at the fog layer.
Performance Comparison of Random Forest, Support Vector Machine and Neural Network in Health Classification of Stroke Patients Sari, Windy Junita; Melyani, Nasya Amirah; Arrazak, Fadlan; Anahar, Muhammad Asyraf Bin; Addini, Ezza; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 1: PREDATECS July 2024
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i1.1119

Abstract

Stroke is the second most common cause of death globally, making up about 11% of all deaths from health-related deaths each year, the condition varies from mild to severe, with the potential for permanent or temporary damage, caused by non-traumatic cerebral circulatory disorders. This research began with data understanding through the acquisition of a stroke patient health dataset from Kaggle, consisting of 5110 records. The pre-processing stage involved transforming the data to optimize processing, converting numeric attributes to nominal, and preparing training and test data. The focus then shifted to stroke disease classification using Random Forest, Support Vector Machines, and Neural Networks algorithms. Data processing results from the Kaggle dataset showed high performance, with Random Forest achieving 98.58% accuracy, SVM 94.11%, and Neural Network 95.72%. Although SVM has the highest recall (99.41%), while Random Forest and ANN have high but slightly lower recall rates, 98.58% and 95.72% respectively. Model selection depends on the needs of the application, either focusing on precision, recall, or a balance of both. This research contributes to further understanding of stroke diagnosis and introduces new potential for classifying the disease.
Comparative Analysis of Weather Image Classification Using CNN Algorithm with InceptionV3, DenseNet169 and NASNetMobile Architecture Models Wulandari, Vina; Sari, Windy Junita; Al-Sawaff, Zaid Husham; Manickam, Selvakumar
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1608

Abstract

Rapid weather changes have a significant impact on various aspects of human life, including social and economic development. Weather analysis traditionally relies on data from Doppler radar, weather satellites, and weather balloons. However, advancements in computer vision technology provide new opportunities to enhance weather prediction systems through image recognition and classification. Studies evaluating and comparing deep learning architectures for weather image classification remain limited.This research utilizes Convolutional Neural Networks (CNN) to classify weather images using three architectures: InceptionV3, DenseNet169, and NASNetMobile. The results show that InceptionV3 achieved 97.94% accuracy on training data, 92.34% on validation data, and 93.81% on test data. DenseNet169 achieved 98.09% accuracy on training data, 88.46% on validation data, and 92.33% on test data. NASNetMobile achieved 96.51% accuracy on training data, 87.82% on validation data, and 89.97% on test data. Based on these results, InceptionV3 is the optimal choice for weather classification due to its consistent performance.This research addresses the gap in evaluating CNN architectures for weather data and contributes to improving weather monitoring systems, early disaster warnings, and applications reliant on accurate predictions. These findings also provide a foundation for the development of advanced technologies in image analysis and weather forecasting in the future.
A deep learning approach to detect DDoS flooding attacks on SDN controller Bahashwan, Abdullah Ahmed; Anbar, Mohammed; Manickam, Selvakumar; Al-Amiedy, Taief Alaa; Hasbullah, Iznan H.
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1245-1255

Abstract

Software-defined networking (SDN), integrated into technologies like internet of things (IoT), cloud computing, and big data, is a key component of the fourth industrial revolution. However, its deployment introduces security challenges that can undermine its effectiveness. This highlights the urgent need for security-focused SDN solutions, driving advancements in SDN technology. The absence of inherent security countermeasures in the SDN controller makes it vulnerable to distributed denial of service (DDoS) attacks, which pose a significant and pervasive threat. These attacks specifically target the controller, disrupting services for legitimate users and depleting its resources, including bandwidth, memory, and processing power. This research aims to develop an effective deep learning (DL) approach to detect such attacks, ensuring the availability, integrity, and consistency of SDN network functions. The proposed DL detection approach achieves 98.068% accuracy, 98.085% precision, 98.067% recall, 98.057% F1-score, 1.34% false positive rate (FPR), and 1.713% detection time.
Enabling SECS/GEM in legacy equipment: a proof of concept Syahir Kamal Fitri, Muhammad; Manickam, Selvakumar; Ul Arfeen Laghari, Shams; Kok Chia, Siang; Khairi Ishak, Mohamad; Karuppayah, Shankar
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9516

Abstract

The rapid adoption of Industry 4.0 (I4.0) has driven the need for automated machine-to-machine (M2M) communication in manufacturing. However, legacy equipment remains a challenge due to its incompatibility with modern protocols like semiconductor equipment and materials international (SEMI) equipment communication standard/generic equipment model (SECS/GEM). Replacing these machines is costly, making retrofitting a more viable solution. This paper proposes a modular automation software framework that enables SECS/GEM integration for legacy machines without extensive hardware modifications. The system is implemented using Raspberry Pi and Arduino, acting as an intermediary between legacy equipment and modern factory networks. The framework facilitates real-time data exchange, remote monitoring, and enhanced automation while ensuring scalability and cost-effectiveness. Experimental evaluation demonstrates improved interoperability and reduced manual intervention. This solution provides a practical and adaptable approach to integrating legacy systems into (I4.0) environments.
Co-Authors Addini, Ezza Al-Amiedy, Taief Alaa Al-Sawaff, Zaid Husham Al-Shareeda, Mahmood A. Alsadhan, Abeer Abdullah Anahar, Muhammad Asyraf Bin Anbar, Mohammed Arrazak, Fadlan Bahashwan, Abdullah Ahmed H. Qasim, Hamzah Hasbullah, Iznan H. Karuppayah, Shankar Khairi Ishak, Mohamad Kok Chia, Siang Laghari, Shams Ul Arfeen Melyani, Nasya Amirah Nagasundaram, Devamekalai Sari, Windy Junita Syahir Kamal Fitri, Muhammad Ul Arfeen Laghari, Shams Wulandari, Vina