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All Journal Jurnal Ilmiah Ilmu Terapan Universitas Jambi
Almufti, Saman M
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Education Engineering Industrial & Manufacturing Engineering

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SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING Ahmad, Hawar Bahzad; Asaad, Renas Rajab; Almufti, Saman M; Hani, Ahmed Alaa; Sallow, Amira Bibo; Zeebaree, Subhi R. M.
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 1 (2024): Volume 8, Nomor 1, June 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i1.32598

Abstract

In response to escalating energy consumption, particularly within the housing sector, a global imperative to reduce energy usage has emerged, propelling the concept of "smart houses" to the forefront of innovation. This paradigm shift owes its genesis to the convergence of advancements in energy conversion, communication networks, and information technology, catalyzing the emergence of the Internet of Things (IoT). The IoT facilitates seamless connectivity of devices via the World Wide Web, enabling remote management, monitoring, and detection capabilities. Capitalizing on this technological synergy, the integration of IoT, big data, and machine learning with home automation systems holds immense promise for enhancing energy efficiency. This paper introduces HEMS-IoT, a groundbreaking energy control system for intelligent homes, underpinned by big data analytics and machine learning algorithms, prioritizing security, convenience, and energy conservation. Leveraging J48 neural network technology and the Weka API, the study illuminates user behaviors and energy consumption patterns, enabling household classification based on energy usage profiles. Moreover, to ensure user comfort and safety, RuleML and Apache Mahout are deployed to customize energy-saving recommendations tailored to individual preferences. By presenting a practical demonstration of smart home monitoring, this paper validates the effectiveness of the proposed approach in enhancing security, comfort, and energy conservation. This pioneering research not only showcases the transformative potential of IoT-driven energy management systems but also sets the stage for a sustainable and interconnected future.
INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION Almufti, Saman M; Hani, Ahmed Alaa; Zeebaree, Subhi R. M.; Asaad, Renas Rajab; Majeed, Dilovan Asaad; Sallow, Amira Bibo; Ahmad, Hawar Bahzad
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 1 (2024): Volume 8, Nomor 1, June 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i1.32767

Abstract

In the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.
Co-Authors Ahmad, Hawar Bahzad Asaad, Renas Rajab Hani, Ahmed Alaa Majeed, Dilovan Asaad Sallow, Amira Bibo Zeebaree, Subhi R. M.