WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things

Eliviani, Rosa; Bandung, Yoanes

doi:10.5614/itbj.ict.res.appl.2023.17.3.4

Jurnal SINTA 2

WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things

Journal of ICT Research and Applications (JICTRA) · Vol. 17 No. 3 (2023) · pp. 336–355

DOI: 10.5614/itbj.ict.res.appl.2023.17.3.4
ISSN: 2338-5499, 2337-5787
Published: 2023-12-31
Publisher: DRI - ITB
Language: eng

Abstract

Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.

Keywords

Collaboration network of the authors

10 authors · 15 co-authorship links · 4 across different organisations

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Bandung Institute of Technology
University of Singaperbangsa Karawang
Institut Teknologi Nasional Bandung

Different organisations
Same organisation
Affiliation unknown
Thicker line = more joint articles · bigger circle = more articles

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Co-author	Organisation	Joint articles
Armein Z. R. Langi	Bandung Institute of Technology	2
Carmadi Machbub	Bandung Institute of Technology	1
Suhono Harso Supangkat	Bandung Institute of Technology	1
Adhi Rizal	University of Singaperbangsa Karawang	1
Gladina Desideria	Bandung Institute of Technology	1
Yedi Dermadi	Bandung Institute of Technology	1
Gina Purnama	Bandung Institute of Technology	1
Irma Amelia Dewi	Institut Teknologi Nasional Bandung	1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jictra/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en-US">WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things</dc:title>
	<dc:creator>Eliviani, Rosa</dc:creator>
	<dc:creator>Bandung, Yoanes</dc:creator>
	<dc:subject xml:lang="en-US">framework</dc:subject>
	<dc:subject xml:lang="en-US">Internet of Things (IoT)</dc:subject>
	<dc:subject xml:lang="en-US">multimedia</dc:subject>
	<dc:subject xml:lang="en-US">throughput prediction</dc:subject>
	<dc:subject xml:lang="en-US">wireless sensor network</dc:subject>
	<dc:description xml:lang="en-US">Accurate throughput predictions can significantly improve the quality of experience (QoE), where QoE denotes a network’s capacity to provide satisfactory service. By increasing the results of good throughput predictions, the best strategy can be planned for managing data transmission networks with the aim of better and faster data transmission, thereby increasing QoE. Consequently, this paper investigates how to predict the throughput of wireless sensor networks utilizing multimedia data. First, we conducted a comparative analysis of relevant prior research on the topic of throughput prediction in Multimedia Internet of Things (Multimedia IoT). We developed a throughput prediction framework for wireless sensor networks based on what we learned from these studies using machine learning. The Throughput Prediction Framework identifies historical throughput data and employs these traits to predict throughput. In the final phase, multiple camera nodes and local servers are utilized to test a framework for throughput prediction. Our analysis demonstrates that WSN-IoT predictions are quite precise. For a 1-second time breakdown, the average absolute percentage error for all investigated scenarios ranges from 1 to 8 percent.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2023-12-31</dc:date>
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	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 17 No. 3 (2023); 336-355</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
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	<dc:language>eng</dc:language>
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