Melola Discovery

← Melola Discovery

Journal SINTA 2

JATO: Deep Reinforcement Learning-based Joint Optimization for Task Offloading and Adaptive Transmission in Multimedia IoT Systems

Journal of ICT Research and Applications (JICTRA) · Vol. 20 No. 2 (2026) · pp. 205–218

DOI
10.5614/itbj.ict.res.appl.2026.20.2.6
ISSN
2338-5499, 2337-5787
Published
2026-08-31
Publisher
DRI - ITB
Language
eng

Abstract

As the Multimedia Internet of Things (M-IoT) evolves, the orchestration of numerous resources that offer support for high-bandwidth, low-latency applications arises as a key challenge. Architecturally, the edge-cloud framework alleviates structural concerns, but the linked nature of compute and data transfer poses problems of resource management. Approaches that tackle task offloading and adaptive transmission that think independently of each other tend to have problems such as user-server cross-region overloads or network congestion. This paper presents JATO, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning. JATO offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality. The framework was implemented for evaluation with a combination of different edge devices in a testbed alongside a simulation environment. JATO recorded a result of 0.9321 as the holistic score of the overall framework endpoint, a score significantly better than that of all the other frameworks that were used as functional baselines. JATO was able to resource optimally with a network lag of 131.65 milliseconds and a network freeze of 0.09% with the resources utilized. This is evidence that offloading and rate control in combination provides better resource elasticity for M-IoT systems.

Keywords

Collaboration network of the authors

27 authors · 58 co-authorship links · 10 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Bandung Institute of Technology
  • Institut Teknologi Nasional Bandung
  • Maranatha Christian University
  • University of Singaperbangsa Karawang
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Suhono Harso SupangkatBandung Institute of Technology4
Carmadi MachbubBandung Institute of Technology3
Nur AhmadiBandung Institute of Technology2
Amy Hamidah SalmanBandung Institute of Technology2
Richard MengkoAffiliation unknown2
Tati L. R. MengkoBandung Institute of Technology2
Eko MarpanajiBandung Institute of Technology1
Adit KurniawanBandung Institute of Technology1
Bamba Riyanto TrilaksonoAffiliation unknown1
Andri MahendraBandung Institute of Technology1
Thay LiungBandung Institute of Technology1
Herry Imanta SitepuBandung Institute of Technology1
Adhi RizalUniversity of Singaperbangsa Karawang1
Gladina DesideriaBandung Institute of Technology1
Yedi DermadiBandung Institute of Technology1
Rosa ElivianiBandung Institute of Technology1
Arry Akhmad ArmanBandung Institute of Technology1
Radiant Victor ImbarMaranatha Christian University1
Meliana Christianti JohanMaranatha Christian University1
Fawwaz Muhammad SuryadikarsaInstitut Teknologi Nasional Bandung1
Youllia Indrawaty NurhasanahInstitut Teknologi Nasional Bandung1
Arsyad Ramadhan DarlisInstitut Teknologi Nasional Bandung1
Lisa KristianaInstitut Teknologi Nasional Bandung1

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 Oct 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">JATO: Deep Reinforcement Learning-based Joint Optimization for Task Offloading and Adaptive Transmission in Multimedia IoT Systems</dc:title>
	<dc:creator>Purnama, Gina</dc:creator>
	<dc:creator>Dewi, Irma Amelia</dc:creator>
	<dc:creator>Langi, Armein Z. R.</dc:creator>
	<dc:creator>Bandung, Yoanes</dc:creator>
	<dc:subject xml:lang="en-US">adaptive transmission</dc:subject>
	<dc:subject xml:lang="en-US">deep reinforcement learning</dc:subject>
	<dc:subject xml:lang="en-US">edge-cloud computing</dc:subject>
	<dc:subject xml:lang="en-US">joint optimization</dc:subject>
	<dc:subject xml:lang="en-US">quality of service</dc:subject>
	<dc:subject xml:lang="en-US">task offloading</dc:subject>
	<dc:subject xml:lang="en-US">multimedia IoT</dc:subject>
	<dc:description xml:lang="en-US">As the Multimedia Internet of Things (M-IoT) evolves, the orchestration of numerous resources that offer support for high-bandwidth, low-latency applications arises as a key challenge. Architecturally, the edge-cloud framework alleviates structural concerns, but the linked nature of compute and data transfer poses problems of resource management. Approaches that tackle task offloading and adaptive transmission that think independently of each other tend to have problems such as user-server cross-region overloads or network congestion. This paper presents JATO, a framework to jointly tackle the problems of adaptive task offloading and transmission optimization using Deep Reinforcement Learning. JATO offers a mono-faceted solution, learning a policy to simultaneously determine the best offloading target and the transmission quality. The framework was implemented for evaluation with a combination of different edge devices in a testbed alongside a simulation environment. JATO recorded a result of 0.9321 as the holistic score of the overall framework endpoint, a score significantly better than that of all the other frameworks that were used as functional baselines. JATO was able to resource optimally with a network lag of 131.65 milliseconds and a network freeze of 0.09% with the resources utilized. This is evidence that offloading and rate control in combination provides better resource elasticity for M-IoT systems.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2026-08-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/26786</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2026.20.2.6</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 20 No. 2 (2026); 205-218</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2026.20.2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/26786/7465</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Journal of ICT Research and Applications</dc:rights>
</oai_dc:dc>