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Challenges and prospects in the 6G-enabled Internet of Things ecosystem

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 16 No. 4 (2026)

DOI
10.11591/ijece.v16i4.pp1927-1943
ISSN
2722-2578, 2088-8708
Published
2026-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The exponential growth of the Internet of Things (IoT) applications is revealing critical limitations in current fifth generation networks (5G), especially with respect to scalability, latency, energy efficiency and intelligent resource management. These concerns make sixth-generation (6G) communication systems a near-future solution to facilitate intelligent, autonomous, and sustainable IoT ecosystems by amalgamating artificial intelligence (AI), terahertz (THz) communication, edge intelligence, semantic communication, and ultra-reliable low-latency communication (URLLC). But the research environment regarding 6G enabled IoT is still fragmented with the non-existence of a common analytical framework. In this paper, we present a structured survey and taxonomy based analytical framework for the 6G enabled IoT ecosystem. It classifies the recent works into enabling technologies, intelligent architectures, emerging applications, deployment challenges and future research directions. The requisite advancements in technologies needed for future IoT infrastructures are also highlighted through a comparison of 5G and 6G capabilities. The results of the analysis indicate that 6G can contribute to a substantial improvement of IoT performance in smart cities, healthcare, industrial automation and control applications (manufacturing science), autonomous transportation and precision agriculture. However, issues concerning cybersecurity, interoperability, sustainability, spectrum management and infrastructure cost still remain. This study is expected to enable the development of secure, scalable, intelligent and sustainable next-generation IoT systems based on collaborative edge computing.

Collaboration network of the authors

21 authors · 66 co-authorship links · 23 across different organisations

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

  • Multimedia University
  • Universiti Putra Malaysia
  • University of Technology Malaysia
  • Tun Hussein Onn University of Malaysia
  • Dhaka International University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Md. Armanur RahmanMultimedia University2
Md. Jakir HossenMultimedia University2
Chinthakunta VenkataseshaiahMultimedia University2
CK HoMultimedia University2
Aziza SultanaDhaka International University2
M. Z. H. JesmeenMultimedia University2
Tharek Abd RahmanUniversity of Technology Malaysia2
Omar Abdul AzizUniversity of Technology Malaysia2
Mohd Nazeri KamaruddinMultimedia University2
Azlan Abdul AzizAffiliation unknown2
Fazirulhisyam HashimUniversiti Putra Malaysia1
Nnamdi Henry UmeloUniversiti Putra Malaysia1
Nor K. NoordinUniversiti Putra Malaysia1
Mohd Fadlee A. RasidUniversiti Putra Malaysia1
Azlan Abd AzizMultimedia University1
Yaser Awadh BakhuraisaMultimedia University1
Saifulnizan JamianTun Hussein Onn University of Malaysia1
Fajaruddin MustakimTun Hussein Onn University of Malaysia1

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.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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