Document Type
Article
Source of Publication
Computers and Electrical Engineering
Publication Date
11-1-2026
Abstract
Large Language Models (LLM), which have gained great momentum in recent years, have revolutionized the field of Artificial Intelligence (AI); while their applicability for hardware-constrained Internet of Things (IoT) environments has begun to be questioned. This has led to the emergence of compact architecture and resource-efficient Tiny LLM models. This survey paper systematically examines Tiny LLMs for IoT networks and classifies existing approaches in five basic dimensions: model architectures, optimization strategies, transfer learning methods, deployment paradigms, and explainability-security integration. By applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, 139 related studies published between 2020 and 2025 are analyzed to evaluate architectural adaptation, resource adaptation, and collaborative learning approaches. The findings reveal the most current fundamental technical gaps in the field and discuss the research directions of Tiny LLM in terms of generalizability, interpretability, and reliability in mission-critical IoT ecosystems.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
139
Disciplines
Computer Sciences
Keywords
Edge AI, IoT networks, Lightweight language models, Low-resource NLP, Model compression, Tiny LLMs, Transformer optimization
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Golec, Muhammed; Melhem, Suhib Bani; Khamayseh, Yaser; Alwarafy, Abdulmalik; and Al-Dhahir, Naofal, "Tiny Large Language Models for IoT networks: Potentials and challenges" (2026). All Works. 8077.
https://zuscholars.zu.ac.ae/works/8077
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series