International Journal of Technology and Applied Science

E-ISSN: 2230-9004   •   Impact Factor: 10.31

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 17 Issue 3 (March 2026) Submit your research before the last 3 days of this month to publish your research paper in the current issue.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 17 Issue 3
March 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Dynamic Resource Allocation in Massive UAV-Enabled Industrial IoT System Using Convolutional Neural Network and Deep Q-Networks

Author(s) Ajit Tiwari, Dr Sunil Tiwari
Country India
Abstract The heavy demands of the internet of things (IoT) and sensor technology in industries and their rapid proliferation and application as the industrial internet of things (IIoT) demand efficient resource allocation to support massive device connectivity with quality-of-service (QoS) requirements. Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing network coverage and flexibility in IIoT environments. However, dynamic resource allocation in such systems is challenged by complex channel conditions, varying device demands, and energy constraints. This paper proposes a novel approach for dynamic resource allocation in massive UAV-enabled IIoT systems, leveraging the synergy of Convolutional Neural Networks (CNNs) and Deep Q-Networks (DQNs). The CNN extracts spatial and channel features from the IIoT environment, while the DQN optimizes power allocation and subchannel assignment to maximize energy efficiency while meeting minimum rate requirements. We model the problem as a Markov Decision Process, incorporating realistic channel models with path loss and fading. Simulation results, using a real-world IIoT dataset, demonstrate that the proposed CNN-DQN framework achieves superior energy efficiency and throughput compared to traditional heuristic and Q-learning methods, with reduced computational complexity. This approach offers a scalable and adaptive solution for next-generation IIoT networks, paving the way for efficient UAV-assisted communication systems.
Keywords Industrial Internet of Things, Unmanned Aerial Vehicle, Convolutional Neural Networks, Deep Q-Networks, Dynamic Resource Allocation
Field Engineering
Published In Volume 17, Issue 3, Array 2026
Published On 2026-03-09
Cite This Dynamic Resource Allocation in Massive UAV-Enabled Industrial IoT System Using Convolutional Neural Network and Deep Q-Networks - Ajit Tiwari, Dr Sunil Tiwari - IJTAS Volume 17, Issue 3, Array 2026.
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJTAS DOI prefix is
10.71097/IJTAS

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 12Cover Page Vol 15 Isu 11Cover Page Vol 15 Isu 10

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJTAS
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijtas.com