Modal-aware Resource Allocation for Cross-modal Collaborative Communication in IIoT
Document Type
Article
Publication Title
IEEE Internet of Things Journal
Abstract
With the development of human-machine interactions, users are increasingly evolving towards an immersion experience with multi-dimensional stimuli. Facing this trend, cross-modal collaborative communication is considered an effective technology in the Industrial Internet of Things (IIoT). In this paper, we focus on open issues about resource reuse, pair interactivity, and user assurance in cross-modal collaborative communication to improve quality of service (QoS) and users’ satisfaction. Therefore, we propose a novel architecture of modal-aware resource allocation to solve these contradictions. First, taking all the characteristics of multi-modal into account, we introduce network slices to visualize resource allocation, which is modeled as a Markov Decision Process (MDP). Second, we decompose the problem by the transformation of probabilistic constraint and Lyapunov Optimization. Third, we propose a deep reinforcement learning (DRL) decentralized method in the dynamic environment. Meanwhile, a federated DRL framework is provided to overcome the training limitations of local DRL models. Finally, numerical results demonstrate that our proposed method performs better than other decentralized methods and achieves superiority in cross-modal collaborative communications.
First Page
1
Last Page
1
DOI
10.1109/JIOT.2023.3263687
Publication Date
3-31-2023
Keywords
Collaboration, cross-modal collaborative communication, DRL, federated learning, IIoT, Industrial Internet of Things, Markov processes, MDP, Reliability, resource allocation, Resource management, Streaming media, Ultra reliable low latency communication
Recommended Citation
M. Chen, L. Zhao, J. Chen, X. Wei and M. Guizani, "Modal-aware Resource Allocation for Cross-modal Collaborative Communication in IIoT," in IEEE Internet of Things Journal, March 2023, doi: 10.1109/JIOT.2023.3263687.
Comments
IR Deposit conditions:
OA version (pathway a) Accepted version
No embargo
When accepted for publication, set statement to accompany deposit (see policy)
Must link to publisher version with DOI
Publisher copyright and source must be acknowledged