ArticleIEEE internet of things journal2022
Towards Deep Q-Network Based Resource Allocation in Industrial Internet of Things.
Article in IEEE internet of things journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Energy optimized scheduling in wireless sensor networks (WSNs) using hybrid bio-inspired reinforcement learning approach.Scientific reports · 2026Article
- Multi-Agent Deep Reinforcement Learning for Joint Task Offloading and Resource Allocation in IIoT with Dynamic Priorities.Sensors (Basel, Switzerland) · 2025Article
- Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems.Scientific reports · 2024Article
- Energy-Efficient Uplink Scheduling in Narrowband IoT.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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Abstract
With the increasing adoption of Industrial Internet of Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, operation environments are time-varying, and required information may not be available, it is difficult to leverage traditional optimization techniques to solve the optimal resource allocation problem. To this end, in this paper we propose a Deep
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Registered trials
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