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AI-Driven Collaborative Resource Allocation for Task Execution in 6G-Enabled Massive IoT
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DOI：10.1109/JIOT.2021.3051031.png)
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Abstract
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In the foreseeable future, the rapid growth of devices in the Internet of Things (IoT) will make it difficult for 5G networks to ensure sufficient network resources. 6G technology has attracted increasing attention, bringing new design concepts to the dynamic real-time resource allocation. The resource requirements of devices are usually variable, so a dynamic resource allocation method is needed to ensure the smooth execution of tasks. Therefore, this article first designs a 6G-enabled massive IoT architecture that supports dynamic resource allocation. Then, a dynamic nested neural network is constructed, which adjusts the nested learning model structure online to meet training requirements of dynamic resource allocation. An AI-driven collaborative dynamic resource allocation (ACDRA) algorithm is proposed based on the nested neural network combined with Markov decision process training for 6G-enabled massive IoT. Extensive simulations have been carried out to evaluate ACDRA in terms of several performance criteria, including resource hit rate and decision delay time. The results validated that ACDRA improves the average resource hit rate by about 8% and reduces the average decision delay time by about 7% compared with three reference existing algorithms.
Keywords:
Resource management
Task analysis
6G mobile communication
Dynamic scheduling
Heuristic algorithms
Vehicle dynamics
Real-time systems
Artificial intelligence
edge computing
Internet of Things (IoT)
resource allocation
sixth-generation mobile networks
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