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The rapid technological advances of cellular technologies will revolutionizenetwork automation in industrial internet of things (IIoT). In this paper, weinvestigate the two-timescale resource allocation problem in IIoT networks withhybrid energy supply, where temporal variations of energy harvesting (EH),electricity price, channel state, and data arrival exhibit differentgranularity. The formulated problem consists of energy management at a largetimescale, as well as rate control, channel selection, and power allocation ata small timescale. To address this challenge, we develop an online solution toguarantee bounded performance deviation with only causal information.Specifically, Lyapunov optimization is leveraged to transform the long-termstochastic optimization problem into a series of short-term deterministicoptimization problems. Then, a low-complexity rate control algorithm isdeveloped based on alternating direction method of multipliers (ADMM), whichaccelerates the convergence speed via the decomposition-coordination approach.Next, the joint channel selection and power allocation problem is transformedinto a one-to-many matching problem, and solved by the proposed price-basedmatching with quota restriction. Finally, the proposed algorithm is verifiedthrough simulations under various system configurations.

Two-timescale Resource Allocation for Automated Networks in IIoT