  Wireless powered mobile edge computing has been envisioned as a promising
paradigm to enhance the computation capability of low-power wireless devices in
Industrial Internet of Things. An efficient resource scheduling method is
critical yet challenging to design in such a scenario due to stochastic traffic
arrival, time-coupling uplink/downlink decision and incomplete system state
knowledge. To tackle these challenges, an online optimization algorithm is
proposed in this paper to maximize long-term system utility balancing
throughput and fairness, subject to data age and stability constraints. A set
of virtual queues is designed to transform the scheduling task, which is hard
to solve due to time-dependent data age constraints, into a stochastic
optimization problem. Leveraging Lyapunov and convex optimization techniques,
the proposed approach can achieve asymptotically near-optimal online decisions
without any prior statistical knowledge, and maintain the asymptotic optimality
in the presence of partial and outdated network state information. Numerical
simulations corroborate the theoretical analysis and demonstrate the
effectiveness of the proposed approach.
