  This paper focuses on developing energy-efficient online data processing
strategy of wireless powered MEC systems under stochastic fading channels. In
particular, we consider a hybrid access point (HAP) transmitting RF energy to
and processing the sensing data offloaded from multiple WDs. Under an average
power constraint of the HAP, we aim to maximize the long-term average data
sensing rate of the WDs while maintaining task data queue stability. We
formulate the problem as a multi-stage stochastic optimization to control the
energy transfer and task data processing in sequential time slots. Without the
knowledge of future channel fading, it is very challenging to determine the
sequential control actions that are tightly coupled by the battery and data
buffer dynamics. To solve the problem, we propose an online algorithm named
LEESE that applies the perturbed Lyapunov optimization technique to decompose
the multi-stage stochastic problem into per-slot deterministic optimization
problems. We show that each per-slot problem can be equivalently transformed
into a convex optimization problem. To facilitate online implementation in
large-scale MEC systems, instead of solving the per-slot problem with
off-the-shelf convex algorithms, we propose a block coordinate descent
(BCD)-based method that produces close-to-optimal solution in less than 0.04\%
of the computation delay. Simulation results demonstrate that the proposed
LEESE algorithm can provide 21.9\% higher data sensing rate than the
representative benchmark methods considered, while incurring sub-millisecond
computation delay suitable for real-time control under fading channel.
