  Mobile edge computing (MEC) has recently become a prevailing technique to
alleviate the intensive computation burden in Internet of Things (IoT)
networks. However, the limited device battery capacity and stringent spectrum
resource significantly restrict the data processing performance of MEC-enabled
IoT networks. To address the two performance limitations, we consider in this
paper an MEC-enabled IoT system with an energy harvesting (EH) wireless device
(WD) which opportunistically accesses the licensed spectrum of an overlaid
primary communication link for task offloading. We aim to maximize the
long-term average sensing rate of the WD subject to quality of service (QoS)
requirement of primary link, average power constraint of MEC server (MS) and
data queue stability of both MS and WD. We formulate the problem as a
multi-stage stochastic optimization and propose an online algorithm named PLySE
that applies the perturbed Lyapunov optimization technique to decompose the
original problem into per-slot deterministic optimization problems. For each
per-slot problem, we derive the closed-form optimal solution of data sensing
and processing control to facilitate low-complexity real-time implementation.
Interestingly, our analysis finds that the optimal solution exhibits an
threshold-based structure. Simulation results collaborate with our analysis and
demonstrate more than 46.7\% data sensing rate improvement of the proposed
PLySE over representative benchmark methods.
