  Achieving long battery lives or even self sustainability has been a long
standing challenge for designing mobile devices. This paper presents a novel
solution that seamlessly integrates two technologies, mobile cloud computing
and microwave power transfer (MPT), to enable computation in passive
low-complexity devices such as sensors and wearable computing devices.
Specifically, considering a single-user system, a base station (BS) either
transfers power to or offloads computation from a mobile to the cloud; the
mobile uses harvested energy to compute given data either locally or by
offloading. A framework for energy efficient computing is proposed that
comprises a set of policies for controlling CPU cycles for the mode of local
computing, time division between MPT and offloading for the other mode of
offloading, and mode selection. Given the CPU-cycle statistics information and
channel state information (CSI), the policies aim at maximizing the probability
of successfully computing given data, called computing probability, under the
energy harvesting and deadline constraints. The policy optimization is
translated into the equivalent problems of minimizing the mobile energy
consumption for local computing and maximizing the mobile energy savings for
offloading which are solved using convex optimization theory. The structures of
the resultant policies are characterized in closed form. Furthermore, given
non-causal CSI, the said analytical framework is further developed to support
computation load allocation over multiple channel realizations, which further
increases computing probability. Last, simulation demonstrates the feasibility
of wirelessly powered mobile cloud computing and the gain of its optimal
control.
