  Mobile edge computing (MEC) is envisioned as a promising technique to support
computation-intensive and timecritical applications in future Internet of
Things (IoT) era. However, the uplink transmission performance will be highly
impacted by the hostile wireless channel, the low bandwidth, and the low
transmission power of IoT devices. Recently, intelligent reflecting surface
(IRS) has drawn much attention because of its capability to control the
wireless environments so as to enhance the spectrum and energy efficiencies of
wireless communications. In this paper, we consider an IRS-aided multidevice
MEC system where each IoT device follows the binary offloading policy, i.e., a
task has to be computed as a whole either locally or remotely at the edge
server. We aim to minimize the total energy consumption of devices by jointly
optimizing the binary offloading modes, the CPU frequencies, the offloading
powers, the offloading times and the IRS phase shifts for all devices. Two
algorithms, which are greedy-based and penalty-based, are proposed to solve the
challenging nonconvex and discontinuous problem. It is found that the
penalty-based method has only linear complexity with respect to the number of
devices, but it performs close to the greedy-based method with cubic complexity
with respect to number of devices. Furthermore, binary offloading via IRS
indeed saves more energy compared to the case without IRS.
