  The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) has
been deemed a promising paradigm to provide ubiquitous communication and
computing services for the Internet of Things (IoT). Besides, by intelligently
reflecting the received signals, the reconfigurable intelligent surface (RIS)
can significantly improve the propagation environment and further enhance the
service quality of the UAV-enabled MEC. Motivated by this vision, in this
paper, we consider both the amount of completed task bits and the energy
consumption to maximize the energy efficiency of the RIS-assisted UAV-enabled
MEC systems, where the bit allocation, transmit power, phase shift, and UAV
trajectory are jointly optimized by an iterative algorithm with a double-loop
structure based on the Dinkelbach's method and block coordinate decent (BCD)
technique. Simulation results demonstrate that: 1) with the deployment of RIS,
our proposed algorithm can achieve higher energy efficiency than baseline
schemes while satisfying the task tolerance latency; 2) the energy efficiency
first increases and then decreases with the increase of the mission period and
the total amount of task-input bits of IoT devices; 3) when the CPU cycles
required for computing 1-bit of task-input data becomes larger, more task bits
will be offloaded to the UAV while the energy efficiency will be decreased.
