  Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things
(IoT) systems, e.g., smart farms, as a data collection platform. However, the
UAV-IoT wireless channels may be occasionally blocked by trees or high-rise
buildings. An intelligent reflecting surface (IRS) can be applied to improve
the wireless channel quality by smartly reflecting the signal via a large
number of low-cost passive reflective elements. This article aims to minimize
the energy consumption of the system by jointly optimizing the deployment and
trajectory of the UAV. The problem is formulated as a
mixed-integer-and-nonlinear programming (MINLP), which is challenging to
address by the traditional solution, because the solution may easily fall into
the local optimal. To address this issue, we propose a joint optimization
framework of deployment and trajectory (JOLT), where an adaptive whale
optimization algorithm (AWOA) is applied to optimize the deployment of the UAV,
and an elastic ring self-organizing map (ERSOM) is introduced to optimize the
trajectory of the UAV. Specifically, in AWOA, a variable-length population
strategy is applied to find the optimal number of stop points, and a nonlinear
parameter a and a partial mutation rule are introduced to balance the
exploration and exploitation. In ERSOM, a competitive neural network is also
introduced to learn the trajectory of the UAV by competitive learning, and a
ring structure is presented to avoid the trajectory intersection. Extensive
experiments are carried out to show the effectiveness of the proposed JOLT
framework.
