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.
翻译:无人驾驶航空飞行器(UAVs)可以应用在许多Tings(IoT)互联网系统中,例如智能农场,作为数据收集平台;然而,UAV-IOT无线频道有时会被树木或高楼建筑阻塞;可以应用智能反射表面(IRS),通过大量低成本被动反射元素,智能地反映信号,提高无线频道质量;这一条旨在通过联合优化UAV的部署和轨迹,最大限度地减少系统能源消耗;将问题设计成混合英特和非线性编程(MINLP),传统解决方案很难解决这个问题,因为解决办法很容易落到当地最佳状态;为解决这一问题,我们提议了一个部署和轨迹的联合优化框架(JOLT),通过大量低成本被动被动反射反射元素来优化UAV的部署;为优化UAV的部署和正轨图(ERSOM)提出弹性圈自我组织图(ERS),以优化UAV的轨迹。 具体地说,在AWOA中,一个可移动的轨道和偏差轨道展示战略是用来在AVERSLT中找到最优的轨道和部分的轨道。