TY - JOUR
T1 - Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System
AU - Dong, Li
AU - Liu, Zhibin
AU - Jiang, Feibo
AU - Wang, Kezhi
N1 - Funding information: This work was supported in part by the National Natural Science Foundation of China under Grant nos. 41904127, 41604117, 62002115. in part by the Hunan Provincial Natural Science Foundation of China under Grant nos. 2020JJ4428, 2020JJ5105. in part by the Key Research and Development Plan of Hunan Province under Grant no 2021NK2020.
PY - 2022/11/1
Y1 - 2022/11/1
N2 - 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.
AB - 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.
KW - Autonomous aerial vehicles
KW - Data collection
KW - Deployment optimization
KW - Energy consumption
KW - IRS
KW - Internet of Things
KW - Optimization
KW - Statistics
KW - Trajectory
KW - UAV
KW - adaptive whale optimization algorithm
KW - elastic ring self-organizing map
KW - trajectory optimization
UR - http://www.scopus.com/inward/record.url?scp=85133717276&partnerID=8YFLogxK
U2 - 10.1109/jiot.2022.3185012
DO - 10.1109/jiot.2022.3185012
M3 - Article
SN - 2327-4662
VL - 9
SP - 21583
EP - 21593
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 21
M1 - 9802633
ER -