计算机科学
冗余(工程)
弹道
节点(物理)
相似性(几何)
数据挖掘
数据冗余
能源消耗
路径(计算)
实时计算
数学优化
人工智能
工程类
数学
物理
电气工程
结构工程
天文
图像(数学)
操作系统
程序设计语言
作者
Haoran Mei,Muhammad Fawad Khan,Limei Peng,Byungchul Tak,Ji-Yeon Lee,Pin–Han Ho
标识
DOI:10.1016/j.compeleceng.2023.108994
摘要
Data collected from densely deployed IoT sensor nodes via UAVs may incur remarkable redundancy, unnecessarily wasting the UAV power. This paper introduces a framework of UAV trajectory planning for optimizing energy efficiency via data-similarity-based node selection while maintaining sufficient information integrity out of the refined data. The proposed framework consists of three phases, namely data similarity determination, redundant nodes removal, and UAV trajectory planning. In particular, we propose a sliding-window dynamic time warping (SDTW) algorithm to quantify the data similarity between nodes. Then a hybrid genetic ant colony algorithm (HGACA) is introduced for the redundant node removal, where data similarity and UAV energy consumption are jointly considered. Finally, we formulate the trajectory planning problem as a three-stage integer linear programming (ILP) model, which clusters the nodes with minimal overlap and finds the shortest UAV cruising path that traverses each cluster head once and only once. The simulation result demonstrates that the proposed framework outperforms all the considered counterparts under various threshold values of data similarity in terms of execution time and power consumption while maintaining information integrity.
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