Open AccessAn efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles
Author(s) -
Sun Bing,
Yu Mingxing,
Wu Yi,
Qi Yuanhang,
Nie Xin,
Xi Shan
Publication year - 2021
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/cmu2.12110
Subject(s) - computer science , data collection , real time computing , computer data storage , data mining , transformation (genetics) , time series , affine transformation , data compression , data management , the internet , database , algorithm , computer hardware , biochemistry , statistics , chemistry , mathematics , machine learning , pure mathematics , world wide web , gene
Due to a large amount of time‐series data (e.g. precipitation, temperature, humidity) in the air, reliable and fast data collection is a challenging issue. Using an unmanned aerial vehicle (UAV) as a data collector to collect time‐series data is an effective method. However, due to the limited storage capacity of the UAV, the UAV must access the sensor multiple times to collect time‐series data from the deployed sensors and dump the data to the data centre, which leads to significantly increased time and cost for data collection mission. To address this challenge, an efficient time‐series data collection framework is proposed based on the Internet of UAVs. In this system, a min‐maximum data processing strategy is adopted based on data value to store the collected time‐series data. Specifically, efficient data compression storage is achieved with minimal loss of precision by extracting the dominant dataset with maximum value. Furthermore, an efficient affine transformation method is proposed to improve the efficiency of the system. Extensive case studies on some real‐world datasets demonstrate that the proposed framework can achieve efficient data management and compressed storage.