Intelligent UAVs Trajectory Optimization From Space-Time for Data Collection in Social Networks

With rapid development of artificial intelligence (AI) technology, social network (SN) can use AI to extract useful knowledge of users to improve the quality of peoples lives. Although AI has achieved a very big breakthrough, it also faces many challenges for collecting data, such as larger data redundancy and higher energy consumption. To conquer those problems, a matrix completion-based Sampling Points Selection joint Intelligent Unmanned Aerial Vehicle (UAVs) Trajectory Optimization (SPS-IUTO) scheme for data acquisition is proposed. In terms of space, for one column, the probability that a sample point is selected is inversely proportional to the number of sample points selected by all previous rows. In terms of time, the first step is that sampling points with higher degree are selected as dominator sampling points in each row and column. The second step is that sampling points with lower degree are selected as virtual dominator sampling points. The movement trajectory of the UAV is optimized using the proposed algorithm. As is shown in the experimental results, the proposed scheme can achieve significant improvement in terms of energy and redundant data.

Automated summary

This study proposes a matrix completion-based Sampling Points Selection joint Intelligent Unmanned Aerial Vehicle (UAVs) Trajectory Optimization (SPS-IUTO) scheme, which significantly improves energy and redundant data issues.