Deduplication-Oriented Mutual-Assisted Cooperative Video Upload for Mobile Crowd Sensing
Ying Wang, Quyuan Wang, Songtao Guo, Yuanyuan Yang · IEEE Transactions on Mobile Computing · 2021
Deduplication (redundancy elimination) and cooperative video delivery are two effective ways to save the bandwidth and energy consumption and ensure video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video routing. To solve such problems, this paper propose a cooperative upload method for sensing videos, which performs the local video deduplication without excessive comparison and feature exchange. Also, we combine the content-aware deduplication with the dynamic relay selection to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and load balance. We formulate the deduplication-supported cooperative video upload as a multi-stage decision problem. To solve the uncertainty of destinations in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVU) to schedule video chunks and remove duplicates. Extensive experiments are conducted to compare MAVU with the existing algorithms. The numerical results validate that our MAVU has advantages over the other algorithms in collected video size and upload latency