Edge Learning for Surveillance Video Uploading Sharing in Public Transport Systems

Abstract

Nowadays, surveillance cameras have been pervasively equipped with vehicles in public transport systems. For the sake of public security, it is crucial to upload recorded surveillance videos to remote servers timely for backup and necessary video analytics. However, continuously uploading video content generated by tens of thousands of vehicles can be extremely bandwidth consuming. In this work, we investigate the video uploading problem for moving buses by proposing to deploy dedicated access points (AP) at bus stops to facilitate video uploading. We define the harmonic objective for our problem, which includes minimizing the video uploading delay and minimizing the AP deployment cost. This problem is with two fundamental challenges. Firstly, it is difficult to balance the bandwidth capacity allocated to many buses because a bus obtains bandwidth resource from a series of APs deployed at stops along its route. Secondly, due to the randomness of bus movement and the complexity of bus routes, it is hard to predict the workload of an AP. Hence, it is challenging to estimate the delay of uploading video content through an AP. To cope with these challenges, we propose a water filling placement (WFP) algorithm, aiming to balance the aggregated bandwidth allocated to each bus. A queuing model is established to analyze the uploading delay of video content. We further resort to machine learning models to factor the influence of bus routes into our queuing model. Finally, a convex problem is formulated to optimize the harmonic objective, which can be optimally solved with the gradient descent (GD) based algorithm. We validate the correctness of our theoretical analysis and demonstrate the effectiveness of our method by carrying out extensive experiments using bus traces collected in Shenzhen city of China. In comparison with benchmark algorithms, our solution can always achieve the best performance.