Edge Learning for Surveillance Video Uploading Sharing in Public Transport Systems
Edge Learning for Surveillance Video Uploading Sharing in Public Transport Systems
复制标题
公共交通系统监控视频上传共享的边缘学习
DOI：
10.1109/tits.2020.3008420
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发表时间：
2020-07
影响因子：
8.5
通讯作者：
Shiping Chen
中科院分区：
文献类型：
--
作者：
Laizhong Cui;Dongyuan Su;Yipeng Zhou;Lei Zhang;Yulei Wu;Shiping Chen
如今，公共交通系统的车辆上普遍配备了监控摄像头。为了公共安全，及时将监控录像上传到远程服务器，以便进行备份和必要的视频分析是至关重要的。然而，持续上传由数万辆汽车生成的视频内容会消耗大量带宽。在这项工作中，我们通过建议在公交车站部署专用接入点（AP）来研究移动公交车的视频上传问题，以方便视频上传。我们定义了问题的调和目标，包括最小化视频上传延迟和最小化AP部署成本。这个问题有两个基本挑战。首先，由于总线从沿线站点部署的一系列ap中获得带宽资源，因此难以平衡分配给多个总线的带宽容量。其次，由于公共汽车运动的随机性和公共汽车路线的复杂性，很难预测AP的工作量。因此，估计通过AP上传视频内容的延迟是具有挑战性的。为了应对这些挑战，我们提出了一种水填充放置（WFP）算法，旨在平衡分配给每辆公共汽车的聚合带宽。为了分析视频内容的上传延迟，建立了排队模型。我们进一步利用机器学习模型将公交路线的影响因素纳入我们的排队模型。最后，提出了一个优化调和目标的凸问题，并用基于梯度下降（GD）的算法进行最优求解。我们验证了我们的理论分析的正确性，并通过在中国深圳收集的公交车轨迹进行了广泛的实验，证明了我们方法的有效性。与基准算法相比，我们的解决方案总能达到最佳性能。
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.
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影响因子：
7.9
作者：
Jianping He;Yuanzhi Ni;Lin X. Cai;Jianping Pan;Cailian Chen
通讯作者：
Jianping He;Yuanzhi Ni;Lin X. Cai;Jianping Pan;Cailian Chen
人工智能启发认知车辆通信和网络中的传输调度
影响因子：
10.6
作者：
Zhang Ke;Leng Supeng;Peng Xin;Pan Li;Maharjan Sabita;Zhang Yan
通讯作者：
Zhang Yan
DOI：
--
发表时间：
2013-07
期刊：
ArXiv
影响因子：
--
作者：
M. Zukerman
通讯作者：
M. Zukerman
影响因子：
6
作者：
Trullols, O.;Fiore, M.;Barcelo Ordinas, J. M.
通讯作者：
Barcelo Ordinas, J. M.
影响因子：
4.8
作者：
Bellalta, Boris;Kosek-Szott, Katarzyna
通讯作者：
Kosek-Szott, Katarzyna