| dc.description.abstracteng | Over the past 20 years, visual data has taken over every aspect of our lives, and camera deployment has experienced an unprecedented increase. For example, in the USA and UK, there is one camera for every 8 people used for diverse applications, like surveillance and public safety. However, in many extreme conditions, pre-deploying cameras are not feasible. Fortunately, the development of UAVs let these agile, flexible, and powerful devices make up the limitation of pre-deployed cameras. While high-resolution visual data offers rich information about the sensing environment, it causes significant challenges to the data analysis. Advances in computer vision present an excellent opportunity to process and analyze this massive amount of data; however, they have come at the expense of compute and network costs. For analysis, computing-resource-limited UAVs need to transmit sensing data to a computing-resource-rich server (on the ground). This distributed architecture posits several network-level resource management challenges: to ensure optimal UAV trajectories for sensing visual data; and to address the mobility impact and fair data delivery in multi-UAV access networks; and to provide low-latency, high-accuracy, and low-bandwidth-cost analysis.
We begin by presenting a general algorithm design schema for the waypoint planning problem to generate waypoints (i.e., UAVs hovering and sensing points) achieving quality bounded sensing data. This schema includes three steps: discretization divides the entire solution space into subspaces; then dominating set extraction find out all the optimal solutions in every subspace; at last, transform waypoint planning into submodular optimization problem and propose an approximate algorithm. We apply this schema to three scenarios and verify the performance of our method provides 1.6× gain in sensing data quality.
Next, in order to address communication challenges, specifically the mobility impact and fairness among multiple UAV-server streamings, we develop VSiM - an easy-deployment and high-compatibility end-to-end solution to fairness in multiple mobile video streaming applications with a shared bottleneck bandwidth. It is pluggable to the server directly without caring and modifying any existing protocols or components. VSiM consists of three key techniques: dynamic and fair bandwidth allocation by incorporating mobile profile and QoE-related information; quick buffer filling for clients with lower playback time according to the requirement of the buffersensitive clients; adaptiveness to heterogeneous wireless network environments, like varied mobility patterns and topologies of base stations (BSes). It improves more than 40% on min QoE, which equals resolution improvement of viewing quality from 720p to 1080p) compared to state-of-the-art solutions.
Finally, in order to achieve low-latency, high-accuracy, and low-bandwidth-cost analysis, we present AccDecoder - a new decoder that derives important video content from bits stream and enhances them by super-resolution (SR) model. SR model achieves low-bandwidth-cost by allowing UAVs to transmit low-resolution data and enhance them into high-resolution getting high-accuracy. AccDecoder performs low latency by analyzing bits stream in compressed-video-space, then selecting and enhancing a small part of data. AccDecoder preliminary opens the original decoder and reveals handy video codec information has potential room to accelerate analysis more than 4× speed.
This dissertation combines abstract mathematical models to describe and derive UAV Monitoring System (MoniSys) behaviors to design theory-level algorithms and develop system-level implementations for a set of working. The proposed key solutions have been implemented on DASH, PyTorch, H.264, and QUIC, four open-source codes, and our code is also public released on GitHub. | de |