UC Irvine

The operations of Mobile Autonomous Systems (MAS) rely on real-time data analysis. For instance, autonomous vehicles’ navigation requires the low-latency analysis of high resolution images to detect, and avoid, objects. Unfortunately, many Mobile Autonomous Systems (MASs) have constrained computing and energy resources, and the continuous execution of state-of-the-art algorithms is out of their reach. By offloading the processing load to compute-capable device located at the network edge, the edge computing paradigm can mitigate this issue. However in practical real-world settings, the wireless channel connecting the mobile devices to the edge server often presents erratic capacity patterns due to mobility.As a result, the overall delay perceived by the mobile application may be affected by large variations, which in turn harm control.This thesis explores solutions to the problem described above. To this aim, in addition to new concepts and edge offloading strategies, a complete real-world platform – the HyDRA platform, was developed to support design and evaluation, as well as dataset collection.HyDRA is a fully open source software and hardware platform realizing flexible machine learning-empowered computing for MAS. From a hardware perspective, HyDRA is composed of several Unmanned Aerial Vehicles (UAVs) and ground devices collaboratively performing data analysis to accomplish system-wide goals. From a software perspective, the HyDRA middleware enables real-time control of data and task routing within the system, organized as a distributed set of modules transforming the data captured by the MAS into actuable control.Taking HyDRA as a starting point, this thesis makes the following conceptual contributions:‚the end-to-end delay in remote computing settings for MAS – and specifically autonomous quad-copters – were characterized by means of real-world experiments that produced a comprehensive dataset focused on object detection from images. The study considered both Wi-Fi and Long-Term Evolution connectivity, and several embedded computing platforms. The results demonstrates the instability of application level delay even in line-of-sight settings and relatively slow vehicle motion. A framework for the dynamic control of task offloading in MASs with extreme temporal variations is developed. The frameworks is based on a preliminary experimental analysis, which indicates that there is no dominant feature, including obvious features such as channel quality, and that prediction necessitates an ensemble of weaker features. We first mathematically formulate a Redundant Task Offloading Problem. Then, we create predictors that can help manage the resource usage/performance trade-off. Specifically, we propose a myopic predictor as baseline and a DRL-based approach, which operates on a set of features from application, network and device-level components. To the best of our knowledge, this is the first framework addressing the problem of redundant task offloading in MAS with a data-driven approach which efficacy is verified in a real-world testbed and with replicable dataset-based experiments.‚A modeling and optimization framework based on Markov Decision Processes (MDP) was developed to analyze the structural properties of dynamic control strategies determining where information is processed in collaborative computing scenarios for MAS. In this section of the thesis, the focus of control is primarily between local and remote analysis. Using recent split Deep Neural Networks deep neural network (DNN) techniques, the framework also controls at a fine-grain how the analysis task (the DNN in this case) is divided between the mobile device and the edge server based on current system parameters.