CAREER: Uncertainty-aware sensing and management for IoT

在大规模无处不在的智能设备的支持下，物联网（IoT）的出现通过大量的应用程序为我们的日常生活带来了极大的便利，其中许多应用程序都是安全关键的，包括医疗保健，监控和自动驾驶等。对于这样的安全关键领域，目前的工具包通常在不确定性量化方面不足，这是明智决策所必需的一个关键特征。考虑到物联网设备在移动中收集的大量数据，可扩展性是低延迟实时物联网感知和管理的另一个关键因素。此外，如何赋予学习对物联网中不可预测的动态的适应性和鲁棒性至关重要，特别是对于人在回路中。在充分发挥安全关键型物联网的潜力之前，必须开发新的工具来应对这些重大挑战。为了实现这一目标，该CAREER提案倡导基础研究，旨在推进当前的实时物联网传感和管理工具，对许多安全关键领域产生直接影响，包括医疗保健，交通和环境传感。利用PI的机构资源，PI将把拟议的研究目标转化为教育活动，通过i）指导研究生和本科生，特别是那些来自代表性不足的群体; ii）交叉施肥机器学习，通信，信号处理和网络领域的课程开发;以及iii）与UGA的网络物理系统中心的跨学科合作。这种研究和教育的无缝集成是中央PI的职业道路，并与UGA的使命“教，服务，并探究事物的本质对齐。“为进一步推动社会接纳新兴的物联网技术，研究所致力透过短期课程、网上短片及工作坊，向公众，特别是幼儿园至12年级的学生，推广研究成果。这项建议提出了一项雄心勃勃的计划，透过剪裁现代贝叶斯机器学习工具的先进技术，即贝叶斯函数近似、贝叶斯强盗优化、贝叶斯函数近似、贝叶斯和贝叶斯强化学习，以解决上述挑战。这种新的贝叶斯风格自然会创新现有的工具包，具有不确定性量化和鲁棒性，这对安全关键型物联网至关重要。由此产生的方法不仅将有利于关键的物联网任务，而且还将通过纳入物联网驱动的约束来显着推动这些学科的发展。具体而言，将追求三个互补和相互交织的研究重点。Thrust 1（T1）提出了一个基本的不确定性感知函数学习框架，它不仅直接有利于T1中面向预测的物联网感知任务，而且有助于Thrust 2（T2）中开环盲物联网管理的贝叶斯优化，其中物联网控制器做出的决策不影响物联网状态。Thrust 3在T1和T2的基础上进一步扩展了Bayesian RL，以实现实时闭环物联网管理，并在物联网状态和物联网控制器之间进行全面交互。最终追求是一个整体框架，将新型算法与不确定性意识、可扩展性和自适应性集成在一起，用于实时物联网传感和管理，以及对不可预测动态的鲁棒性的相关严格分析，和部署到真实的安全-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的评估，被认为值得支持。影响审查标准。

Bolstered by a massive scale of ubiquitously connected smart devices, the emergence of Internet-of-Things (IoT) has brought about substantial conveniences to our daily life through a plethora of applications, of which many are safety-critical, including healthcare, surveillance, and autonomous driving, to name a few. For such safety-critical domains, the current toolkits usually fall short in uncertainty quantification, a key feature that is necessitated for informed decision-making. Given the enormous data collected by IoT devices on-the-go, scalability is another key enabler of real-time IoT sensing and management with low latency. Further, how to endow learning with adaptivity and robustness to unpredictable dynamics in IoT is of utmost importance, especially with humans-in-the-loop. Before embracing the full potential of safety-critical IoT, novel tools have to be developed to address these major challenges. Towards this goal, this CAREER proposal advocates fundamental research that aspires to advance the current tools for real-time IoT sensing and management, with direct impact on a number of safety-critical domains, including healthcare, transportation, and environmental sensing. Leveraging the PI's institutional resources, the PI will transform the proposed research goals into educational activities, through i) mentoring graduate and undergraduate students, especially those from the underrepresented groups; ii) curriculum development that cross-fertilizes the fields of machine learning, communications, signal processing and networking; as well as iii) interdisciplinary collaboration with UGA's Center of Cyber-Physical Systems. This seamless integration of research and education is central to the PI's career path and is well aligned with UGA's mission ``to teach, to serve, and to inquire into the nature of things." To further promote the societal embracing of the emergent IoT technologies, the PI is committed to disseminate the research outcomes to the general public, in particular K-12 students, through short courses, online videos, and workshops.This proposal puts forth an ambitious plan by tailoring advances in contemporary Bayesian machine learning tools, namely, Bayesian function approximation, Bayesian bandit optimization, and Bayesian reinforcement learning, to address the aforementioned challenges. This fresh Bayesian flavor naturally innovates existing toolkits with uncertainty quantification and robustness, essential to safety-critical IoT. The resultant approaches will not only benefit key IoT-enabled tasks, but also markedly push the envelope of these disciplines by incorporating IoT-driven constraints. Specifically, three complementary and intertwined research thrusts will be pursued. Thrust 1 (T1) puts forth a fundamental uncertainty-aware function learning framework, which not only directly benefits the prediction-oriented IoT sensing task in T1, but also contributes to Bayesian optimization for open-loop blind IoT management in Thrust 2 (T2), where the decisions made by the IoT controller do not affect the IoT state. Thrust 3 further builds on T1 and T2 to scale up Bayesian RL for real-time closed-loop IoT management with full interaction between the IoT state and the IoT controller. The ultimate pursuit is a holistic framework that integrates novel algorithms with uncertainty awareness, scalability, and adaptivity for real-time IoT sensing and management, the associated rigorous analyses for robustness to unpredictable dynamics, and the deployment to real safety-critical IoT applications.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.