CAREER: Uncertainty-aware sensing and management for IoT

职业：物联网的不确定性感知传感和管理

• 批准号: 2340049
• 负责人: Qin Lu
• 金额: $ 52.28万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340049&HistoricalAwards=false
• 关键词: CAREER; Uncertainty; aware; sensing; management

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.

在大规模、无处不在的智能设备的支持下，物联网 (IoT) 的出现通过大量应用为我们的日常生活带来了极大的便利，其中许多应用都是安全关键的，包括医疗保健、监控、以及自动驾驶等等。对于此类安全关键领域，当前的工具包通常在不确定性量化方面存在不足，而不确定性量化是明智决策所必需的关键功能。鉴于物联网设备在移动中收集大量数据，可扩展性是低延迟实时物联网传感和管理的另一个关键推动因素。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，尤其是在人机交互的情况下。在充分发挥安全关键型物联网的潜力之前，必须开发新颖的工具来应对这些重大挑战。为了实现这一目标，该职业提案倡导基础研究，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 将通过以下方式将拟议的研究目标转化为教育活动： i) 指导研究生和本科生，特别是来自代表性不足群体的学生； ii) 机器学习、通信、信号处理和网络领域交叉发展的课程开发；以及 iii) 与佐治亚大学网络物理系统中心的跨学科合作。研究和教育的无缝整合是 PI 职业道路的核心，并且与 UGA 的使命“教学、服务和探究事物的本质”完全一致。进一步促进社会接受新兴的物联网技术PI 致力于通过短期课程、在线视频和研讨会向公众传播研究成果，特别是 K-12 学生。该提案通过定制当代贝叶斯机器的进步提出了一项雄心勃勃的计划学习工具，即贝叶斯函数逼近、贝叶斯强盗优化和贝叶斯强化学习，来解决上述挑战，这种新鲜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，这对于安全关键的物联网至关重要。不仅有利于关键的物联网任务，而且还通过纳入物联网驱动的约束来显着突破这些学科的极限。具体来说，将追求三个互补且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，不仅直接有利于T1中面向预测的物联网传感任务，而且还有助于Thrust 2（T2）中开环盲物联网管理的贝叶斯优化，其中 IoT 控制器做出的决策不会影响 IoT 状态。 Thrust 3 进一步构建在 T1 和 T2 的基础上，扩展贝叶斯强化学习，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实现全面交互。最终的追求是一个整体框架，它将新颖的算法与实时物联网传感和管理的不确定性意识、可扩展性和适应性相结合，对不可预测的动态的鲁棒性进行相关的严格分析，并将其部署到真正的安全关键型物联网应用中。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。