CPS: Medium: Batteryless Sensors Enabling Smart Green Infrastructure

CPS：中：无电池传感器支持智能绿色基础设施

• 批准号: 2038853
• 负责人: Josiah Hester
• 金额: $ 120万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038853&HistoricalAwards=false
• 关键词: CPS; Medium; Batteryless; Sensors; Enabling

Cities across the nation have invested significantly in making infrastructure smarter, more sustainable, and more resilient to extreme weather events. Green infrastructure (GI), a general term for planned installations of trees, plants, soils, wetlands, and other natural resource, is increasingly being installed in cities nationwide to provide resilience to flooding, sewer overflows, urban heat, air pollution, habitat loss, and coastal erosion, that are overwhelming traditional infrastructure and negatively impact urban life. Embedding smart devices in GI provides numerous benefits to a city, providing insight into the health and effectiveness of the system, and the operations and fitness of the city. However, deploying smart devices in GI is challenging because of the scale and need for long-term deployment, meaning battery powered or expensive plugged-in devices are not feasible. This project builds Smart Green Infrastructure; augmenting GI with battery-free smart devices, powered by energy harvested directly from soil, which gather data, infer, actuate, and collaborate with each other. By harvesting from freely available soil and removing batteries, these devices can last for decades. Through partnerships with organizations in Chicago, Illinois–The Nature Conservancy, the Chicago Botanic Garden, and the Lincoln Park Zoo–the project demonstrates applicability by tackling stormwater management, urban wildlife surveillance, green roofs and other real-world applications. . Beyond cities, the work in this project will enable new applications in agriculture and smart farming, water resources management, and any applications where long term, zero maintenance embedded intelligence is required. Building Smart GI presents cyber-physical systems challenges in enabling robust inference, communication, and coordination on ultra-constrained computing platforms, and despite frequent power failures and dynamic energy availability. These devices harvest energy from soil or plant organic matter using terrestrial Microbial Fuel Cells (MFCs), constructed from inert materials providing decades long lifetime. These devices use machine learning to understand their environment, enabling robust, long-term monitoring that requires no maintenance or replacement. Finally, these devices actuate to "heal" the surrounding environment, switching the MFC from energy generation, to producing a disinfectant instead with the microbial community. A high-powered edge device orchestrates the actions of the swarm of MFC powered nodes, making decisions based on network data and external factors like user tasks. Project tasks include; co-design of the Terrestrial MFC and corresponding energy model for efficient and dynamic energy harvesting and; development of a tiny, resilient computing platform that harvests soil energy and supports sensing, and actuation; creation of a framework for conducting on-device machine learning to recognize subtle environmental changes from lossy data; exploration of orchestration of a network of intermittently powered devices; and conducting a series of end-to-end deployments with Chicago institutions.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.

全国各地的城市都在使基础设施更聪明，更可持续性和对极端天气事件的弹性更大。绿色基础设施（GI）是计划安装树木，植物，土壤，湿地和其他自然资源的一般术语，越来越多地安装在全国的城市中，以提供对洪水，下水道溢出，城市热量，空气污染，栖息地，栖息地，栖息地损失和沿海侵蚀的抗韧性，而传统的基础架构会影响传统的基础架构和负面影响。将智能设备嵌入GI中为城市带来了许多好处，从而洞悉了系统的健康和有效性以及城市的运营和适应性。但是，由于规模和长期部署的需求，将智能设备部署在GI中受到挑战，这意味着电池供电或昂贵的插入设备是不可行的。该项目建立了智能绿色基础设施；用无电池的智能设备增强GI，该设备由直接从土壤中收获的能量提供动力，这些能量彼此收集，推断，促进和协作。通过从可用的土壤和拆除电池中收获，这些设备可以持续数十年。通过与伊利诺伊州芝加哥的组织合作 - 自然保护协会，芝加哥植物园和林肯公园动物园 - 该项目通过解决雨水管理，城市野生动植物监视，绿色屋顶和其他现实世界应用来展示适用性。除了城市之外，该项目的工作还将在需要零件的智能和智能农业，水资源管理以及任何长期维护嵌入式情报的应用中启用新的应用程序。构建智能GI提出了网络物理系统在超约束计算平台上实现强大的推理，沟通和协调方面的挑战，并希望经常使用功率故障和动态能源可用性。这些设备使用陆地微生物燃料电池（MFC）从土壤或植物有机物中收集能量，该燃料材料可长期提供数十年的惰性材料。这些设备使用机器学习来了解其环境，从而实现了不需要维护或替换的长期监控。最后，这些设备激活以“治愈​​”周围环境，将MFC从能量产生切换，以与微生物群落产生消毒剂。高功率的边缘设备策划了MFC驱动节点群的动作，根据网络数据和外部因素（例如用户任务）做出决定。项目任务包括；陆地MFC和相应的能量模型的共同设计，以进行有效和动态的能量收获；开发一个微小的，耐药的计算平台，该平台收获土壤能量并支持感测和激活；创建用于进行设备机器学习的框架，以识别损失数据中微妙的环境变化；探索间歇性设备网络的编排；并通过芝加哥机构进行一系列端到端部署。该奖项反映了NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为是通过评估来获得的支持。

项目成果

Invited: Waving the Double-Edged Sword: Building Resilient CAVs with Edge and Cloud Computing

• DOI: 10.1109/dac56929.2023.10247809
• 发表时间: 2023-07
• 期刊: 2023 60th ACM/IEEE Design Automation Conference (DAC)
• 作者: Xiangguo Liu;Y. Luo;Anthony Goeckner;Trishna Chakraborty;Ruochen Jiao;Ningfei Wang;Yixuan Wang;Takami Sato;Qi Alfred Chen;Qi Zhu

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

• DOI: 10.1109/iros47612.2022.9981029
• 发表时间: 2022-03
• 期刊: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Ruochen Jiao;Xiangguo Liu;Bowen Zheng;Davis Liang;Qi Zhu

Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

• DOI: 10.1145/3486611.3486644
• 发表时间: 2021-06
• 期刊: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
• 作者: Shichao Xu;Yangyang Fu;Yixuan Wang;Zheng O’Neill;Qi Zhu

Safety-Assured Speculative Planning with Adaptive Prediction

• DOI: 10.1109/iros55552.2023.10341530
• 发表时间: 2023-07
• 期刊: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
• 作者: Xiangguo Liu;Ruochen Jiao;Yixuan Wang;Yimin Han;Bowen Zheng;Qi Zhu

DEJA VU: Continual Model Generalization For Unseen Domains

• DOI: 10.48550/arxiv.2301.10418
• 发表时间: 2023-01
• 期刊: ArXiv
• 作者: Chenxi Liu;Lixu Wang;Lingjuan Lyu;Chen Sun;Xiao Wang;Qi Zhu