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.

全国各地的城市都投入了大量资金，使基础设施更智能，更可持续，更能抵御极端天气事件。绿色基础设施（GI）是对树木、植物、土壤、湿地和其他自然资源的有计划的设施的总称，它越来越多地安装在全国各地的城市中，以提供对洪水、下水道溢出、城市高温、空气污染、栖息地丧失和海岸侵蚀的恢复能力，这些都是压倒传统基础设施并对城市生活产生负面影响的。在GI中嵌入智能设备为城市带来了许多好处，可以深入了解系统的健康和有效性以及城市的运营和健康状况。然而，在GI中部署智能设备具有挑战性，因为规模和长期部署的需求，这意味着电池供电或昂贵的插入式设备是不可行的。该项目构建智能绿色基础设施;使用无电池智能设备增强GI，由直接从土壤中收集的能量供电，收集数据，推断，驱动和相互协作。通过从可自由获得的土壤中收获并移除电池，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--大自然保护协会、芝加哥植物园和林肯公园动物园--的合作，该项目通过解决雨水管理、城市野生动物监视、绿色屋顶和其他实际应用展示了其适用性。.除城市外，该项目的工作还将实现农业和智能农业、水资源管理以及任何需要长期零维护嵌入式智能的应用。构建智能地理信息系统提出了网络物理系统的挑战，在超约束计算平台上实现强大的推理，通信和协调，尽管经常停电和动态能源可用性。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获取能量，这些微生物燃料电池由惰性材料制成，寿命长达数十年。这些设备使用机器学习来了解其环境，从而实现强大的长期监控，无需维护或更换。最后，这些装置致动以“治愈”周围环境，将MFC从能量产生切换到产生消毒剂，而不是与微生物群落一起。一个高性能的边缘设备协调MFC驱动的节点群的动作，根据网络数据和用户任务等外部因素做出决策。项目任务包括：共同设计陆地MFC和相应的能量模型，以实现高效和动态的能量收集;开发一个小型的弹性计算平台，该平台收集土壤能量并支持传感和驱动;创建一个框架，用于进行设备上的机器学习，以从有损数据中识别微妙的环境变化;探索间歇供电设备网络的编排;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影响审查标准进行评估，被认为值得支持。

项目成果

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

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

Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems

• DOI: 10.1109/allerton58177.2023.10313451
• 发表时间: 2023-09
• 期刊: 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton)
• 作者: Qi Zhu;Wenchao Li;Chao Huang;Xin Chen;Weichao Zhou;Yixuan Wang;Jiajun Li;Feisi Fu

Protean: Adaptive Hardware-Accelerated Intermittent Computing

Protean：自适应硬件加速间歇计算

• DOI: 10.1145/3599184.3599186
• 发表时间: 2023
• 期刊: GetMobile: Mobile Computing and Communications
• 作者: Bakar, Abu;Goel, Rishabh;de Winkel, Jasper;Huang, Jason;Ahmed, Saad;Islam, Bashima;Pawelczak, Przemyslaw;Yildirim, Kasim Sinan;Hester, Josiah
• 通讯作者: Hester, Josiah

Accelerate online reinforcement learning for building HVAC control with heterogeneous expert guidances

• DOI: 10.1145/3563357.3564064
• 发表时间: 2022-11
• 期刊: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
• 作者: Shichao Xu;Yangyang Fu;Yixuan Wang;Zhuoran Yang;Zheng O’Neill;Zhaoran Wang;Qi Zhu