CAREER: Robust and Adaptive Streaming Analytics for Sensorized Farms: Internet-of-Small-Things to the Rescue

职业：适用于传感农场的稳健且自适应的流分析：小型物联网的救援

• 批准号: 2146449
• 负责人: Somali Chaterji
• 金额: $ 55万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146449&HistoricalAwards=false
• 关键词: CAREER; Robust; Adaptive; Streaming; Analytics

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Today’s increasingly sensorized agricultural farms are composed of sensors and drones generating copious volumes of data. Two trends in computation have catalyzed the “Internet-of-Small-Things”, or IoST, in relation to digital and sustainable agriculture. First, the availability of inexpensive sensors that can withstand the rigors of agriculture. Second, the development of approximation algorithms for on-device computation of data analytics algorithms. In parallel, some demanding algorithms can be opportunistically offloaded to edge devices or to the cloud. There is an increasing trend to leverage the data from these “small” sensor nodes to actuate dependable, prompt, and resilient actions. Dependable means the algorithms need to deal with missing or corrupted data, network disruption, and node failures. Prompt refers to low-latency decisions, which are at par with the needs of the farmers or digital agriculture providers. The proposed project, Sirius, brings together IoST with machine learning (ML), and creates a compute fabric that is adaptive to the cyber and the physical conditions, and provides prompt actuation, resilient to noisy sensor nodes and communication channels.Sirius will achieve, for the first time, in the context of Cyber Physical Systems for digital agriculture: (1) On-device computation that will adapt to the computation capabilities of heterogeneous devices, to the network conditions, and to the contention on the devices due to co-located applications. (2) Approximate computation for heavyweight streaming analytics using a network of inherently unreliable sensor nodes. (3) Leverage the continuum of sensors, edge devices, and cloud to opportunistically adapt the computation to match the user requirements. This will then be extended to the recent server-less computing architectures and to drone swarms for mobile surveillance, sensing, and actuation. The outcomes of this research will have significant societal impacts in the area of sustainable agriculture. It will also propel education and investigation in multiple disciplines. This will range from data science for on-device analytics and actuation, innovative networking applications, approximating computer vision algorithms, and energy-efficient drone surveillance for agricultural applications. Further, this work will use gaming platforms and new data science courses to be offered in HBCUs and over interactive online platforms. This CPS CAREER proposal will also grow the infrastructure of US data scientists, in particular undergraduate and graduate students, including those from underrepresented minorities, and build strong cross-disciplinary expertise in machine learning, computer vision, and digital agriculture. Finally, the datasets that Sirius will create will be carefully curated and used in CPS-themed camps. The project will also leverage the strong industry linkages to facilitate translation of discoveries to usable prototypes.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。当今越来越多的传感器农业农场由传感器和无人机组成，产生大量数据。计算的两个趋势促进了与数字和可持续农业有关的“小事物互联网”（IoST）。首先，可以承受农业严酷条件的廉价传感器的可用性。第二，开发用于数据分析算法的设备上计算的近似算法。同时，一些要求苛刻的算法可以被机会主义地卸载到边缘设备或云。利用来自这些“小”传感器节点的数据来驱动可靠、及时和有弹性的动作的趋势越来越明显。依赖性意味着算法需要处理丢失或损坏的数据、网络中断和节点故障。提示是指低延迟决策，符合农民或数字农业提供商的需求。Sirius项目将物联网与机器学习（ML）结合在一起，创建一个计算结构，该结构适应网络和物理条件，并提供快速驱动，对嘈杂的传感器节点和通信信道具有弹性。Sirius将首次在数字农业的网络物理系统背景下实现：（1）设备上计算，其将适应于异构设备的计算能力、网络条件以及由于共处一地的应用而引起的设备上的争用。(2)使用固有不可靠传感器节点网络进行重量级流分析的近似计算。(3)利用传感器、边缘设备和云的连续性，适时调整计算以满足用户需求。然后，这将扩展到最近的无服务器计算架构和无人机群，用于移动的监视、传感和驱动。这项研究的成果将在可持续农业领域产生重大的社会影响。 它还将推动多学科的教育和研究。这将包括用于设备分析和驱动的数据科学、创新的网络应用、近似的计算机视觉算法以及用于农业应用的节能无人机监控。此外，这项工作将使用游戏平台和新的数据科学课程，这些课程将在HBCU和交互式在线平台上提供。CPS CAREER提案还将发展美国数据科学家的基础设施，特别是本科生和研究生，包括来自代表性不足的少数民族的学生，并在机器学习，计算机视觉和数字农业方面建立强大的跨学科专业知识。最后，Sirius将创建的数据集将被精心策划并用于CPS主题的营地。该项目还将利用强大的行业联系，促进将发现转化为可用的原型。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

KRATOS: Context-Aware Cell Type Classification and Interpretation using Joint Dimensionality Reduction and Clustering

• DOI: 10.1145/3534678.3539455
• 发表时间: 2022-08
• 期刊: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Zihan Zhou;Zijia Du;S. Chaterji

FLAIR: Defense against Model Poisoning Attack in Federated Learning

• DOI: 10.1145/3579856.3582836
• 发表时间: 2023-07
• 期刊: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security
• 作者: Atul Sharma;Wei Chen;Joshua C. Zhao;Qiang Qiu;S. Bagchi;S. Chaterji

ORION and the Three Rights: Sizing, Bundling, and Prewarming for Serverless DAGs

ORION 和三项权利：无服务器 DAG 的规模调整、捆绑和预热

• 发表时间: 2022
• 期刊: 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22
• 作者: Mahgoub, Ashraf;Yi, Edgardo Barsallo;Shankar, Karthick;Elnikety, Sameh;Chaterji, Somali;Bagchi, Saurabh
• 通讯作者: Bagchi, Saurabh

WISEFUSE: Workload Characterization and DAG Transformation for Serverless Workflows

WISEFUSE：无服务器工作流的工作负载特征和 DAG 转换

• DOI: 10.1145/3489048.3530959
• 发表时间: 2022
• 期刊: ACM SIGMETRICS
• 作者: Mahgoub, Ashraf;Yi, Edgardo Barsallo;Shankar, Karthick;Minocha, Eshaan;Elnikety, Sameh;Bagchi, Saurabh;Chaterji, Somali
• 通讯作者: Chaterji, Somali

Vega: Drone-based Multi-Altitude Target Detection for Autonomous Surveillance

• DOI: 10.1109/dcoss-iot58021.2023.00044
• 发表时间: 2023-06
• 期刊: 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)
• 作者: Akhil Bandarupalli;Sarthak Jain;Akash Melachuri;Joseph Pappas;S. Chaterji