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

Purdue University, West Lafayette IN

Investigators

Abstract

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.