Project Overview

UCSB SmartFarm is a research project that investigates the design and implementation of an open source, hybrid cloud approach to agriculture analytics for enabling sustainable farming practices. SmartFarm

• Integrates disparate environmental and Internet-of-Things (IoT) sensor technologies into an on-farm, private cloud software infrastructure that ensures that all private data remain under the control of farmers.
• Provides farmers with a secure, easy to use, low-cost data analysis system.
• Couples data from external cloud sources (weather predictions, satellite imagery, state and national datasets, etc) with farm-local statistics.
• Provides an interface into which custom analytics apps can be plugged (via an AppStore model).

The research that we pursue related to this project includes the design, implementation, and empirical evaluation of

• Low-cost, robust sensing devices and intermediate nodes (intercessors) for the Internet of Things (IoT) using off-the-shelf components,
• Self-managing edge cloud systems and their integration into multi-tier (sensing, edge, cloud) IoT systems,
• Characterization and optimization of performance interference across big/fast data frameworks for resource constrained, multi-analytics edge clouds,
• New programming models, virtualization techniques, and communications protocols for multi-tier IoT systems, and
• Novel analytics and machine learning services for decision support.
• Intelligent drone and robotics systems
• Extreme energy efficiency and sustainable computing

Contact us if you are interested in collaborating!

Team

• PIs: Chandra Krintz and Rich Wolski
• Collaborators:
  • UCSB NRS Sedgwick Reserve
  • Olivier Jarphagnon and AgMonitor
  • Bruce Roberts (Fresno State)
  • Balaji Sethuramasamyraja (Fresno State)
  • Bo Liu (Cal Poly SLO)
• Students

Support

• NSF CNS-2107101 (Detroit)
• NSF CNS-1703560 (DatGeo)
• NSF ACI-1541215 (Aristotle)
• NSF CCF-1539586 (SmartFarm)
• The California Energy Commission

Publications and Presentations

• SmartFarm Overview
• P. Guan, A. Dangwal, A. Taherkordi, R. Wolski, and C. Krintz, Energy-Aware IoT Deployment Planning (PDF), ACM Computing Frontiers, May 2024
• P. Rolshausen, C. Krintz, R. Wolski, M. Roose, and A. El-Kereamy. Prospects for Farming Citrus Under Protective Screen Page 38 in the Fall 2023 CRB Citrograph (PDF) Fall 2023
• G. Mundewadi, R. Wolski, and C. Krintz, Data Acquisition and Analysis for Improving the Utility of Low Cost Soil Moisture Sensors (PDF), IEEE SmartAGR (at IEEE SmartComp), doi, Jun 2023
• M. Zhang, C. Krintz, and R. Wolski, Sparta: A Heat-Budget-based Scheduling Framework on IoT Edge Systems (PDF), International Conference on Edge Computing (EDGE), Sep 2021
• M. Zhang, C. Krintz, and R. Wolski, Edge-Adaptable Serverless Acceleration for Machine Learning IoT Applications (PDF), Software: Practice and Experience: Special Issue on Elastic Computing from Edge to the Cloud, 2020, DOI 10.1002/spe.2944
• G. George, F. Bakir, C. Krintz, and R. Wolski, NanoLambda: Implementing Functions as a Service at All Resource Scales for the Internet of Things, (PDF) (pres), ACM Symposium on Edge Computing (SEC), Nov 2020
• M. Zhang, C. Krintz, and R. Wolski, STOIC: Serverless TeleOperable Hybrid Cloud for Machine Learning Applications on Edge Device (PDF), IEEE SmartEdge, Mar 2020
• N. Golubovic, R. Wolski, C. Krintz and M. Mock, Improving the Accuracy of Outdoor Temperature Prediction by IoT Devices (PDF), IEEE Conference on IoT (ICIOT), July 2019, Won Best Paper Award!
• N. Golubovic, C. Krintz, R. Wolski, B. Sethuramasamyraja, and B. Liu, A Scalable System for Executing and Scoring K-Means Clustering Techniques and Its Impact on Applications in Agriculture, (PDF), International Journal of Big Data Intelligence, Vol. 6, Nos. 3/4, 2019
• R. Wolski, C. Krintz, F. Bakir, G. George, and W-T. Lin, CSPOT: Portable, Multi-scale Functions-as-a-Service for IoT (PDF), ACM Symposium on Edge Computing (SEC), Nov 2019
• F. Bakir, R. Wolski, C. Krintz, and G. Sankar Ramachandran, Devices-as-Services: Rethinking Scalable Service Architectures for the Internet of Things (PDF), USENIX HotEdge, July 2019
• W-T. Lin, F. Bakir, C. Krintz, R. Wolski, and M. Mock, Data repair for Distributed, Event-based IoT Applications (PDF), ACM International Conference On Distributed and Event-Based Systems, June 2019
• C. Krintz, R. Wolski, N. Golubovic, and F. Bakir, Estimating Outdoor Temperature from CPU Temperature for IoT Applications in Agriculture, (PDF), International Conference on the Internet of Things (IoT), Oct 2018
• Wei-Tsung Lin, Chandra Krintz, and Rich Wolski, Tracing Function Dependencies Across Clouds, (PDF), IEEE Cloud, July 2018
• N. Golubovic, A. Gill, C. Krintz, and R. Wolski, CENTAURUS: A Cloud Service for K-means Clustering (for Applications in Agriculture), (PDF), IEEE DataCom 2017
• S. Shekhar, J. Colletti, F. Munoz-Arriola, L. Ramaswamy, C. Krintz, L. Varshney, D. Richardson, Intelligent Infrastructure for Smart Agriculture: An Integrated Food, Energy and Water System, (PDF), CRA CCC Catalyst, April, 2017
• A. Rosales Elias, N. Golubovic, R. Wolski, and C. Krintz, Where's The Bear? -- Automating Wildlife Image Processing Using IoT and Edge Cloud Systems (PDF), ACM Conference on IoT Design and Implementation, April, 2017; was UCSB TR2016-07
  • Presentation
• N. Golubovic, C. Krintz, R. Wolski, S. Lafia, T. Hervey, and W. Kuhn, Extracting Spatial Information from Social Media in Support of Agricultural Management Decisions (PDF), ACM SIGSPATIAL Workshop on Geographic Information Retrieval, October, 2016
• C. Krintz, R. Wolski, N. Golubovic, B. Lampel, V. Kulkarni, B. Sethuramasamyraja, B. Roberts, and B. Liu, SmartFarm: Improving Agriculture Sustainability Using Modern Information Technology (PDF), KDD 2016 Workshop on Data Science for Food, Energy, and Water (DSFEW), August, 2016
• SmartFarm Overview
• 2016 SmartFarm Presentation
• The UCSB Lab for Research on Adaptive Computing Environments (RACELab)
• Related project: Where's the Bear (WTB)
• Related project: Next Generation Cloud Systems