Award Abstract # 1952247
SCC-IRG-Track2: Creating an Extensible Data Exchange and Analytics Sandbox for Smart Water infrastructures

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITY OF CALIFORNIA IRVINE
Initial Amendment Date: August 14, 2020
Latest Amendment Date: September 2, 2021
Award Number: 1952247
Award Instrument: Standard Grant
Program Manager: Vishal Sharma
vsharma@nsf.gov
 (703)292-0000
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2020
End Date: September 30, 2025 (Estimated)
Total Intended Award Amount: $1,499,943.00
Total Awarded Amount to Date: $1,499,943.00
Funds Obligated to Date: FY 2020 = $1,499,943.00
History of Investigator:
  • Nalini Venkatasubramanian (Principal Investigator)
    nalini@ics.uci.edu
  • Sharad Mehrotra (Co-Principal Investigator)
  • Shangping Ren (Co-Principal Investigator)
  • David Feldman (Co-Principal Investigator)
  • Ronald Eguchi (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-Irvine
160 ALDRICH HALL
IRVINE
CA  US  92697-0001
(949)824-7295
Sponsor Congressional District: 47
Primary Place of Performance: University of California-Irvine
141 Innovation Drive, Ste 250
Irvine
CA  US  92617-3213
Primary Place of Performance
Congressional District:
47
Unique Entity Identifier (UEI): MJC5FCYQTPE6
Parent UEI: MJC5FCYQTPE6
NSF Program(s): S&CC: Smart & Connected Commun
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 042Z
Program Element Code(s): 033Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

The importance of water to civilization is unquestionable; over centuries, this critical community lifeline has become complex with multiple subsystems (drinking water(DW), wastewater(WW), and stormwater(SW)) to import, deliver and haul away water. Today, these infrastructures are designed and operated separately by an array of local governments, water districts, and regulatory agencies - all subjected to stress caused by aging, urbanization, failures, extreme events, and demand/supply variabilities. This proposal brings together an interdisciplinary team of researchers and practitioners in computer science, civil engineering, public policy, and social ecology to create a Smart Water data-exchange framework, SWADE, that will serve as a repository and sandbox for collecting, sharing, exploring, analyzing, and curating information about diverse community water systems.

SWADE will utilize recent advances in IoT and big data systems to create a holistic understanding of these interacting platforms - the framework integrates static and dynamic data from infrastructures and communities with domain-specific models/simulators and analytics services to create new levels of efficiency and resilience in co-executing systems. Innovative research will address tradeoffs (e.g. cost, accuracy) in data collection, develop semantic approaches for joint data representation and storage, explore data cleaning and refinement mechanisms, promote community engagement to drive policy-based exchange to address data-sharing barriers and design novel analytics to understand resilience and societal impact of water policies. Innovations to existing infrastructures require public acceptance; to achieve this, the team includes practitioners at water agencies in Southern California (e.g. Orange County, Irvine, Los Angeles) and Illinois who will help create and instantiate the SWADE framework; interactions with agencies in Florida and Maryland will help ensure transferability of SWADE.

Through SWADE, communities around the nation can learn and share lessons with each other, experiment with sample data/networks to understand design choices as they plan future investment in water systems. This project can help guide policy research on information interchange in other complex community infrastructures (e.g. water-energy-food nexus, transportation networks) where socioeconomic and geopolitical constraints play a role in determining what can be shared and exchanged. Educational outreach will leverage efforts of the Water UCI Center, and campus programs including RET, REU, K-12, and women in STEM programs at UCI and SDSU. Our programs will focus on promoting broader participation by allowing citizens from diverse backgrounds and perspectives to contribute to the essential research mission of ensuring safe and reliable water services for the future.

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chio, Andrew and Peng, Jian and Venkatasubramanian, Nalini "STEP: Semantics-Aware Sensor Placement for Monitoring Community-Scale Infrastructure" , 2023 https://doi.org/10.1145/3600100.3623752 Citation Details
Rahman, Rummana and Lin, CH and Hsu, Chenghsin and Venkatasubramanian, Nalini "A Measurement-informed Approach to Modeling Underground IoT Communications" , 2024 Citation Details
Venkatasubramanian, Nalini and Davis, Craig A and Eguchi, Ronald T. "Designing community-based intelligent systems for water infrastructure resilience" In 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC20), November 36, 2020, Seattle, WA, USA. ACM, New York, NY, USA, , 2020 https://doi.org/10.1145/3423455.3430318 Citation Details
Venkateswaran, Praveen and Benson, Kyle E. and Hsieh, Chia-Ying and Hsu, Cheng-Hsin and Mehrotra, Sharad and Venkatasubramanian, Nalini "REAM: A Framework for Resource Efficient Adaptive Monitoring of Community Spaces" Pervasive and Mobile Computing , v.76 , 2021 https://doi.org/10.1016/j.pmcj.2021.101459 Citation Details
Venkateswaran, Praveen and Hsu, Cheng-Hsin and Mehrotra, Sharad and Venkatasubramanian, Nalini "REAM: Resource Efficient Adaptive Monitoring of Community Spaces at the Edge Using Reinforcement Learning" 2020 IEEE International Conference on Smart Computing (SMARTCOMP) , 2020 https://doi.org/10.1109/SMARTCOMP50058.2020.00023 Citation Details
Venkateswaran, Praveen and Isahagian, Vatche and Muthusamy, Vinod and Venkatasubramanian, Nalini "FedGen: Generalizable Federated Learning for Sequential Data" , 2023 https://doi.org/10.1109/CLOUD60044.2023.00044 Citation Details
Zhang, GuangXue and Feldman, David L and Lin, Yiming and Mehrotra, Sharad and Venkatasubramanian, Nalini and Drew, Thayer and Sentovich, Kim and Veranth, Owen "Water-COLOR: Water-COnservation using a Learning-based Optimized Recommender" , 2024 https://doi.org/10.1109/SMARTCOMP61445.2024.00034 Citation Details
Zhang, Zhenyu and Guo, Chunhui and Peng, Wenyu and Ren, Shangping "Using Event Log Timing Information to Assist Process Scenario Discoveries" 2020 IEEE Third International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) , 2020 https://doi.org/10.1109/AIKE48582.2020.00017 Citation Details
Zhang, Zhenyu and Guo, Chunhui and Ren, Shangping "Mining Timing Constraints from Event Logs for Process Model" 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) , 2020 https://doi.org/10.1109/COMPSAC48688.2020.0-139 Citation Details
Zhang, Zhenyu and Hildebrant, Ryan and Asgarinejad, Fatemeh and Venkatasubramanian, Nalini and Ren, Shangping "Improving Process Discovery Results by Filtering Out Outliers from Event Logs with Hidden Markov Models" 2021 IEEE 23rd Conference on Business Informatics (CBI) , 2021 https://doi.org/10.1109/CBI52690.2021.00028 Citation Details

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