1. Data Challenges and Real-time Autonomous Sensing Systems
Ziqian (Cecilia) Dong, Ph.D.
Associate Professor
Department of Electrical and Computer Engineering
New York Institute of Technology, New York, NY 10023

2. Challenges in modeling for FEW nexus
Data
Where and how do we get data?
How much data?
How reliable are the data?
How do we validate the data?
Who’s going to use it?
What is it used for?

3. Real-time autonomous data collection
Smart city infrastructure backed by Internet of Things
Smart sensors (pressure, toxicity, dissolved oxygen, heavy metal, turbidity, etc.), energy meters (energy usage, demand and supply)
Inventory tracking on food distribution systems
Weather radar, satellite imaging
Economic and demographic data
An informed FEW management system

4. Where to collect data? Water supply, distribution system
Food production and distribution
Energy production and consumption
Waste disposal or treatment (food, water, energy)

5. How can we use the data?
Identify inefficiencies in the existing systems: leaks, wastes,
Optimize resource allocation
- Strategically water resource allocation -> intelligent water management system
Scientific modeling at different scales, from micro -> macro

6. NY Harbor Water Sampling data
70 sampling stations
Source:

7. A case study of existing water quality data from EPA: Long Island
Analyzed the existing water quality data from Environmental Protection Agency database for Long Island
Exposed the challenges in data to understand the correlation of water quality with weather events and identifying point sources of pollutants
Z. Dong, S. Meyland and M. Karaomeroglu, “FEW: A case study of an autonomous wireless sensor network system for environmental data collection,” Environmental Progress and Sustainable Energy, Version of Record online: 1 Aug. 2017, DOI: /ep.12716

8. A case study of existing water quality data from EPA: Long Island

9. Water quality indicators at one location in Long Island Sound

10. Macro -> Micro scale sensing
Z. Dong, F. Li, B. Beheshti, A. Mickelson, M. Panero and N. Anid, “Autonomous Real-time Water Quality Sensing as an Alternative to Conventional Monitoring to Improve the Detection of Food, Energy, and Water Indicators,” Journal of Environmental Studies and Sciences, March 2016, pp. 1-8.

11. Sustainable Communication Network Design

12. Research Challenges Sensors Computing module Processor, memory
Communication module
Power unit
Power supply for wireless sensors
Communication overhead for wireless sensor networks
Unreliable data communication may cause data sets with large amount of missing data
How to estimate the missing values from the known measurements?

13. Current and Future work
Autonomous real-timer water quality sensing system
Validate and correlate measurement data
Use the data in the FEW models that is scalable to megacities – agent-based models

14. References
[1] Cisco, IoE-Driven Smart City Barcelona Initiative Cuts Water Bills, Boosts Parking Revenues, Creates Jobs and More, Online:
[2] IBM, Water Management for a Smarter Planet, Online:
[3] Ziqian Dong, S. Anand, and R. Chandramouli, “Estimation of Missing RTTs in Computer Networks: Matrix Completion vs Compressed Sensing,” Computer Networks, Vol. 55, Issue 15, October 2011.
[4] Ziqian Dong, Sarah Meyland and Muhammet Karaomeroglu, “FEW: A case study of an autonomous wireless sensor network system for environmental data collection,” Environmental Progress and Sustainable Energy, Version of Record online: 1 Aug. 2017, DOI: /ep.12716
[5] Ziqian Dong, Fang Li, Babak Beheshti, Alan Mickelson, Marta Panero and Nada Anid, “Autonomous Real-time Water Quality Sensing as an Alternative to Conventional Monitoring to Improve the Detection of Food, Energy, and Water Indicators,” Journal of Environmental Studies and Sciences, March 2016, pp. 1-8.

15. Thank you Questions
Prepare slides for the appendix in the event that more details or supplemental slides are needed. The appendix is also useful if the presentation is distributed later.