Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System

Authors

Yemeserach Taye MekonnenFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical and Computer Engineering

First Advisor's Name

Arif Sarwat

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Shekhar Bhansali

Second Advisor's Committee Title

Committee Co-chair

Third Advisor's Name

Krish Jayachandran

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Alexander Perez-Pons

Fourth Advisor's Committee Title

Committee member

Date of Defense

11-4-2019

Abstract

As the global population soars from today’s 7.3 billion to an estimated 10 billion by 2050, the demand for Food, Energy, and Water (FEW) resources is expected to more than double. Such a sharp increase in demand for FEW resources will undoubtedly be one of the biggest global challenges. The management of food, energy, water for smart, sustainable cities involves a multi-scale problem. The interactions of these three dynamic infrastructures require a robust mathematical framework for analysis. Two critical solutions for this challenge are focused on technology innovation on systems that integrate food-energy-water and computational models that can quantify the FEW nexus. Information Communication Technology (ICT) and the Internet of Things (IoT) technologies are innovations that will play critical roles in addressing the FEW nexus stress in an integrated way. The use of sensors and IoT devices will be essential in moving us to a path of more productivity and sustainability. Recent advancements in IoT, Wireless Sensor Networks (WSN), and ICT are one lever that can address some of the environmental, economic, and technical challenges and opportunities in this sector. This dissertation focuses on quantifying and modeling the nexus by proposing a Leontief input-output model unique to food-energy-water interacting systems. It investigates linkage and interdependency as demand for resource changes based on quantifiable data. The interdependence of FEW components was measured by their direct and indirect linkage magnitude for each interaction. This work contributes to the critical domain required to develop a unique integrated interdependency model of a FEW system shying away from the piece-meal approach. The physical prototype for the integrated FEW system is a smart urban farm that is optimized and built for the experimental portion of this dissertation. The prototype is equipped with an automated smart irrigation system that uses real-time data from wireless sensor networks to schedule irrigation. These wireless sensor nodes are allocated for monitoring soil moisture, temperature, solar radiation, humidity utilizing sensors embedded in the root area of the crops and around the testbed. The system consistently collected data from the three critical sources; energy, water, and food. From this physical model, the data collected was structured into three categories. Food data consists of: physical plant growth, yield productivity, and leaf measurement. Soil and environment parameters include; soil moisture and temperature, ambient temperature, solar radiation. Weather data consists of rainfall, wind direction, and speed. Energy data include voltage, current, watts from both generation and consumption end. Water data include flow rate. The system provides off-grid clean PV energy for all energy demands of farming purposes, such as irrigation and devices in the wireless sensor networks. Future reliability of the off-grid power system is addressed by investigating the state of charge, state of health, and aging mechanism of the backup battery units. The reliability assessment of the lead-acid battery is evaluated using Weibull parametric distribution analysis model to estimate the service life of the battery under different operating parameters and temperatures. Machine learning algorithms are implemented on sensor data acquired from the experimental and physical models to predict crop yield. Further correlation analysis and variable interaction effects on crop yield are investigated.

Identifier

FIDC008865

ORCID

0000-0003-2773-8745

Previously Published In

1. Y. Mekonnen, A. Sarwat, S. Bhanslai, (2019). Food, Energy and Water (FEW) Nexus Modeling and Analysis, Proceedings of the Future Technologies Conference, Springer 2019
2. Y. Mekonnen, H. Aburub, A. Sarwat, (2018). Life Cycle Prediction of Sealed Lead Acid Batteries Based on a Weibull Model, Journal of Energy Storage 18, (467-475)
3. Y. Mekonnen, L. Burton, A. Sarwat, S. Bhansali, (2018, Oct.). IoT Sensor Network Approach for Smart Farming: An Application in Food, Energy and Water System, IEEE Global Humanitarian Technology Conference (GHTC), (1-5)
4. Y. Mekonnen, A. Sarwat, (2017, June). Renewable Energy Supported Microgrid in Rural Electrification of Sub-Saharan Africa, IEEE PES PowerAfrica, (595-599)

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

Recommended Citation

Mekonnen, Yemeserach Taye, "Edge IoT Driven Framework for Experimental Investigation and Computational Modeling of Integrated Food, Energy, and Water System" (2019). FIU Electronic Theses and Dissertations. 4298.
https://digitalcommons.fiu.edu/etd/4298