A case study of an autonomous wireless sensor network system for environmental data collection. Issue 1 (1st August 2017)

• Record Type: Journal Article
• Authors: Dong, Ziqian; Meyland, Sarah; Karaomeroglu, Muhammet
• Is Part Of: Environmental progress & sustainable energy. Volume 37:Issue 1(2018)
• Page Start: 180
• Page End: 188
• Publication Date: 2017-08-01
• Subjects: FEW -- wireless sensor network -- unmanned aerial vehicle -- sensing -- water quality data -- autonomous -- nutrient; Environmental engineering -- Periodicals; Sustainable engineering -- Periodicals; Environmental chemistry -- Periodicals; 333.7
• DOI: 10.1002/ep.12716
• Languages: English
• ISSNs: 1944-7442

Abstract

Abstract : This article presents a case study of the design of an autonomous wireless sensor network for environmental monitoring to provide data for understanding the relationships among food, energy, and water systems in suburban areas in Long Island New York. The study focuses on the existing water quality data for the two counties on Long Island, Nassau and Suffolk Counties, to understand the impact of energy and food production on the coastal waters of Long Island Sound and Peconic Bay. The analysis of existing data from the US Environmental Protection Agency database reveals the challenges in using such data to understand the relationships between water quality, weather events and identified point sources of pollutants. Maintaining on‐going data collection programs is expensive, time‐consuming and personnel intensive which is why this project examines new ways to obtain and utilize environmental data. The researchers propose the design of an autonomous wireless sensor network environmental monitoring system with an optimal sensor placement strategy to identify targeted locations for water quality monitoring. The goal is to provide environmental data that can facilitate a better understanding of the interactions of farming activities and energy production resulting in undesirable impacts on water quality. The proposed design can be used to provide real‐time data to help stakeholders improve efficiency in resource management and advance early detection of potential hazardous events. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 180–188, 2018