Authors: Bo Sun; Farhad Ahmed; Frank Sun; Qin Qian; Yang Xiao
Addresses: Department of Computer Science, Lamar University, P.O. Box 10056, Beaumont, TX 77710, USA ' Department of Computer Science, Lamar University, P.O. Box 10056, Beaumont, TX 77710, USA ' Department of Computer Science, Lamar University, P.O. Box 10056, Beaumont, TX 77710, USA ' Department of Civil Engineering, Lamar University, P.O. Box 10024, Beaumont, TX 77710, USA ' School of Computer & Software, Nanjing University of Information Science & Technology, Nanjing, 210044, China; Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487, USA
Abstract: Sustainable water management decisions are often made with the support of water quantity and water quality models with a focus on prediction uncertainty. Unfortunately, limited observational data severely constrains the design of accurate water models for these decisions. This paper presents our initial efforts to deploy STORM 3 data loggers and a wireless sensor network (WSN) to collect real-time and in-situ data at fine temporal granularities to monitor the pond at Lamar University in Beaumont, TX. Specifically, we present the details about how to set up STORM 3, integrate H-377 water temperature sensor probe from WaterLOG, and validate collected water temperature data. We further explain our prototype WSN and a variety of third-party probes to collect water Dissolved Oxygen (DO) and Water pH values. Our deployed STORM 3 and NI-based WSN have been able to collect water temperature, DO, and pH values consistently and periodically in a real-time manner.
Keywords: wireless sensor networks; WSNs; STORM 3 data logger; water quality monitoring; sustainable water management; water modelling; water temperature sensors; water dissolved oxygen; water pH values.
International Journal of Sensor Networks, 2016 Vol.20 No.1, pp.26 - 36
Available online: 20 Jan 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article