Development of an IoT Water Quality Monitoring System for Data-Based Aquaculture Siting

Compared to historical levels, the oyster population in North Carolina has remained low, especially when compared with neighboring regions (e.g., Virginia). Therefore, to enable efficient aquaculture siting for shellfish fisheries, accurate measurement information about the suitability for a targeted location is needed to promote efficient use of resources. Currently available commercial measurement products are often expensive, which limits the amount of measurement points that can be achieved for a given cost point. There are some software-based aquaculture siting tools available, but they are limited in accuracy and utility by the amount of available and current measurement data in a particular area. To help promote data driven decision making, additional measurements are desirable. An internet of things (IoT) approach has been used effectively in other sectors, such as agriculture. This paper presents a similar IoT approach for aquaculture via a proposed water quality monitoring system with design features that are targeted for aquaculture siting applications (i.e., shellfish fisheries). The current challenges associated with aquaculture siting applications are discussed with a survey of currently available resources (e.g., commercial measurement systems and siting tools). This work presents the design requirements for the proposed water quality monitoring system, the design features, and analysis of the design features that validates the design requirements are satisfied.