Processing of a Low Cost, Energy Efficient Internet of Things Harmful Algal Bloom Monitoring Buoy
Mentor 1
Todd Miller
Start Date
1-5-2020 12:00 AM
Description
Harmful algal blooms (HABs) are large accumulations of toxin algae in lakes and oceans and an ever - increasing problem in water bodies globally. Not only do they negatively affect the ecosystems they inhabit but also the surrounding area. Generally, the public, water resource managers, and public health professionals demand economically efficient and timely warnings of HABs in local waterways. The development of a low cost, energy efficient buoy capable of detecting HABs benefits both scientists and the general public alike. Our goal is to produce an economical scientific buoy that can track HABs within a water body in real time while also providing a platform that is web accessible for the general public to view the real time data. On top of this, the buoys will utilize a suite of sensors that each gather numerous forms of data on the potential for HABs in a given area. Once each sensor has collected data in situ, it is organized and saved to an onboard SD card and sent to an Internet of Things (IoT) cloud computing platform via a mounted cellular modem. We present the results from initial testing of different hardware configurations and development of software to efficiently run the buoy system and send data to the IoT database.
Processing of a Low Cost, Energy Efficient Internet of Things Harmful Algal Bloom Monitoring Buoy
Harmful algal blooms (HABs) are large accumulations of toxin algae in lakes and oceans and an ever - increasing problem in water bodies globally. Not only do they negatively affect the ecosystems they inhabit but also the surrounding area. Generally, the public, water resource managers, and public health professionals demand economically efficient and timely warnings of HABs in local waterways. The development of a low cost, energy efficient buoy capable of detecting HABs benefits both scientists and the general public alike. Our goal is to produce an economical scientific buoy that can track HABs within a water body in real time while also providing a platform that is web accessible for the general public to view the real time data. On top of this, the buoys will utilize a suite of sensors that each gather numerous forms of data on the potential for HABs in a given area. Once each sensor has collected data in situ, it is organized and saved to an onboard SD card and sent to an Internet of Things (IoT) cloud computing platform via a mounted cellular modem. We present the results from initial testing of different hardware configurations and development of software to efficiently run the buoy system and send data to the IoT database.
