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Designing Low-Cost Technology for Coastal Ecosystem Science


Term:

Spring

Department:

MAS: Media Arts and Sciences

Faculty Supervisor:

Danielle Wood

Faculty email:

drwood@media.mit.edu

Apply by:

2/10/2020

Contact:

Ufuoma Ovienmhada: ufuoma@mit.edu

Project Description

Coastal ecosystems provide many benefits to humans in the form of transportation, subsistence, and economic opportunity. Effective management is important to protect the ecological balance of these ecosystems and the benefits that humans derive from them. Decision Support Systems (DSS), informed by rich environmental and socioeconomic data can help inform effective management. However, data cannot always be incorporated into the decision-making workflow for a multitude of reasons from awareness, to interpretability, accessibility and cost. The objective of this project is to develop techniques that enable low cost data collection for coastal ecosystems. This is researched through a case study on Lake Nokoue in Benin Republic, a country that has been harmed by the infestation of an invasive plant species known as the water hyacinth. The UROP will support this objective through the design of a sensor kit that measures key water quality parameters known to affect the water hyacinth. The kit has gone through several iterations. The latest needs are to calibrate the sensors, debug the software, enable cellular data transmission, and design waterproofing precautions. These tasks will involve coordination with collaborators in Benin. The data from the water sensor kit combined with Satellite Earth Observation will feed into an overall Decision Support System to improve environmental management of the water hyacinth in Benin. The option to renew for summer or fall may be available.

Pre-requisites

We are looking for students with a technical background in mechanical or electrical engineering, computer science/data science or related disciplines. In addition to this technical background, we seek students with an interest in sustainable development. Preferred: coding proficiency in Arduino/C++, Intermediate/Fluent French