Constructing intelligent drone systems to monitor environmental conditions

Abstract

Durban is the third largest South African economic hub after Johannesburg and Cape Town. Durban houses the largest port harbour in Africa. The port generates massive road cargo to and from all over the continent. Furthermore, it is through the Durban South Basin that crude oil is imported, refined and then transported to the rest of the country by road or special dedicated pipelines. All of these have a significant impact on the local environmental. Durban University of Technology is one of 26 academic institutions producing future graduates for the nation. Literature informs that only Environmental Science students write or talk about the environment with authority. There is therefore a need to inculcate an environmental awareness by demonstrating actions have consequence to the environment that we work and study in. The aim of the project is to develop a frugal mobile environmental data collector by embedding or installing sensors onto an Unmanned Aerial Vehicle, together with a microcontroller and transmission module for data collection and transmission to the user for viewing and analysis. The main objective of this project is to assist in obtaining distinct environmental information from different layers of the atmosphere, from different areas through difficult terrains some of which are alternatively hazardous or populated spaces. The research methodology and design was guided by the Agile Design Science Research Methodology because of the need to combine information technology, engineering and environmental science. Furthermore, the use of data analytics-based algorithms in an environmental monitoring scenario was adopted for analysing and making educated decisions regarding environmental conditions. The k-means method was compared to the Silhouette index, Davies-Bouldin index, and Dunn's index, which are all well-known distance metrics. The evaluation's findings suggest that the well-known k-means algorithm performed effectively in the environmental condition dataset analysis, implying that the environmental condition of the collected data is normal. The results show the construction of a frugal drone to undertake environmental data gathering as well as data analytics using artificial intelligence methods such as k-means is possible. The multidisciplinary model should be piloted in other environments located at hospitals, industrial zones, and the port itself.