Environmental monitoring stations remain sparse and costly in low-resource environments especially Sub Saharan Africa (SSA), leading to gaps in high-resolution local data. This research aims to build low-cost low-power remote sensor technologies for reliable monitoring of significant environment parameters. This will facilitate collection and maintenance of large open datasets to assist in characterization of environmental phenomena and forecasting of potential extreme environmental conditions in low-resource environments. This work demonstrates how AI, IoT and embedded systems can be leveraged for reliable environmental monitoring and early warning by supporting proactive and timely response to environmental hazards and mitigating the adverse impact of climate change in low-resource settings.
