Low cost, LoRa based river water level data acquisition system.

In recent years, climate change and catchment degradation have negatively affected stage patterns in rivers which in turn have affected the availability of enough water for various ecosystems. To realize and quantify the effects of climate change and catchment degradation on rivers, water level monitoring is essential. Various effective infrastructures for river water level monitoring that have been developed and deployed in developing countries over the years, are often bulky, complex and expensive to build and maintain. Additionally, most are not equipped with communication hardware components which can enable wireless data transmission. This paper presents a river water level data acquisition system that improves on the effectiveness, size, deployment design and data transmission capabilities of systems being utilized. The main component of the system is a river water level sensor node. The node is based on the MultiTech mDot - an ARM-Mbed programmable, low power RF module - interfaced with an ultrasonic sensor for data acquisition. The data is transmitted via LoRaWAN and stored on servers. The quality of the stored raw data is controlled using various outlier detection and prediction machine learning models. Simplified firmware and easy to connect hardware make the sensor node design easy to develop. The developed sensor nodes were deployed along River Muringato in Nyeri, Kenya for a period of 18 months for continuous data collection. The results obtained showed that the developed system can practically and accurately obtain data that can be useful for analysis of river catchment areas.

DSAIL-Porini: Annotated camera trap image data of wildlife species from a conservancy in Kenya.

For years, zoologists, ecologists, and researchers at large have been using instruments such as camera traps in acquiring images of wild animals non-intrusively for ecological research. The main reason behind ecological research is to increase the understanding of various interactions in ecosystems while providing supporting data and information. Due to climate change and the destruction of animal habitats in recent years, researchers have been conducting studies on diminishing populations of various species of interest and the effectiveness of habitat restoration practices. By collecting and examining wild animal image data, inferences such as the health, breeding rate, and population of a particular species can be made. This paper presents an annotated camera trap dataset, DSAIL-Porini, consisting of images of wildlife species captured in a conservancy in Nyeri, Kenya. 6 wildlife species are captured in this dataset: impala, bushbuck, Sykes' monkey, defassa waterbuck, common warthog, and Burchell's zebra. This dataset was collected using camera traps based on the Raspberry Pi 2, Raspberry Pi Zero, and OpenMV Cam H7. It provides an example of images collected using relatively low-cost hardware platforms. The image dataset can be used in training and testing object detection and classification machine learning models.