An IoT-Based Eidolon helvum Echolocation and Environmental Dataset

Published: 20 May 2024| Version 1 | DOI: 10.17632/m9s9nkzmpf.1
Halleluyah Aworinde,


Bats occupy a strategic position in our ecosystem and as such, considered bio indicators. They represent more than 1,400 different species, or nearly 20% of all recognized mammal species worldwide. They possess extremely outstanding qualities such as flight, echolocation, extreme longevity, and unique immunity; they are known to be the only flying mammal. These mammals exist in many parts of the globe except in the cold regions; they play a significant role in pollinating flowers and dispersing seeds, they are highly useful in reforestation. Insight into the life of these mammals can help ensure protection, restoration, and promotion of sustainable use of terrestrial ecosystem and biodiversity as it relates to sustainable development goal (SDG) 15. With Artificial intelligence (AI) playing important roles in curation of data on movement patterns, wildlife observations, monitoring ecological population and as well, understanding behaviours, this research dataset is aimed at understanding several activities of the Eidolon helvum fruit bats as it relates the effect their choice of roosting location has on soil nutrient; the state of health of the trees they roost on, and the implication of various sound signal they make in different situations and at different times of the day (echolocation). The dataset contains parameters such as Temperature, Humidity, Light Intensity and Time of Flight. These parameters are meant for mapping the correlation between the fruit bats' behavioural pattern and their environment or where they are located.


Steps to reproduce

To achieve these targets, we proposed monitoring the soil nutrient sensor, the sound which intend to categorize the Fruit Bat Sound for different times of the day and distress calls. The study also proposed monitoring health status of the roosting trees as well as the environmental temperature and relative humidity. Three (3) different TinyML sensors provided by Seeed Studio under the 2023 ITU AI-5G TinyML Challenge were used to gather dataset over a period of five (5) days. Temperature and Humidity Sensor Pro (DHT22/AM2303) SKU 101020019; Light Sensor (v1.2 LS06-S); Time of Flight Distance Sensor (VL53L0X) and sound sensor. The dataset contains 47,371 rows across 5 columns based on the parameters used viz Day, Temperature, Humidity, Light Intensity and Time of Flight.


Bowen University


Computer Science, Artificial Intelligence, Data Science, Environmental Bioengineering, Mechatronics, Computational Biology, Ecological Analysis