Transforming animal tracking frameworks using wireless sensors and machine learning algorithms

Conventional animal tracking systems such as physical human observation, animal ear tagging or notching raises serious concerns over the observation and animal handling techniques that may sometimes cause stress and disruptions to animal ecology. Wireless sensor networks on the other hand hold real promise for animal tracking due to their accuracy, scalability, and ethical consideration frameworks involved. To test machine learning algorithms in a wireless sensor framework, a simulation was carried out to illustrate the behavior of a Wireless sensor network to draw conclusions. Advanced data algorithms and Python features was adopted to emulate the behavior of a wireless sensor network from cattle datasets sourced from the repository of Ireland’s government Department of Agriculture, Food and Marine which contains 3,503 records of cattle in various areas in Europe. The capacities of different algorithms for location estimation and assessment of performance were also analyzed and the results demonstrates great potentials of a WSN for efficiency in farm monitoring, where parameters such as location and sensor accuracy can be monitored in real time.