  In animal monitoring applications, both animal detection and their movement
prediction are major tasks. While a variety of animal monitoring strategies
exist, most of them rely on mounting devices. However, in real world, it is
difficult to find these animals and install mounting devices. In this paper, we
propose an animal monitoring application by utilizing wireless sensor networks
(WSNs) and unmanned aerial vehicle (UAV). The objective of the application is
to detect locations of endangered species in large-scale wildlife areas and
monitor movement of animals without any attached devices. In this application,
sensors deployed throughout the observation area are responsible for gathering
animal information. The UAV flies above the observation area and collects the
information from sensors. To achieve the information efficiently, we propose a
path planning approach for the UAV based on a Markov decision process (MDP)
model. The UAV receives a certain amount of reward from an area if some animals
are detected at that location. We solve the MDP using Q-learning such that the
UAV prefers going to those areas that animals are detected before. Meanwhile,
the UAV explores other areas as well to cover the entire network and detects
changes in the animal positions. We first define the mathematical model
underlying the animal monitoring problem in terms of the value of information
(VoI) and rewards. We propose a network model including clusters of sensor
nodes and a single UAV that acts as a mobile sink and visits the clusters.
Then, one MDP-based path planning approach is designed to maximize the VoI
while reducing message delays. The effectiveness of the proposed approach is
evaluated using two real-world movement datasets of zebras and leopard.
Simulation results show that our approach outperforms greedy, random heuristics
and the path planning based on the traveling salesman problem.
