# Providing Distribution Estimation for Animal Tracking with Unmanned Aerial Vehicles

### Cite as:

J. Xu, G. Solmaz, R. Rahmatizadeh, L. Bölöni, and D. Turgut. Providing Distribution Estimation for Animal Tracking with Unmanned Aerial Vehicles. In to be presented at IEEE GLOBECOM'18, December 2018.

(unavailable)

### Abstract:

This paper focuses on the application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV) for animal tracking problem. The goal of this application is to monitor the target animals in large wild areas without any attachment devices. The WSN includes clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a model predictive control (MPC) method that is used to guide the UAV in planning its path. We first build a prediction model to learn the animal appearance patterns from the sensed historical data. Then, based on the real-time predicted animal distributions, we introduce a path planning approach for the UAV that reduces message delay by maximizing the collected rewards. The experimental results show that our approach outperforms the greedy and traveling salesmen problem-based path planning heuristics in terms of collected \em value of information. We also discuss the results of other performance metrics involving message delay and percentage of events collected.

### BibTeX:

@inproceedings{Xu-2018-GLOBECOM,
author = "J. Xu and G. Solmaz and R. Rahmatizadeh and L. B{\"o}l{\"o}ni and D. Turgut",
title = "Providing Distribution Estimation for Animal Tracking with Unmanned Aerial Vehicles",
booktitle = "to be presented at IEEE GLOBECOM'18",
month = "December",
year = "2018",
abstract = {
This paper focuses on the application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV) for animal tracking problem. The goal of this application is to monitor the target animals in large wild areas without any attachment devices. The WSN includes clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. We propose a model predictive control (MPC) method that is used to guide the UAV in planning its path. We first build a prediction model to learn the animal appearance patterns from the sensed historical data. Then, based on the real-time predicted animal distributions, we introduce a path planning approach for the UAV that reduces message delay by maximizing the collected rewards. The experimental results show that our approach outperforms the greedy and traveling salesmen problem-based path planning heuristics in terms of collected {\em value of information}. We also discuss the results of other performance metrics involving message delay and percentage of events collected.
}
}


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