Providing Distribution Estimation For Animal Tracking With Unmanned Aerial Vehicles

Keywords

animal monitoring; distribution prediction.; path planning; UAV; unmanned aerial vehicle

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 value of information. We also discuss the results of other performance metrics involving message delay and percentage of events collected.

Publication Date

1-1-2018

Publication Title

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/GLOCOM.2018.8647784

Socpus ID

85063459011 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85063459011

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