J. Xu, G. Solmaz, R. Rahmatizadeh, D. Turgut, and L. Bölöni. Animal Monitoring with Unmanned Aerial Vehicle-Aided Wireless Sensor Networks. In Proceedings of IEEE LCN'15, pp. 125–132, October 2015.
In this paper, we focus on an application of wireless sensor networks (WSNs) with unmanned aerial vehicle (UAV). The aim of the application is to detect the locations of endangered species in large-scale wildlife areas or monitor movement of animals without any attachment devices. We first define the mathematical model of 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. We propose a path planning approach based on a Markov decision process (MDP) model that maximizes the VoI while reducing message delays. The success of the approach tested using the real-world movement dataset of zebras. Simulation results show that our approach outperforms greedy and random heuristics as well as the path planning based on the solution of the traveling salesman problem.
@inproceedings{Xu-2015-LCN,
author = "J. Xu and G. Solmaz and R. Rahmatizadeh and D. Turgut and L. B{\"o}l{\"o}ni",
title = "Animal Monitoring with Unmanned Aerial Vehicle-Aided Wireless Sensor Networks",
booktitle = "Proceedings of IEEE LCN'15",
pages = "125-132",
month = "October",
year = "2015",
abstract = {
In this paper, we focus on an application of wireless sensor networks
(WSNs) with unmanned aerial vehicle (UAV). The aim of the application
is to detect the locations of endangered species in large-scale
wildlife areas or monitor movement of animals without any attachment
devices. We first define the mathematical model of 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. We propose a path planning approach based on a Markov
decision process (MDP) model that maximizes the VoI while reducing
message delays. The success of the approach tested using the
real-world movement dataset of zebras. Simulation results show that
our approach outperforms greedy and random heuristics as well as the
path planning based on the solution of the traveling salesman
problem.
},
}
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