J. Xu, G. Solmaz, R. Rahmatizadeh, D. Turgut, and L. Bölöni

Animal Monitoring with Unmanned Aerial Vehicle-Aided Wireless Sensor Networks


Cite as:

J. Xu, G. Solmaz, R. Rahmatizadeh, D. Turgut, and L. Bölöni. Animal Monitoring with Unmanned Aerial Vehicle-Aided Wireless Sensor Networks. In Proc. of the 40th IEEE Conf. on Local Computer Networks (LCN-2015), pp. 334–341, October 2015.

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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.

BibTeX:

@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 = "Proc. of the 40th IEEE Conf. on Local Computer Networks (LCN-2015)",
pages = "334-341",
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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