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Unmanned Aerial Vehicle-Aided Wireless Sensor Network Deployment System for Post-disaster Monitoring

  • Gurkan Tuna
  • Tarik Veli Mumcu
  • Kayhan Gulez
  • Vehbi Cagri Gungor
  • Hayrettin Erturk
Part of the Communications in Computer and Information Science book series (CCIS, volume 304)

Abstract

This paper presents design strategies of using unmanned aerial vehicles (UAVs) to deploy wireless sensor networks (WSNs) for post-disaster monitoring. Natural disasters are unforeseeable events which cannot be prevented. But some recovery procedures can be followed to minimize their effects. Post-disaster monitoring is important to estimate the effects of disasters, which in turn is used to determine recovery procedures to be followed. We propose an UAV-aided unattended WSN deployment system. The system is a post-disaster solution which can be used anywhere required. In this study, we mainly evaluate the efficiency of localization and navigation performance of the proposed system. Our simulation studies with an AirRobot quadrotor helicopter in Unified System for Automation and Robot Simulation (USARSim) simulation platform show that UAVs can be used to deploy WSNs after disasters to monitor environmental conditions. Future work includes implementing the system using a hexarotor helicopter.

Keywords

wireless sensor networks unmanned aerial vehicles localization and navigation systems Global Positioning System inertial navigation system Kalman filter 

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References

  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless Sensor Networks: a Survey. Computer Networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Gungor, V.C., Hancke, G.P.: Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches. IEEE Transactions on Industrial Electronics 56(10), 4258–4265 (2009)CrossRefGoogle Scholar
  3. 3.
    Ollero, A., Bernard, M., Civita, M.L., Hoesel, L.V., Marron, P.J., Lepley, J., Andres, E.D.: AWARE: Platform for Autonomous Self-deploying and Operation of Wireless Sensor-actuator Networks Cooperating with Unmanned Aerial Vehicles. In: Proceedings of the 2007 IEEE International Workshop on Safety, Security and Rescue Robotics, pp. 1–6 (2007)Google Scholar
  4. 4.
    Corke, P., Hrabar, S., Peterson, R., Rus, D., Saripalli, S., Sukhatme, G.: Autonomous Deployment and Repair of a Sensor Network using an Unmanned Aerial Vehicle. In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation (ICRA), pp. 3603–3608 (2004)Google Scholar
  5. 5.
    Wang, Y., Wu, C.H.: Robot-Assisted Sensor Network Deployment and Data Collection. In: Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 467–472 (2007)Google Scholar
  6. 6.
    Younis, M., Akkaya, K.: Strategies and Techniques for Node Placement in Wireless Sensor Networks: A Survey. Ad Hoc Networks 6(4), 621–655 (2008)CrossRefGoogle Scholar
  7. 7.
    Suzuki, T., Kawabata, K., Hada, Y., Tobe, Y.: Deployment of Wireless Sensor Network Using Mobile Robots to Construct an Intelligent Environment in a Multi-Robot Sensor Network. In: Advances in Service Robotics, pp. 315–328 (2008)Google Scholar
  8. 8.
    Suzuki, T., Sugizaki, R., Kawabata, K., Hada, Y., Tobe, Y.: Autonomous Deployment and Restoration of Sensor Network using Mobile Robots. International Journal of Advanced Robotic Systems 7(2), 105–114 (2010)Google Scholar
  9. 9.
    Guivant, J.E., Masson, F.R., Nebot, E.M.: Simultaneous Localization and Map Building Using Natural Features and Absolute Information. Robotics and Autonomous Systems 40(2-3), 79–90 (2002)CrossRefGoogle Scholar
  10. 10.
    Huang, J., Tan, H.-S.: A Low-Order DGPS-Based Vehicle Positioning System Under Urban Environment. IEEE Transactions on Mechatronics 11(5), 567–575 (2006)CrossRefGoogle Scholar
  11. 11.
    Sukkarieh, S.: Low Cost, High Integrity, Aided Inertial Navigation Systems for Autonomous Land Vehicles. Ph.D. Thesis, University of Sydney (2000)Google Scholar
  12. 12.
    Giremus, A., Doucet, A., Calmettes, V., Tourneret, J.-Y.: A Rao-Blackwellized Particle Filter for INS/GPS Integration. In: Proceedings of the 2004 IEEE International Conference on Acoustics Speech and Signal Processing, pp. 964–967 (2004)Google Scholar
  13. 13.
    Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A Solution to the Simultaneous Localization and Map Building (SLAM) Problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)CrossRefGoogle Scholar
  14. 14.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)zbMATHGoogle Scholar
  15. 15.
    Carpin, S., Lewis, M., Wang, J., Balakirsky, S., Scrapper, C.: USARSim: a Robot Simulator for Research and Education. In: Proceedings of the 2007 IEEE Conference on Robotics and Automation, Roma, pp. 1400–1405 (2007)Google Scholar
  16. 16.
  17. 17.
    AirRobot (2011), http://www.airrobot.de/
  18. 18.
  19. 19.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gurkan Tuna
    • 1
  • Tarik Veli Mumcu
    • 2
  • Kayhan Gulez
    • 2
  • Vehbi Cagri Gungor
    • 3
  • Hayrettin Erturk
  1. 1.Department of Computer ProgrammingTrakya UniversityEdirneTurkey
  2. 2.Electrical-Electronics Faculty, Control and Automation Eng. Dept.Yildiz Technical UniversityIstanbulTurkey
  3. 3.Faculty of Engineering, Department of Computer EngineeringBahcesehir UniversityIstanbulTurkey

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