Journal of Intelligent & Robotic Systems

, Volume 74, Issue 1–2, pp 437–453 | Cite as

A Framework for Using Unmanned Aerial Vehicles for Data Collection in Linear Wireless Sensor Networks

  • Imad JawharEmail author
  • Nader Mohamed
  • Jameela Al-Jaroodi
  • Sheng Zhang


The wireless sensor network (WSN) technology have been evolving very quickly in recent years. Sensors are constantly increasing in sensing, processing, storage, and communication capabilities. In many WSNs that are used in environmental, commercial and military applications, the sensors are lined linearly due to the linear nature of the structure or area that is being monitored making a special class of these networks; We defined these in a previous paper as Linear Sensor Networks (LSNs), and provided a classification of the different types of LSNs. A pure multihop approach to route the data all the way along the linear network (e.g. oil, gas and water pipeline monitoring, border monitoring, road-side monitoring, etc.), which can extend for hundreds or even thousands of kilometers can be very costly from an energy dissipation point of view. In order to significantly reduce the energy consumption used in data transmission and extend the network lifetime, we present a framework for monitoring linear infrastructures using LSNs where data collection and transmission is done using Unmanned Aerial Vehicles (UAVs). The system defines four types of nodes, which include: sensor nodes (SNs), relay nodes (RNs), UAVs, and sinks. The SNs use a classic WSN multihop routing approach to transmit their data to the nearest RN, which acts as a cluster head for its surrounding SNs. Then, a UAV moves back and forth along the linear network and transport the data that is collected by the RNs to the sinks located at both ends of the LSN. We name this network architecture a UAV-based LSNs (ULSNs). This approach leads to considerable savings in node energy consumption, due to a significant reduction of the transmission ranges of the SN and RN nodes and the use of a one-hop transmission to communicate the data from the RNs to the UAV. Furthermore, the strategy provides for reduced interference between the RNs that can be caused by hidden terminal and collision problems, that would be expected if a pure multihop approach is used at the RN level. In addition, three different UAV movement approaches are presented, simulated, and analyzed in order to measure system performance under various network conditions.


Wireless sensor networks (WSNs) Mobile ad hoc networks (MANETs) Routing Unmanned aerial vehicle (UAV) Ferry Monitoring Delay-tolerant networks (DTNs) 


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  1. 1.
    Jawhar, I., Mohamed, N., Agrawal, D.P.: Linear wireless sensor networks: Classification and applications. Elsevier J. Netw. Comput. Appl. (JNCA) 34, 1671–1682 (2011)CrossRefGoogle Scholar
  2. 2.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S.R., Raman, V., Reiss, F., Shah, M.A.: TelegraphCQ: Continuous dataflow processing for an uncertain world. In: CIDR (2003)Google Scholar
  3. 3.
    Cherniack, M., Franklin, M., Zdonik S.: Expressing user profiles for data recharging. IEEE Pers. Commun. 8, 32–38 (2001)CrossRefGoogle Scholar
  4. 4.
    Clark, D., Tennenhouse, D.: Architectural considerations for a new generation of protocols. In: ACM SIGCOMM, pp. 200–208 (1990)Google Scholar
  5. 5.
    Dikaiakos, M.D., Iqbal, S., Nadeem, T., Iftode, L.: VITP: an information transfer protocol for vehicular computing. In: Workshop on Vehicular Ad Hoc Networks, pp. 30–39 (2005)Google Scholar
  6. 6.
    Jawhar, I., Mohamed, N., Shuaib, K., Kesserwan, N.: An efficient framework and networking protocol for linear wireless sensor networks. Accepted for publication in The Ad Hoc and Sensor Wireless Networks Journal, Old City Publishing, London, UK (2008)Google Scholar
  7. 7.
    Jawhar, I., Mohamed, N., Zhang, L.: A distributed topology discovery algorithm for linear sensor networks. In: Proceedings of IEEE International Conference on Communications in China. IEEE, Beijing, China (2012)Google Scholar
  8. 8.
    Zimmerling, M., Dargie, W., Reason, J.M.: Energy-efficient routing in linear wireless sensor networks. In: IEEE International Conference on Mobile Adhoc and Sensor Systems (MASS) (2007)Google Scholar
  9. 9.
    Zimmerling, M., Dargie, W., Reason, J.M.: Localized power-aware routing in linear wireless sensor networks. In: Proceedings of the 2nd ACM international conference on Context-awareness for self-managing systems, ACM (2008)Google Scholar
  10. 10.
    Noori, M., Ardakani, M.: Characterizing the traffic distribution in linear wireless sensor networks. IEEE Commun. Lett. 12(8), 554–556 (2008)CrossRefGoogle Scholar
  11. 11.
    Liu, A.F., et al.: An energy-balanced data gathering algorithm for linear wireless sensor networks. Int. J. Wireless Inf. Networks 17(1), 42–53 (2010)CrossRefGoogle Scholar
  12. 12.
    Li, H., Shunjie, X.: Energy-efficient node placement in linear wireless sensor networks. Int. Conf. Meas. Technol. Mechatron. Autom. (ICMTMA), IEEE, 2, 104–107 (2010)Google Scholar
  13. 13.
    Martin, K., Paterson, M.: Ultra-lightweight key predistribution in wireless sensor networks for monitoring linear infrastructure. Information Security Theory and Practice. Smart Devices, Pervasive Systems, and Ubiquitous Networks, pp. 143–152 (2009)Google Scholar
  14. 14.
    Dorling, K., Messier, G.G., Magierowski, S., Valentin, S.: Energy-efficient communication protocols for wireless microsensor networks. IEEE ICC 2012—Ad-hoc and Sensor Networking Symposium (2012)Google Scholar
  15. 15.
    de Freitas, E.P., et al.: Uav relay network to support wsn connectivity. In: 2010 International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 309–314 (2010)Google Scholar
  16. 16.
    Oliveira, H.A., Barreto, A.L., Fontao, R.S., Loureiro, A.A., Nakamura, E.F.: A novel greedy forward algorithm for routing data toward a high speed sink in wireless sensor networks. In: Proceedings of 19th International Conference on Computer Communications and Networks (ICCCN), pp. 1–7. IEEE (2010)Google Scholar
  17. 17.
    Giorgetti, A., Lucchi, M., Chiani, M., Win, M.Z.: Energy-efficient communication protocols for wireless microsensor networks. IEEE Trans. Aerosp. Electron. Syst. 47(4), 2610–2626 (2011)CrossRefGoogle Scholar
  18. 18.
    Ho, T.D., Park, J., Shimamoto, S.: Novel multiple access scheme for wireless sensor network employing unmanned aerial vehicle. In: IEEE/AIAA 29th Digital Avionics Systems Conference (DASC). IEEE (2010)Google Scholar
  19. 19.
    Ho, D.T., Park, J., Shimamoto, S.: Performance evaluation of the pfsc based mac protocol for wsn employing uav in rician fading. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 55–60. IEEE (2011)Google Scholar
  20. 20.
    Ho, D.T., Shimamoto, S.: Highly reliable communication protocol for wsn-uav system employing tdma and pfs scheme. In: 2011 IEEE GLOBECOM Workshops (GC Wkshps), pp. 1320–1324. IEEE (2011)Google Scholar
  21. 21.
    Shah, R.C., Roy, S., Jain, S., Brunette, W.: Data MULEs: modeling a three-tier architecture for sparse sensor networks. In: IEEE SNPA (2003)Google Scholar
  22. 22.
    Jea, D., Somasundara, A.A., Srivastava, M.B.: Multiple controlled mobile elements (data mules) for data collection in sensor networks. In: Proceedings IEEE/ ACM Int. Conf. Distrib. Comp., in Sensor Sys. (2005)Google Scholar
  23. 23.
    Zhao, W., Ammar, M.: Message ferrying: Proactive routing in highly-partitioned wireless ad hoc networks. In: Proceedings IEEE Workshop on Future Trends in Distributed Computing Systems (2003)Google Scholar
  24. 24.
    Zhao, W., Ammar, M., Zegura, E.: A message ferrying approach for data delivery in sparse mobile ad hoc networks. In: Proc. ACM Mobihoc (2004)Google Scholar
  25. 25.
    Zhao, W., Ammar, M., Zegura, E.: Controlling the mobility of multiple data transport ferries in a delay-tolerant network. In: IEEE INFOCOM (2005)Google Scholar
  26. 26.
    Tariq, M.B., Ammar, M., Zegura, E.: Message Ferry Route Design for Sparse Ad Hoc Networks with Mobile Nodes. MobiHoc (2006)Google Scholar
  27. 27.
    Mohamed, N., Jawhar, I., Al-Jaroodi, J., Zhang, L.: Monitoring underwater pipelines using sensor networks. In: The 12th IEEE International Conference on High Performance Computing and Communications (HPCC-2010), pp. 346–353. Melbourne, Australia (2010)Google Scholar
  28. 28.
    Mohamed, N., Jawhar, I., Al-Jaroodi, J., Zhang, L.: Sensor network architectures for monitoring underwater pipelines. In: Sensors—The Special Issue on Underwater Sensor Nodes and Underwater Sensor Networks, vol. 11, no. 11, pp. 10738–10764 (2011)Google Scholar
  29. 29.
    Solberg, L., Gjertveit, S.E.: Constructing the worlds longest subsea pipeline, langeled gas export. In: Proceedings of the Offshore Technology Conference. Houston, TX, USA (2007)Google Scholar
  30. 30.
    Manum, H., Schmid, M.: Monitoring in a harsh environment. Control Autom. 18, 22–27 (2008)Google Scholar
  31. 31.
    Hartong, M., Goel, R., Wijesekera, D.: Security and the us rail infrastructure. Int. J. Crit. Infrastruct. Prot. 1, 15–28 (2008)CrossRefGoogle Scholar
  32. 32.
    Akkaya, K., Younis, M.: A survey of routing protocols in wireless sensor networks. Elsevier Ad Hoc Netw. 3(3), 325–349 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Imad Jawhar
    • 1
    Email author
  • Nader Mohamed
    • 1
  • Jameela Al-Jaroodi
    • 2
  • Sheng Zhang
    • 3
  1. 1.College of Information TechnologyUAE UniversityAlainUAE
  2. 2.Middleware Technologies LaboratoryManamaBahrain
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China

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