Adaptive Data Collection in Sparse Underwater Sensor Networks Using Mobile Elements

  • M. J. JalajaEmail author
  • Lillykutty Jacob
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8629)


Underwater Wireless Sensor Network (UWSN) is a group of sensors and underwater vehicles, networked via acoustic links to perform collaborative tasks. Due to hostile environment, resource constraints and the peculiarities of the underlying physical layer technology, UWSNs tend to be sparse or partitioned, and energy-efficient data collection in a sparse UWSN is a challenging problem. We consider mobility-assisted routing as a technique for enabling connectivity and improving the energy efficiency of sparse UWSN, considering it as a Delay/Disruption Tolerant Network (DTN) or Intermittently Connected Network (ICN). The DTN framework shows superior performance in terms of energy efficiency and packet delivery ratio, at the cost of increased message latency. We investigate the effectiveness of a polling model to analyze the delay performance and propose a dynamic optimization technique to minimize latency adaptively, thereby supporting delay-sensitive applications also. The effectiveness of the proposed technique in modelling the dynamically changing environment and minimizing the data collection latency is validated using NS-2 based simulation.


Underwater Wireless Sensor Network Delay Tolerant Network Mobile sink Polling Dynamic optimization Scheduling Preference Index 


  1. 1.
    Akyildiz, I., Pompili, D., Melodia, T.: Underwater acoustic sensor networks: research challenges. Ad Hoc Netw. 3, 257–279 (2005)CrossRefGoogle Scholar
  2. 2.
    Garcia, M., Sendra, S., Atenas, M., Lloret, J.: Underwater wireless ad-hoc networks: a survey. In: Mobile Ad hoc Networks: Current Status and Future Trends. CRC Press/Taylor and Francis, Boca Raton (2011)Google Scholar
  3. 3.
    Lloret, J., Sendra, S., Ardid, M., Rodrigues, J.J.P.C.: Underwater wireless sensor communications in the 2.4 GHz ISM frequency band. Sensors 12(4), 4237–4264 (2012)CrossRefGoogle Scholar
  4. 4.
    Zhang, Z.: Routing in intermittently connected mobile ad hoc networks and delay tolerant networks: overview and challenges. IEEE Commun. Surv. Tuts. 8(1), 24–37 (2006)CrossRefGoogle Scholar
  5. 5.
    Xie, P., Zhou, Z., Peng, Z., Yan, H.: Aqua-sim: an NS-2 based simulator for underwater sensor networks, Underwater Sensor Networks Lab, University of Connecticut, OCEANS (2009)Google Scholar
  6. 6.
    ns-2 Network Simulator.
  7. 7.
    Jain, S., Fall, K., Patra, R.: Routing in a delay tolerant network. In: Proceedings of ACM SIGCOMM 2004, pp. 145–158 (2004)Google Scholar
  8. 8.
    Guo, Z., Colombi, G., Wang, B., Cui, J.H., Maggiorini, D., Rossi, G.P.: Adaptive routing in underwater delay/disruption tolerant sensor networks. In: Proceedings of the Fifth Annual Conference on Wireless on Demand Network Systems and Services, WONS (2008)Google Scholar
  9. 9.
    Jain, S., Shah, R., Brunnette, W., Borriello, G., Roy, S.: Exploiting mobility for energy efficient data collection in wireless sensor networks. Mob. Netw. Appl. 11, 327–339 (2006)CrossRefGoogle Scholar
  10. 10.
    Zorzi, M., Casari, P., Baldo, N., Harris III, A.F.: Energy-efficient routing schemes for underwater acoustic networks. IEEE J. Sel. Areas Commun. 26(9), 1754–1766 (2008)CrossRefGoogle Scholar
  11. 11.
    He, L., Pan, J., Zhuang, Y., et al.: Evaluating on-demand data collection with mobile elements in wireless sensor networks. In: Proceedings of the IEEE VTC (2010)Google Scholar
  12. 12.
    Somasundara, A.A., Kansal, A., Jea, D.D., Estrin, D., Srivastava, M.B.: Controllably mobile infrastructure for low energy embedded networks. IEEE Trans. Mob. Comput. 5(8), 1–16 (2006)CrossRefGoogle Scholar
  13. 13.
    Yoon, S., Azad, A.K., Kim, S.: AURP: an AUV-aided underwater routing protocol for underwater acoustic sensor networks. Sensors 12, 1827–1845 (2012)CrossRefGoogle Scholar
  14. 14.
    Hollinger, G.A., Choudhary, S., Qarabaqi, P., Mitra, U., Sukhatme, G.S., Stojanovic, M., Singh, H., Hover, F.: Underwater data collection using robotic sensor networks. IEEE J. Sel. Areas Commun. 30(5), 899–911 (2012)CrossRefGoogle Scholar
  15. 15.
    Kavitha, V., Altman, E.: Queueing in space : design of message ferry routes in static adhoc networks. In: Proceedings of the 21st International Teletraffic Congress (ITC 21), France, Paris (2009)Google Scholar
  16. 16.
    Takagi, H.: Queueing Analysis of Polling Models: An Update. Stochastic Analysis of Computer and Communication Systems, pp. 267–318. Elsevier Science Publishers B.V., Amsterdam/North Holland (1990)Google Scholar
  17. 17.
    Yechiali, U.: Optimal dynamic control of polling systems. In: Cohen, J.R., Pack, C.D. (eds.) Queueing, Performance and Control in ATM, vol. Elsevier Science Publishers B.V., pp. 205–217. North Holland, North Holland (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringNational Institute of Technology CalicutKozhikodeIndia

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