STEEP: speed and time-based energy efficient neighbor discovery in opportunistic networks

  • Abhishek Thakur
  • R. Sathiyanarayanan
  • Chittaranjan Hota


Neighbor discovery in sparse opportunistic networks can require significant energy. Generally, discovery occurs by sending and waiting for probe messages and responses respectively from nearby nodes. Algorithms dynamically vary intervals between probes to conserve power. Based on analysis of the recent discovery approaches, we propose an adaptive discovery algorithm “speed and time based energy efficient probing (STEEP)”, which uses details of latest ‘connection up’ event and node speed. It studies the impact on discovery when nodes turn off the radio interface to conserve power, which may typically cause higher discovery failures. Extensive experiments conducted using real-world traces and working day model show that STEEP provides 30–50% power savings for discovery in delay tolerant networks (DTN). It also demonstrates good results for DTN routing as well as better adaptation to density changes.


Delay tolerant network Energy efficiency Neighbor discovery Wireless communication 


  1. 1.
    Fall, K. (2003). A delay-tolerant network architecture for challenged internets. In Proceedings of the 2003 conference on applications, technologies, architectures, and protocols for computer communications. ACM, 2003.Google Scholar
  2. 2.
    Cao, Y., & Sun, Z. (2013). Routing in delay/disruption tolerant networks: A taxonomy, survey and challenges. IEEE Communications Surveys & Tutorials, 15(2), 654–677.CrossRefGoogle Scholar
  3. 3.
    Zhang, H., et al. (2017). Energy efficient user association and power allocation in millimeter-wave-based ultra dense networks with energy harvesting base stations. IEEE Journal on Selected Areas in Communications, 35(9), 1936–1947.CrossRefGoogle Scholar
  4. 4.
    Pozza, R., et al. (2015). Neighbor discovery for opportunistic networking in internet of things scenarios: A survey. IEEE Access, 3, 1101–1131.CrossRefGoogle Scholar
  5. 5.
    Choi, B. J., & Shen, X. (2011). Adaptive asynchronous sleep scheduling protocols for delay tolerant networks. IEEE Transactions on Mobile Computing, 10(9), 1283–1296.CrossRefGoogle Scholar
  6. 6.
    Shih, E., Bahl, P., & Sinclair, M. J. (2002). Wake on wireless: An event driven energy saving strategy for battery operated devices. In Proceedings of the 8th annual international conference on Mobile computing and networking. ACM.Google Scholar
  7. 7.
    Wang, W., Motani, M., & Srinivasan, V. (2009). Opportunistic energy-efficient contact probing in delay-tolerant applications. IEEE/ACM Transactions on Networking (TON), 17(5), 1592–1605.CrossRefGoogle Scholar
  8. 8.
    Orlinski, M., & Filer, N. (2012). Movement speed based inter-probe times for neighbor discovery in mobile ad-hoc networks. In International conference on ad hoc networks. Berlin: Springer.Google Scholar
  9. 9.
    Orlinski, M., & Filer, N. (2015). Neighbor discovery in opportunistic networks. Ad Hoc Networks, 25, 383–392.CrossRefGoogle Scholar
  10. 10.
    Han, B., Li, J., & Srinivasan, A. (2015). On the energy efficiency of device discovery in mobile opportunistic networks: A systematic approach. IEEE Transactions on Mobile Computing, 14(4), 786–799.CrossRefGoogle Scholar
  11. 11.
    Izumikawa, H., et al. Energy-efficient adaptive interface activation for delay/disruption tolerant networks. In 2010 The 12th international conference on advanced communication technology (ICACT) (Vol. 1). IEEE, 2010.Google Scholar
  12. 12.
    Feng, Y., et al. (2015). A sleep scheduling mechanism based on power law distribution for mobile delay tolerate networks. In 2015 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC). IEEE, 2015.Google Scholar
  13. 13.
    Feeney, L. M., & Nilsson, M. (2001). Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In INFOCOM 2001. Twentieth annual joint conference of the IEEE computer and communications societies. Proceedings. IEEE (Vol. 3). IEEE, 2001.Google Scholar
  14. 14.
    Zhang, H., et al. (2017). Sensing time optimization and power control for energy efficient cognitive small cell with imperfect hybrid spectrum sensing. IEEE Transactions on Wireless Communications, 16(2), 730–743.CrossRefGoogle Scholar
  15. 15.
    Kamath, S., Lindh, J. (2012). Measuring bluetooth low energy power consumption. Available: Accessed February 4, 2018.
  16. 16. (2015). CC3200 power management optimizations and measurements. Available: Accessed February 4, 2018.
  17. 17.
    Yang, D., et al. (2015). OPEED: Optimal energy-efficient neighbor discovery scheme in opportunistic networks. Journal of Communications and Networks, 17(1), 34–39.CrossRefGoogle Scholar
  18. 18.
    Keränen, A., Ott, J., & Kärkkäinen, T. (2009). The ONE simulator for DTN protocol evaluation.In Proceedings of the 2nd international conference on simulation tools and techniques. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering).Google Scholar
  19. 19.
    Ekman, F., et al. (2008). Working day movement model. In Proceedings of the 1st ACM SIGMOBILE workshop on Mobility models. ACM.Google Scholar
  20. 20.
    Piorkowski, M., Sarafijanovic–Djukic, N., Grossglauser, M. (2009). CRAWDAD dataset epfl/mobility (v. 2009-02-24), downloaded from,
  21. 21.
    Moreira, W., & Mendes, P. (2015). Impact of human behavior on social opportunistic forwarding. Ad Hoc Networks, 25, 293–302.CrossRefGoogle Scholar
  22. 22.
    Cheng, L., et al. (2014). QoS aware geographic opportunistic routing in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(7), 1864–1875.CrossRefGoogle Scholar
  23. 23.
    Liu, Y., et al. (2015). Performance and energy consumption analysis of a delay-tolerant network for censorship-resistant communication. In Proceedings of the 16th ACM international symposium on mobile ad hoc networking and computing. ACM.Google Scholar
  24. 24.
    Spyropoulos, T., Psounis, K., & Raghavendra, C. S. (2005). Spray and wait: An efficient routing scheme for intermittently connected mobile networks. In Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking. ACM.Google Scholar
  25. 25.
    Grossglauser, M., & Tse, D. (2001). Mobility increases the capacity of ad-hoc wireless networks. In INFOCOM 2001. Twentieth annual joint conference of the IEEE computer and communications societies. Proceedings. IEEE (Vol. 3).Google Scholar
  26. 26.
    Vahdat, A., & Becker, D. (2000). Epidemic routing for partially-connected adhoc networks. Duke University Technical Report Cs-2000-06, Tech.Rep.Google Scholar
  27. 27.
    Batabyal, S., & Bhaumik, P. (2015). Analysing social behaviour and message dissemination in human based delay tolerant network. Wireless Networks, 21(2), 513–529.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.BITS Pilani, Hyderabad CampusHyderabadIndia

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