Wireless Networks

, Volume 25, Issue 4, pp 1641–1655 | Cite as

Enhanced mobility aware routing protocol for Low Power and Lossy Networks

  • Shridhar SanshiEmail author
  • C. D. Jaidhar


Due to the technological advancement in Low Power and Lossy Networks (LLNs), sensor node mobility becomes a basic requirement for many extensive applications. Routing protocol designed for LLNs must ensure real-time data transmission with minimum power consumption. However, the existing mobility support protocols cannot work efficiently in LLNs as they are unable to adapt to the change in the network topology quickly. Therefore, we propose an Enhanced Routing Protocol for LLNs (ERPL), which updates the Preferred Parent (PP) of the Mobile Node (MN) quickly whenever the MN moves away from the already selected PP. Further, a new objective function that takes the mobility of the node into an account while selecting a PP is proposed. Performance of the ERPL has been evaluated with the varying system and traffic parameters under different topologies similar to most of the real-life networks. The simulation results showed that the proposed ERPL reduced the power consumption, packet overhead, latency and increased the packet delivery ratio as compared to other existing works.


Low Power and Lossy Networks Distance Objective function Preferred Parent Routing protocol 


  1. 1.
    Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805.CrossRefzbMATHGoogle Scholar
  2. 2.
    urii, M. P., Tafa, Z., Dimi, G., & Milutinovi, V. (2012). A survey of military applications of wireless sensor networks. In 2012 Mediterranean conference on embedded computing (MECO), pp 196–199.Google Scholar
  3. 3.
    Othman, M. F., & Shazali, K. (2012). Wireless sensor network applications: A study in environment monitoring system. Procedia engineering. In International symposium on robotics and intelligent sensors, vol. 41, pp. 1204–1210. (IRIS 2012).Google Scholar
  4. 4.
    Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710.CrossRefGoogle Scholar
  5. 5.
    Rehan, W., Fischer, S., Rehan, M., & Rehmani, M. H. (2017). A comprehensive survey on multichannel routing in wireless sensor networks. Journal of Network and Computer Applications, 95, 1–25.CrossRefGoogle Scholar
  6. 6.
    Winter, T. (2012). Rpl: Ipv6 routing protocol for low-power and lossy networks.Google Scholar
  7. 7.
    Ko, J., Terzis, A., Dawson-Haggerty, S., Culler, D. E., Hui, J. W., & Levis, P. (2011). Connecting low-power and lossy networks to the internet. IEEE Communications Magazine, 49(4), 96–101.CrossRefGoogle Scholar
  8. 8.
    Gnawali, O. (2012). The minimum rank with hysteresis objective function.Google Scholar
  9. 9.
    Carels, D., Poorter, E. D., Moerman, I., & Demeester, P. (2015). Rpl mobility support for point-to-point traffic flows towards mobile nodes. International Journal of Distributed Sensor Networks, 11(6), 470349.CrossRefGoogle Scholar
  10. 10.
    Korbi, I. E., Brahim, M. B., Adjih, C., & Saidane, L. A. (2012). Mobility enhanced rpl for wireless sensor networks. In 2012 Third international conference on the network of the future (NOF), pp. 1–8.Google Scholar
  11. 11.
    Cobrzan, C., Montavont, J., & Nol, T. (2014). Analysis and performance evaluation of rpl under mobility. In 2014 IEEE symposium on computers and communications (ISCC), pp. 1–6.Google Scholar
  12. 12.
    Gara, F., Saad, L. B., Hamida, E. B., Tourancheau, B., & Ayed, R. B. (2016). An adaptive timer for rpl to handle mobility in wireless sensor networks. In 2016 International wireless communications and mobile computing conference (IWCMC), pp. 678–683.Google Scholar
  13. 13.
    Park, J., Kim, K. H., & Kim, K. (2017). An algorithm for timely transmission of solicitation messages in rpl for energy-efficient node mobility. Sensors, 17(4), 899.CrossRefGoogle Scholar
  14. 14.
    Levis, P., Patel, N., Culler, D., & Shenker, S. (2004). Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks. In Proceedings of the 1st conference on symposium on networked systems design and implementation—volume 1, USENIX Association, Berkeley, CA, USA, NSDI’04, pp. 2–2.Google Scholar
  15. 15.
    Khan, A. A., Rehmani, M. H., & Reisslein, M. (2017). Requirements, design challenges, and review of routing and mac protocols for cr-based smart grid systems. IEEE Communications Magazine, 55(5), 206–215.CrossRefGoogle Scholar
  16. 16.
    Ko, J., & Chang, M. (2015). Momoro: Providing mobility support for low-power wireless applications. IEEE Systems Journal, 9(2), 585–594.CrossRefGoogle Scholar
  17. 17.
    Lee, K. C., Sudhaakar, R., Dai, L., Addepalli, S., & Gerla, M. (2012). Rpl under mobility. In 2012 IEEE consumer communications and networking conference (CCNC), pp. 300–304Google Scholar
  18. 18.
    Cobârzan, C., Montavont, J., & Noel, T. (2015). Integrating mobility in rpl. In European conference on wireless sensor networks, Springer, pp. 135–150.Google Scholar
  19. 19.
    Kuntz, R., Montavont, J., & Noël, T. (2013). Improving the medium access in highly mobile wireless sensor networks. Telecommunication Systems, 52(4), 2437–2458.CrossRefGoogle Scholar
  20. 20.
    Barcelo, M., Correa, A., Vicario, J. L., Morell, A., & Vilajosana, X. (2016). Addressing mobility in rpl with position assisted metrics. IEEE Sensors Journal, 16(7), 2151–2161.CrossRefGoogle Scholar
  21. 21.
    Gaddour, O., Kouba, A., Rangarajan, R., Cheikhrouhou, O., Tovar, E., & Abid, M. (2014). Co-rpl: Rpl routing for mobile low power wireless sensor networks using corona mechanism. In Proceedings of the 9th IEEE international symposium on industrial embedded systems (SIES 2014), pp. 200–209.Google Scholar
  22. 22.
    Fotouhi, H., Moreira, D., & Alves, M. (2014). Mobile iot: smart-hop over rpl. CISTER-Research Centre in Realtime and Embedded Computing Systems: Tech. rep.Google Scholar
  23. 23.
    Saleem, Y., Yau, K. L. A., Mohamad, H., Ramli, N., Rehmani, M. H., & Ni, Q. (2017). Clustering and reinforcement-learning-based routing for cognitive radio networks. IEEE Wireless Communications, 24(4), 146–151.CrossRefGoogle Scholar
  24. 24.
    Draves, R., Padhye, J., & Zill, B. (2004). Routing in multi-radio, multi-hop wireless mesh networks. In Proceedings of the 10th annual international conference on mobile computing and networking, ACM, New York, NY, USA, MobiCom ’04, pp. 114–128.Google Scholar
  25. 25.
    Dunkels, A., Osterlind, F., Tsiftes, N., & He, Z. (2007). Software-based on-line energy estimation for sensor nodes. In Proceedings of the 4th workshop on embedded networked sensors, ACM, New York, NY, USA, EmNets ’07, pp. 28–32.Google Scholar
  26. 26.
    Sky, T. (2006). Ultra low power ieee 802.15. 4 compliant wireless sensor module. Moteiv CorporationGoogle Scholar
  27. 27.
    Amiri, M. (2010). Measurements of energy consumption and execution time of different operations on tmote sky sensor nodes. Ph.D. thesis, Masarykova univerzita, Fakulta informatiky.Google Scholar
  28. 28.
    Instruments, T. (2007). Cc2420 datasheet. Reference SWRS041B.Google Scholar
  29. 29.
    Kumar, P., Reddy, L., & Varma, S. (2009). Distance measurement and error estimation scheme for rssi based localization in wireless sensor networks. In 2009 Fifth international conference on wireless communication and sensor networks (WCSN), pp. 1–4.Google Scholar
  30. 30.
    Jevtić, M., Zogović, N., & Dimić, G. (2009). Evaluation of wireless sensor network simulators. In Proceedings of the 17th telecommunications forum (TELFOR 2009), Belgrade, Serbia, pp. 1303–1306.Google Scholar
  31. 31.
    Dunkels, A. (2012). Contiki operating system
  32. 32.
    Aschenbruck, N., Ernst, R., Gerhards-Padilla, E., & Schwamborn, M. (2010). Bonnmotion: A mobility scenario generation and analysis tool. In Proceedings of the 3rd international ICST conference on simulation tools and techniques, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), p. 51.Google Scholar
  33. 33.
    Dunkels, A., Eriksson, J., Finne, N., & Tsiftes, N. (2011). Powertrace: Network-level power profiling for low-power wireless networks.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Information TechnologyNational Institute of Technology KarnatakaSurathkalIndia

Personalised recommendations