Advertisement

Congestion-Aware Opportunistic Routing Protocol in Wireless Sensor Networks

  • Maya ShelkeEmail author
  • Akshay Malhotra
  • Parikshit N. Mahalle
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)

Abstract

It is expected that 50 billion devices in the world will be connected on IOT by 2025. The importance of wireless sensor networks cannot be overstated in this scenario. Network becomes more beneficial to an application when it can be used to its full potential, which is difficult to achieve because of limitations of resources (processor, memory, and energy). There are many existing routing mechanisms which deal with these issues by reducing number of transmissions between sensor nodes by choosing appropriate path toward base station. In this paper, we propose a routing protocol to select the optimized route by using opportunistic theory and by incorporating appropriate sleep scheduling mechanisms into it. This protocol focuses on reduction of congestion in the network and thus increases an individual node’s life, the entire network lifetime, and reduces partitioning in the network.

Keywords

Wireless sensor networks Opportunistic routing protocol Congestion control Sleep scheduling mechanisms 

References

  1. 1.
    Akyildiz, I.F., Weilian, S., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  3. 3.
    Al-Karaki, J.N., Kamal, A.E.: Routing techniques in wireless sensor networks: a survey. IEEE Wirel. Commun. 11(6), 6–28 (2004)CrossRefGoogle Scholar
  4. 4.
    Liu, D., et al.: Duplicate detectable opportunistic forwarding in duty-cycled wireless sensor networks. IEEE/ACM Trans. Netw. 24(2), 662–673 (2016)CrossRefGoogle Scholar
  5. 5.
    Kumar, N., Singh, Y.: An energy efficient opportunistic routing metric for wireless sensor networks. Ind. J. Sci. Technol. 9(32), 1–5 (2016)Google Scholar
  6. 6.
    Mao, X., Tang, S., Xu, X., Li, X.-Y., Ma, H.: Energy-efficient opportunistic routing in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22(11), 1934–1942 (2011)CrossRefGoogle Scholar
  7. 7.
    Singh, D., Tripathi, G., Jara, A.J.: A survey of Internet-of-Things: future vision, architecture, challenges and services. In: 2014 IEEE World Forum on Internet of Things (WF-IoT), pp. 287–292. IEEE, Washington (2014)Google Scholar
  8. 8.
    Baba, S.B., Mohan Rao, K.R.R.: Improving the network life time of a wireless sensor network using the integration of progressive sleep scheduling algorithm with opportunistic routing protocol. Indian J. Sci. Technol. 9(17), 1–6 (2016)Google Scholar
  9. 9.
    Zhang, Z., et al.: Energy-efficient and low-delay scheduling scheme for low power wireless sensor network with real-time data flows. Int. J. Ad Hoc Ubiquitous Comput. 22(3), 174–187 (2016)CrossRefGoogle Scholar
  10. 10.
    Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16(2), 97–166 (2001)CrossRefGoogle Scholar
  11. 11.
    Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. Commun. Surv. Tutor. IEEE 16(1), 414–454 (2014)CrossRefGoogle Scholar
  12. 12.
    Shelke, M., et al.: Fuzzy-based fault-tolerant low-energy adaptive clustering hierarchy routing protocol for wireless sensor network. Int. J. Wirel. Mob. Comput. 11(2), 117–123 (2016)CrossRefGoogle Scholar
  13. 13.
    Yao, Y., Cao, Q., Vasilakos, A.V.: EDAL: an energy-efficient, delay-aware, and lifetime-balancing data collection protocol for heterogeneous wireless sensor networks. IEEE/ACM Trans. Netw. 23(3), 810–823 (2015)CrossRefGoogle Scholar
  14. 14.
    Chachulski, S., et al.: trading structure for randomness in wireless opportunistic routing vol. 37(4). ACM, New York (2007)Google Scholar
  15. 15.
    Liu, H., Zhang, B., Mouftah, H.T., Shen, X., Ma, J.: Opportunistic routing for wireless ad hoc and sensor networks: present and future directions. IEEE Commun. Mag. 47(12), 103–109 (2009)CrossRefGoogle Scholar
  16. 16.
    Biswas, S., Morris, R.: ExOR: opportunistic multi-hop routing for wireless networks. In: ACM SIGCOMM Computer Communication Review vol. 35(4), pp. 133–144. ACM, New York (2005)Google Scholar
  17. 17.
    Zorzi, M., Rao, R.R.: Geographic random forwarding (GeRaF) for ad hoc and sensor networks: energy and latency performance. IEEE Trans. Mob. Comput. 2(4), 349–365 (2003)CrossRefGoogle Scholar
  18. 18.
    Luo, J., Hu, J., Wu, D., Li, R.: Opportunistic routing algorithm for relay node selection in wireless sensor networks. IEEE Trans. Ind. Inform. 11(1), 112–121 (2015)CrossRefGoogle Scholar
  19. 19.
    Jörger, T., Höflinger, F., Gamm, G.U., Reindl, L.M.: Wireless distance estimation with low-power standard components in wireless sensor nodes. arXiv preprint arXiv:1601.07444 (2016)
  20. 20.
    Kaur, J., Grewal, R., Singh Saini, K.: A survey on recent congestion control schemes in wireless sensor network. In: Advance Computing Conference (IACC), 2015 IEEE International, pp. 387–392. IEEE, Washington (2015)Google Scholar
  21. 21.
    Wang, C., Li, B., Sohraby, K., Daneshmand, M., Hu, Y.: Upstream congestion control in wireless sensor networks through cross-layer optimization. IEEE J. Sel. Areas Commun. 25(4), 786–795 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Maya Shelke
    • 1
    Email author
  • Akshay Malhotra
    • 1
  • Parikshit N. Mahalle
    • 2
  1. 1.Symbiosis Institute of Technology (SIT) Affiliated to Symbiosis International University (SIU)PuneIndia
  2. 2.Smt. Kashibai Navale College of Engineering Affiliated to Savitribai Phule Pune UniversityPuneIndia

Personalised recommendations