A novel DEA-OR algorithm for route failure recovery in dense wireless sensor networks

Article
  • 40 Downloads

Abstract

Wireless sensor nodes generally have less memory and low battery life. Due to this constraint, a strong algorithm is needed which can reduce the energy consumption. Communication in wireless sensor network (WSN) depends on active number of neighboring nodes and battery power of the operating node. Factors like neighbor availability, link stability, energy and route failures directly influence network performance, for which optimization is vital. Traditional optimization techniques advise solutions that consume higher number of iterations without considering post network metrics like link stability and path cost. To address the issues of the existing energy optimization techniques, we put forward an innovative distance and energy aware optimized routing (DEA-OR) algorithm for WSNs. Considering distance as the base factor, DEA-OR algorithm gives solution for energy efficient transmission and route failure recovery. The process is of two stages: weight based neighbor selection for routing and cost-confined greedy method for route failure recovery. In this process, backtracking process of source is prevented through which overhead in neighbor selection is minimized. Our proposed algorithm minimizes energy consumption, delay and overhead thereby improvising throughput and network lifetime.

Keywords

Cost estimation Distance based neighbor selection Energy efficient routing Link recovery Weight computation 

References

  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Computer networks 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Sohraby, K., Minoli, D., Znati, T.: Wireless sensor networks: technology, protocols, and applications. Chapter 4. Wiley, Hoboken (2007)CrossRefGoogle Scholar
  3. 3.
    Bokareva, T., Hu, W., Kanhere, S., Ristic, B., Gordon, N., Bessell, T., Rutten, M., Jha, S.: Wireless sensor networks for battlefield surveillance. In: Proceedings of the Land Warfare Conference, pp. 1–8 (2006)Google Scholar
  4. 4.
    Raghavendra, C., Sivalingam, K., Znati, T.: Wireless Sensor Networks, 1st edn. Springer, New York (2006)MATHGoogle Scholar
  5. 5.
    Muruganathan, S.D., Ma, D.C.F., Bhasin, R.I., Fapojuwo, A.O.: A centralized energy-efficient routing protocol for wireless sensor networks. IEEE Commun. Mag. 43(3), 08–13 (2005)CrossRefGoogle Scholar
  6. 6.
    Mhatre, V., Rosenberg, C.: Design guidelines for wireless sensor networks: communication, clustering and aggregation. Ad Hoc Netw. 2(1), 45–63 (2004)CrossRefGoogle Scholar
  7. 7.
    Lindsey, S, Raghavendra, C.S.: PEGASIS: power-efficient gathering in sensor information systems. In: Aerospace Conference Proceedings, pp. 31–38, IEEE (2002)Google Scholar
  8. 8.
    Younis, O., Fahmy, S.: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mob. Comput. 3(4), 366–379 (2004)CrossRefGoogle Scholar
  9. 9.
    Jarry, A., Leone, P., Powell, O., Rolim, J.: An optimal data propagation algorithm for maximizing the lifespan of sensor networks. In: International Conference on Distributed Computing in Sensor Systems, pp. 405–421, Springer, Berlin (2006)Google Scholar
  10. 10.
    Camilo, T., Carreto, C., Silva1, J.S., Boavida, F.: An energy-efficient ant-based routing algorithm for wireless sensor networks. In: International Workshop on Ant Colony Optimization and Swarm Intelligence, pp. 49–59, Springer, Berlin (2006)Google Scholar
  11. 11.
    Gang, H., Dongmei, X., Yuanzhong, W.: Research and improvement of LEACH for wireless sensor networks. Chin. J. Sens. Actuat. 6(20), 1391–1396 (2007)Google Scholar
  12. 12.
    Liu, M., Cao, J., Chen, G., Wang, X.: An energy-aware routing protocol in wireless sensor networks. Sensors 9(1), 445–462 (2009)CrossRefGoogle Scholar
  13. 13.
    Liu, Y., Luo, Z., Xu, K., Chen, L.: A reliable clustering algorithm base on LEACH protocol in wireless mobile sensor networks. In: 2nd IEEE International Conference on Mechanical and Electrical Technology (ICMET), pp. 692–696 (2010)Google Scholar
  14. 14.
    Cheng, D., Xun, Y., Zhou, T., Li, W.: An energy aware ant colony algorithm for the routing of wireless sensor networks. Intell. Comput. Inf. Sci. (2011).  https://doi.org/10.1007/978-3-642-18129-0_62 Google Scholar
  15. 15.
    Kim, Y., Lee, E., Park, H.S.: Ant colony optimization based energy saving routing for energy-efficient networks. IEEE Commun. Lett. 15(7), 779–781 (2011)CrossRefGoogle Scholar
  16. 16.
    Shankar, S., Varaprasad, G., Suresh, H.N.: Importance of on-demand modified power aware dynamic source routing protocol in mobile ad-hoc networks. IET Microwav. Antennas Propag. 8(7), 459–464 (2014)CrossRefGoogle Scholar
  17. 17.
    Han, Z., Wu, J., Zhang, J., Liu, L., Tian, K.: A general self-organized tree-based energy-balance routing protocol for wireless sensor network. IEEE Trans. Nucl. Sci. 61(2), 732–740 (2014)CrossRefGoogle Scholar
  18. 18.
    Liu, X.: An optimal-distance-based transmission strategy for lifetime maximization of wireless sensor networks. IEEE Sens. J. 15(6), 3484–3491 (2015)CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Electronics and CommunicationManakula Vinayagar Institute of TechnologyPuducherryIndia
  2. 2.Department of Electronics and CommunicationAdhiparasakthi Engineering CollegeMelmaruvathurIndia

Personalised recommendations