Cluster Computing

, Volume 22, Supplement 5, pp 10449–10465 | Cite as

HHSRP: a cluster based hybrid hierarchical secure routing protocol for wireless sensor networks

  • C. DeepaEmail author
  • B. Latha


Wireless sensor networks happen to be fundamentally networks with energy restrictions. This is limited over longer period of time proves to be the challenge in clustering algorithms. We suggested a new method related to co-ordinator head (CNH) selection by using a mixed hierarchical cluster based algorithm that includes selecting the CNH happens to be the greatest value of the co-ordinator node (CN) and fitness value. This analysis put forward two different algorithms; mixed hierarchical cluster oriented routing program and hybrid hierarchical secure algorithm. A mixed hierarchal cluster based algorithm has been suggested for generating CNH and CN, for identifying the harmful node and packet is securely delivered. The source node transmits details about packet to the CN and then it chooses the pathway which is the shortest, depending upon the trust value among intermediate node and sensor node. The behavior activities of all the nodes are analyzed by the CNH, if anyone of the node’s behavior found to be incorrect, the organizer who finds the harmful node and CNH will drop those particular node behaviors. The CNH then changes to other path way immediately and transmits the delivery packet securely to the target destination under short time.


Co-ordinator node Hybrid hierarical routing Energy-efficient routing HHSRP Normalized routing load 


  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Kuila, P., Jana, P.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. J. Commun. Syst. 18, 1016–1025 (2014)Google Scholar
  3. 3.
    Kumar, R., Kumar, D.: Hybrid swarm intelligence energy efficient clustered routing algorithm for wireless sensor networks. J. Sens. 10, 1155–1174 (2015)Google Scholar
  4. 4.
    Jabbar, S., Minhas, A.A., Imran, M., Khalid, S., Saleem, K.: Energy efficient strategy for throughput improvement in wireless sensor networks. Sensors 15, 2473–2495 (2015)CrossRefGoogle Scholar
  5. 5.
    Abbasi, A.A., Younis, M.: A survey on clustering algorithms for wireless sensor networks. Comput. Commun. 30, 2826–2841 (2007)CrossRefGoogle Scholar
  6. 6.
    Heinzelman, W.B., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocols for wireless micro-sensor networks. In: Proceedings of Hawaii international conference on system sciences (2000)Google Scholar
  7. 7.
    Younis, O., Fahmy, S.: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Trans. Mob. Comput. 3, 366–379 (2004)CrossRefGoogle Scholar
  8. 8.
    Bandhopadhyay, S., Coyle, E.: An energy efficient hierarchical clustering algorithm for wireless sensor networks. In: Proceesings IEEE INFOCOM (2003)Google Scholar
  9. 9.
    Chan, H., Perrig, A.: ACE: an emergent algorithm for highly uniform cluster formation. In: Proceedings of the first European workshop on sensor networks, pp. 154–171 (2004)Google Scholar
  10. 10.
    Ye, M., Li, C.F., Chen, G.H., Wu, J.: An energy-efficient unequal clustering mechanism for wireless sensor networks. In: Proceedings of the international conference on mobile ad-hoc and sensor systems, p. 8 (2005)Google Scholar
  11. 11.
    Ye, M., Li, C.F., Chen, G.H., Wu, J.: EECS: an energy efficient clustering scheme in wireless sensor networks, IEEE International Performance Computing andCommunication Conference, pp. 535–540 (2005)Google Scholar
  12. 12.
    Ding, P., Holliday, J., Celik, A.: Distributed energy-efficient hierarchical clustering for wireless sensor networks. Lecture Notes in Computer Science, vol. 20 (2005)Google Scholar
  13. 13.
    Cao, Y., He, C.: A distributed clustering algorithm with an adaptive backoff strategy for wireless sensor networks. IEICE Trans. Commun. 89, 609–613 (2006)CrossRefGoogle Scholar
  14. 14.
    Dimokas, N., Katsaros, D., Manolopoulos, Y.: Energy-efficient distributed clustering in wireless sensor networks. J Parallel Distrib. Comput. 70, 371–383 (2010)CrossRefGoogle Scholar
  15. 15.
    Kuila, P., Gupta, S.K., Prasanta, K.J.: A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol. Comput. 12, 48–56 (2013)CrossRefGoogle Scholar
  16. 16.
    Kuila, P., Prasanta, K.J.: Approximation schemes for load balanced clustering in wireless sensor networks. J. Super Comput. 68, 87–105 (2014)CrossRefGoogle Scholar
  17. 17.
    Tarachand, A., Prasanta, K.J.: BDCP: a backoff-based distributed clustering protocol for wireless sensor networks. In: Proceedings of the International Conference on Advances in Computing, Communication and Informatics, pp. 1012–1016 (2013)Google Scholar
  18. 18.
    Boukerche, A., Pazzi, R.W.N., Araujo, R.B.: Fault-tolerant wireless sensor network routing protocols for the supervision of context-aware physicalenvironments. J Parallel Distrib. Comput. 66, 586–599 (2006)CrossRefGoogle Scholar
  19. 19.
    Perillo, M., Cheng, Z., Heinzelman, W.: On the problem of unbalanced load distribution in wireless sensor networks. In: Proceedings of the IEEE GLOBECOM workshops, pp. 74–79 (2004)Google Scholar
  20. 20.
    Muruganathan, S.D., Ma, D.C., Bhasin, R.I., Fapojuwo, A.O.: centralized energy-efficient routing protocol for wireless sensor networks. IEEE Commun. Mag. 43, S8–S13 (2005)CrossRefGoogle Scholar
  21. 21.
    Yu, M., Kin, K.L., Ankit, M.: A dynamic clustering and energy efficient routing techniques for sensor networks. IEEE Trans. Wirel. Commun. 6, 3069–3079 (2007)CrossRefGoogle Scholar
  22. 22.
    Fariborzi, H., Moghavvemi, M.: EAMTR: energy aware multi-tree routing for wireless sensor networks. IET Commun. 3, 733–739 (2009)CrossRefGoogle Scholar
  23. 23.
    Andrei, G., Sajid, H., Yang, L.T.: Distributed hierarchical search for balanced energy consumption routing spanning trees in wirelesssensor networks. J. Parallel Distrib. Comput. 70, 975–982 (2010)CrossRefGoogle Scholar
  24. 24.
    Ren, F., Zhang, J., He, T., Lin, C., Das, S.K.: EBRA: energy-balanced routing protocol for data gathering in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 22, 2018–2125 (2011)Google Scholar
  25. 25.
    Abdelsalam, H.S., Olariu, S.: BEES: bioinspired backbone selection in wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23, 44–51 (2012)CrossRefGoogle Scholar
  26. 26.
    Torkestani, J.A., Meybodi, M.R.: LLACA: an adaptive localized clustering algorithm for wireless ad-hoc networks. Comput. Electr. Eng. 37, 461–474 (2011)CrossRefGoogle Scholar
  27. 27.
    Nazir, B., Hasbullah, H.: Mobile nodes based clustering protocol for lifetime optimization in wireless sensor network. In: Proceedings of International Conference on Intelligence and Information Technology (ICIIT 2010), vol. 2, pp. 615-620 (2010)Google Scholar
  28. 28.
    Peethambaran, P., Jayasudha, J.S.: Survey of manet misbehaviour detection approaches. Int. J. Netw. Secur. Appl. 6, 19–29 (2014)Google Scholar
  29. 29.
    Sahu, N., Dubey, S., Diwan, T.D.: Performance evaluation of cluster-based routing protocols used in wireless sensor networks. Asian J. Sci. Technol. 7, 2213–2219 (2016)Google Scholar
  30. 30.
    Joshi, K., Lomte, V.: Preventing flooding attack in MANET using node-to-node authentication. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3, 136–140 (2013)Google Scholar
  31. 31.
    Singh, S.P., Sharma, S.C.: Secure clustering protocols in wireless sensor networks. J. Wirel. Sens. Netw. 3, 1–10 (2016)Google Scholar
  32. 32.
    Zhang, P., Xiao, H.P., Tan, H.P.: Clustering algorithms for maximizing the lifetime of wireless sensor networks with energy harvesting sensors. Comput. Netw. 57, 2689–2704 (2013)CrossRefGoogle Scholar
  33. 33.
    Salunkhe, S.G., Kumbhar, H.V.: A review of various energy efficientmobile sink routing protocols for wireless sensor network. Int. J. Curr. Eng. Technol. 6, 62–67 (2016)Google Scholar
  34. 34.
    Balaji, S., Priyadharsini, V.: A robust cluster head selection based on neighborhood contribution and average minimum power for MANETs. ICTACT J. Commun. Technol. 6, 1099–1104 (2015)CrossRefGoogle Scholar
  35. 35.
    Odedra, L., Revar, A., Lunagaria, M.H.: Comparative analysis of prevention and detection policies for selfish behaviour in MANET. Int. J. Adv. Res. Comput. Commun. Eng. 5, 605–607 (2016)Google Scholar
  36. 36.
    Mourad, W., Saroit, I.A., El Mahdy, H.N., Tawfik, B.B.S.: A new energy-efficient cluster head selection algorithm. Int. J. Sci. Eng. Res. 6, 387–390 (2015)Google Scholar
  37. 37.
    Virmani, D., Talwar, D., Dhingra, A., Bahl, T.: Priority based energy-efficient data forwarding algorithm in wireless sensor networks, Bhagwan Parshuram Institute of Technology, Rohini, Delhi.
  38. 38.
    Lee, H.J., Wicke, M., Kusy, B., Gnawali, O., Guibas, L.: Data stashing: energy-efficient information delivery to mobilesinks through trajectory prediction. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2010, Stockholm, Sweden. doi: 10.1145/1791212.1791247
  39. 39.
    Jea, D., Somasundara, A., Srivastava, M.: Multiple controlled mobile elements (data mules) for datacollection in sensor networks. In: Proceedings of IEEE/ACM International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina del Rey, California, USA, pp. 244–257, June (2005)Google Scholar
  40. 40.
    Bi, Y., Sun, L., Ma, J., Li, N., Khan, I.A., Chen, C.: HUMS: an autonomous moving strategy for mobile sinksin data-gathering sensor networks, EURASIP J. Wirel. Commun. Netw. doi: 10.1155/2007/64574
  41. 41.
    Yang, Y., Fonoage, M.I., Cardei, M.: Improving network lifetime with mobile wireless sensor networks. Comput. Commun. 33, 409–419 (2010)CrossRefGoogle Scholar
  42. 42.
    Nazir, B., Hasbullah, H.: Energy efficient and QoS aware routing protocol for clustered wireless sensor network. Comput. Electr. Eng. 39, 2425–2441 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Faculty of ComputingSathyabama UniversityChennaiIndia
  2. 2.Department of Computer Science and EngineeringKPR Institute of Engineering and TechnologyCoimbatoreIndia
  3. 3.Department of Computer Science and EngineeringSri Sai Ram Engineering CollegeChennaiIndia

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