Cluster Based Routing Scheme for Heterogeneous Nodes in WSN–A Genetic Approach

  • L. Lakshmanan
  • A. Jesudoss
  • V. Ulagamuthalvi
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


Wireless Sensor Network (WSN) is a propelled key region of research which helps in distinguishing numerous unmanned applications in woods based dangerous zones. The wireless sensor node should function for a long interval by utilizing the available energy resources and should full fill reliability by means of data transmission, even any one of the node fails. Hence in this paper we proposed a new hybrid approach in routing protocol by combining Particle Swarm Optimization (PSO) routing protocol with clustering algorithm. Here the approach focuses fully on Ant Colony Optimization & Bee Colony Optimization (ACO & BCO) on PSO routing protocol and K-Means clustering algorithm for illustrating the clusters of node or grouping the nodes. The proposed approach is tested for its proficiency, performance, energy consumption level and reliability using OMNETPP. The experimental results are shown in the form of graph.


ACO BCO K-Means PSO Wireless sensor networks 



The authors would like to acknowledge that this work has been carried out at DST-FIST sponsored Wireless Sensor Network Platform Lab (order Sanction No.: SR/FST/ETI-413/2018 Dated: 8th February 2018), Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology.


  1. 1.
    Lakshmanan, L., Tomar, D.C.: Optimizing localization route using particle swarm–a genetic approach. Am. J. Appl. Sci. 11(3), 520–527 (2014)CrossRefGoogle Scholar
  2. 2.
    Lee, H.-C.: Towards a general wireless sensor network platform for outdoor environment monitoring. IEEE Sens. J. 15, 3751–3758 (2012)Google Scholar
  3. 3.
    Velavarthy, N., Sindhura, S.: Evaluation of routing protocols used in wireless sensor networks monitoring temperature in composting heaps. In: Annual IEEE India Conference (IDCON2011), December 16, pp. 1–4 (2011)Google Scholar
  4. 4.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 1995 IEEE International Conference on Neural Networks (ICNN 95), November 27–December 01, pp. 1942–1948. IEEE (1995)Google Scholar
  5. 5.
    Cai, Y.G., Wei, M.: Self adaptive chaos particle swarm optimization for allied vehicle routing problem. J. Syst. Eng. 32(10), 2208–2214 (2012)Google Scholar
  6. 6.
    Mascareñas, D., Flynn, E., Farrar, C., Park, G., Todd, M.: A mobile host approach for wireless powering and interrogation of structural health monitoring sensor networks. IEEE Sens. J. 9, 1719–1726 (2009)CrossRefGoogle Scholar
  7. 7.
    Santos, I.M., Dota, M.A., Cugnasca, C.E.: Dynamic definition of the sampling rate of data in Wireless Sensor network with adaptive automata. IEEE Latin Am. Trans. 9, 963–968 (2011)CrossRefGoogle Scholar
  8. 8.
    Derr, K., Manic, M.: Wireless sensor network configuration–part II: adaptive coverage for decentralized algorithms. IEEE Trans. Ind. Inform. 9, 1717–1727 (2013)CrossRefGoogle Scholar
  9. 9.
    Campolo, C., Iera, A., Molinaro, A., Paratore, S.Y., Ruggeri, G.: SMaRTCaR: an integrated smart phone based platform to support traffic management applications. In: 2012 First International Workshop on Vehicular Traffic Management for Smart Cities (VTM), pp. 1–6 (2012)Google Scholar
  10. 10.
    Majumder, R., Bag, G., Kim, K.H.: Power sharing and control in distributed generation with wireless sensor networks. IEEE Trans. Smart Grid 3(2), 618–634 (2012)CrossRefGoogle Scholar
  11. 11.
    Ouacha, A., El Abbadi, J., Habbani, A., Bouamoud, B.: Proactive routing based distributed energy consumption. In: 2013 8th International Conference Intelligent Systems: Theories and Applications (SITA), pp. 1–5 (2013)Google Scholar
  12. 12.
    Masonta, M., Haddad, Y., De Nardis, L., Kliks, A., Holland, O.: Energy efficiency in future wireless networks: cognitive radio standardization requirements. In: 2012 IEEE 17th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD), pp. 31–35 (2012)Google Scholar
  13. 13.
    Accettura N., Palattella M.R., Dohler M., Grieco L.A., Boggia, G.: Standardized power-efficient & internet enabled communication stack for capillary M2M networks. In: 2012 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 226–231Google Scholar
  14. 14.
    Kan, Y., Gidlund, M., Akerberg, J., Bjorkman, M.: Reliable RSS-based routing protocol for industrial wireless sensor networks. In: IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, pp. 3231–3237 (2012)Google Scholar
  15. 15.
    Italian Cultural Institute.
  16. 16.
    Ancillotti, E., Bruno, R., Conti, M.: On the interplay between RPL and address auto configuration protocols in LLNs. In: 9th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 1275–1282 (2013)Google Scholar
  17. 17.
    Hashizume, A., Mizuno T., Mineno, H.: Energy monitoring system using sensor networks in residential houses. In: 26th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 595–600 (2012)Google Scholar
  18. 18.
    Reinhardt, A., Morar, O., Santini, S., Zöller, S., Steinmetz, R.: CBFR: bloom filter routing with gradual forgetting for tree-structured wireless sensor networks with mobile nodes. In: IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–9 (2012)Google Scholar
  19. 19.
    Cherrier S., Ghamri-Doudane, Y.M., Lohier, S., Roussel G., Services collaboration in wireless sensor and actuator networks: orchestration versus choreography. In: IEEE Symposium on Computers and Communications (ISCC), pp. 000411–000418 (2012)Google Scholar
  20. 20.
    Jesudoss, A., Subramaniam, N.P.: EPBAS: securing cloud-based healthcare ınformation systems using enhanced password-based authentication scheme. Asian J. Inf. Technol. 15(14), 2457–2463 (2016)Google Scholar
  21. 21.
    Jose, T.K., Ulagamuthalvi, V.: Enhancement of distributed replica file system performance using probabilistic file share system. J. Theor. Appl. Inf. Technol. (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Sathyabama Institute of Science and TechnologyChennaiIndia

Personalised recommendations