Analysis of Shortest Route for Heterogeneous Node in Wireless Sensor Network

  • L. Lakshmanan
  • T. C. Tomar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


In this paper, we deeply investigate a routing model for heterogeneous nodes in wireless sensor networks using Voronoi cell. We estimate the actual traffic among the sensor node, which is defined clearly as the traffic packets, controlled at each server. Network load is monitored using the traffic inbound rules, and the estimation is defined in the circular pattern in the form of Voronoi cell. Each functional patterns of the traffic are classified as source and destination in asymptotic rule. Each sensor nodes traffic are redirected to the centralized server acting in the real world, where the sensor data are patched periodically, and the data packets traveling from the node to node are updated. Each traffic patterns and sensor nodes are classified, and the node communication regions are known to the base station by drawing the pattern in Voronoi. The experimental results show the actual working model, and our routing model yields 78 % accuracy.


Wireless Sensor networks Genetic swarm optimization Heuristic search technique Voronoi cell Snapshot 


  1. 1.
    V. Neehaarika, S. Sindhura, Evaluation of routing protocols used in wireless sensor networks monitoring temperature in composting heaps, in Annual IEEE India Conference(IDCON2011) (2011), pp. 1–4Google Scholar
  2. 2.
    J. Kennedy, R. Eberhart, Particle swarm optimization, in 1995 IEEE International Conference on Neural Networks (ICNN 95) (1995), pp. 1942–1948Google Scholar
  3. 3.
    K. Derr, M. Manic, Wireless sensor network configuration—part II: adaptive coverage for decentralized algorithms. IEEE Trans. Industr. Inf. 9(3), 1728–1738 (2013) Google Scholar
  4. 4.
    Y.G. Cai, M. Wei, Self adaptive chaos particle swarm optimization for allied vehicle routing problems. J. Syst. Eng. 32(10), 2208–2214 (2012)Google Scholar
  5. 5.
    L. Lakshmanan, D.C. Tomar, Optimizing localization route using particle swarm-a genetic approach. Am. J. Appl. Sci. 11(3), 520–527 (2014)CrossRefGoogle Scholar
  6. 6.
    H.-C. Lee, Towards a general wireless sensor network platform for outdoor environment monitoring. IEEE Sens. J. 12(4), 1–5 (2012) Google Scholar
  7. 7.
    D. Mascareñas, E. Flynn, C. Farrar, G. Park, M. Todd, A mobile host approach for wireless powering and interrogation of structural health monitoring sensor networks. IEEE Sens. J. 9(12), 1719–1726 (2009) Google Scholar
  8. 8.
    I.M. Santos, M.A. Dota, C.E. Cugnasca, Dynamic definition of the sampling rate of data in wireless sensor network with adaptive automata. IEEE Latin Am. Trans. 9(6), 963–968 (2011)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Faculty of ComputingSathyabama UniversityChennaiIndia
  2. 2.Department of Information TechnologyJerusalem College of EngineeringChennaiIndia

Personalised recommendations