Shuffled Particle Swarm Optimization for Energy Efficiency Using Novel Fitness Function in WSN

  • Amruta LipareEmail author
  • Damodar Reddy Edla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)


Clustering of sensor nodes in Wireless Sensor Networks (WSN) is a critical task in order to minimize energy consumption as well as prolonging the lifetime of the network. In cluster based WSN, the gateway from the cluster collects, aggregates and sends data to the base station. To perform these operations, the energy consumption depends on two significant factors; load on the gateway and the data transmission distance. The improper clustering of sensor nodes may consume more energy, and the network may die soon. Therefore, load balancing of gateways according to the transmission distance is necessary to increase the network lifetime. We applied the shuffling strategy from Shuffled Frog Leaping Algorithm (SFLA) to Particle Swarm Optimization (PSO) to implement an algorithm called shuffled PSO (SPSO) for improving energy efficiency in WSN. The quality of the solution is measured by the novel fitness function concerning the lifetime of gateways and average cluster distance. The extensive simulations are performed with some of the existing algorithms. From the experimental analysis, it is observed that the proposed clustering approach gives effective results.


Clustering Energy efficiency Particle swarm optimization Shuffled frog leaping algorithm Wireless sensor networks 


  1. 1.
    Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002). Scholar
  2. 2.
    Edla, D.R., Lipare, A., Cheruku, R.: Shued complex evolution approach for load balancing of gateways in wireless sensor networks. Wirel. Pers. Commun. 98(4), 3455–3476 (2018). Scholar
  3. 3.
    Edla, D.R., Lipare, A., Cheruku, R., Kuppili, V.: An efficient load balancing of gateways using improved shuffled frog leaping algorithm and novel fitness function for WSNs. IEEE Sens. J. 17(20), 6724–6733 (2017). Scholar
  4. 4.
    Eusuff, M., Lansey, K., Pasha, F.: Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng. Optim. 38(2), 129–154 (2006). Scholar
  5. 5.
    Handy, M., Haase, M., Timmermann, D.: Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In: 4th International Workshop on Mobile and Wireless Communications Network, pp. 368–372. IEEE (2002).
  6. 6.
    Kennedy, J.: Particle swarm optimization. Encyclopedia of machine learning, pp. 760–766 (2010)Google Scholar
  7. 7.
    Kuila, P., Gupta, S.K., Jana, P.K.: A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm Evol. Comput. 12, 48–56 (2013). Scholar
  8. 8.
    Kuila, P., Jana, P.K.: Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng. Appl. Artif. Intell. 33, 127–140 (2014). Scholar
  9. 9.
    Lipare, A., Edla, D.R.: Novel fitness function for SCE algorithm based energy efficiency in WSN. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7. IEEE, July 2018.
  10. 10.
    Xiangning, F., Yulin, S.: Improvement on LEACH protocol of wireless sensor network. In: 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), pp. 260–264. IEEE, October 2007.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Institute of Technology GoaFarmagudiIndia

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