Abstract
The life time extension in the wireless sensor network (WSN) is the major concern in real time application, if the battery attached with the sensor node life is not optimized properly then the network life fall short. A protocol using a new evolutionary technique, cat swarm optimization (CSO), is designed and implemented in real time to minimize the intra-cluster distances between the cluster members and their cluster heads and optimize the energy distribution for the WSNs. We analyzed the performance of WSN protocol with the help of sensor nodes deployed in a field and grouped in to clusters. The novelty in our proposed scheme is considering the received signal strength, residual battery voltage and intra cluster distance of sensor nodes in cluster head selection with the help of CSO. The result is compared with the well-known protocol Low-energy adaptive clustering hierarchy-centralized (LEACH-C) and the swarm based optimization technique Particle swarm optimization (PSO). It was found that the battery energy level has been increased considerably of the traditional LEACH and PSO algorithm.









Similar content being viewed by others
References
Baronti, Paolo, et al.: Wireless sensor networks: a survey on the state of the art and the 802.15. 4 and ZigBee standards. Comput. Commun. 30(7), 1655–1695 (2007)
Zhang, B., Simon, R., Aydin, H.: Harvesting-aware energy management for time-critical wireless sensor networks with joint voltage and modulation scaling. IEEE Trans. Ind. Inf. 9(1), 514–526 (2013)
Tan, Y.K., Panda, S.K.: Self-autonomous wireless sensor nodes with wind energy harvesting for remote sensing of wind-driven wildfire spread. IEEE Trans. Instrum. Meas. 60(4), 1367–1377 (2011)
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensornetworks. System sciences, 2000. In: IEEE Proceedings of the 33rd Annual Hawaii International Conference (2000)
Heinzelman, W.B.: Application-specific protocol architectures for wireless networks. Diss. Massachusetts Institute of Technology (2000)
Lindsey, S., Raghavendra, C.S.: PEGASIS: power-efficient gathering in sensor information systems. In: IEEE Aerospace Conference Proceedings, vol. 3 (2002)
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)
Wang, J., et al.: An energy efficient stable election-based routing algorithm for wireless sensor networks. Sensors 13(11), 14301–14320 (2013)
Kannan, G., SreeRenga, R.T.: Energy efficient distributed cluster head scheduling scheme for two tiered wireless sensor network. Egypt. Inform. J. 16, 167–174 (2015)
Amgoth, T., Jana, P.K.: Energy-aware routing algorithm for wireless sensor networks. Comput. Electr. Eng. 41, 357–367 (2015)
Kuila, P., Jana, P.K.: A novel differential evolution based clustering algorithm for wireless sensor networks. Appl. Soft Comput. 25, 414–425 (2014)
Latiff, N.M., Tsimenidis, C.C., Sharif, B.S.: Energy-aware clustering for wireless sensor networks using particle swarm optimization. Personal, indoor and mobile radio communications, 2007. PIMRC 2007. In: IEEE 18th International Symposium (2007)
Singh, B., Lobiyal, D.K.: Energy-aware cluster head selection using particle swarm optimization and analysis of packet retransmissions in WSN. Proced. Technol. 4, 171–176 (2012)
Siew, Z.W., et al.: Cluster heads distribution of wireless sensor networks via adaptive Particle Swarm Optimization. In: Computational Intelligence, Communication Systems and Networks (CICSyN), 2012 IEEE Fourth International Conference (2012)
Karaboga, Dervis, Okdem, Selcuk, Ozturk, Celal: Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel. Netw. 18(7), 847–860 (2012)
Hoang, DucChinh, et al.: Real-time implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE Trans. Ind. Inform. 10(1), 774–783 (2014)
Kong, L., et al.: An energy-aware routing protocol using cat swarm optimization for wireless sensor networks. Advanced Technologies, Embedded and Multimedia for Human-Centric Computing. Springer, Netherlands, pp. 311–318 (2014)
Kong, L., et al.: A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network. Int. J. Distrib. Sens. Netw. 11, 729680 (2015)
Chu, S.-C., Tsai, P.-W.: Computational intelligence based on the behavior of cats. Int. J. Innov. Comput. Inf. Control 3(1), 163–173 (2007)
Chu, S.-C., Tsai, P.-W., Pan, J.-S.: Cat Swarm Optimization. PRICAI 2006: Trends in Artificial Intelligence. Springer, Berlin, vol. 2006, pp. 854–858 (2006)
Santosa, B., Ningrum, M.K.: Cat swarm optimization for clustering. Soft computing and pattern recognition, 2009. SOCPAR’09. In: International Conference of IEEE (2009)
Low Power RF ICs-1GHz-CC1100—Texas Instruments. http://www.ti.com/lit/ds/symlink/cc1100.pdf
Microcontroller PIC16F87/88. http://ww1.microchip.com/downloads/en/DeviceDoc/30487D.pdf
Yan, R., Sun, H., Qian, Y.: Energy-aware sensor node design with its application in wireless sensor networks. IEEE Trans. Instrum. Meas. 62(5), 1183–1191 (2013)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chandirasekaran, D., Jayabarathi, T. Cat swarm algorithm in wireless sensor networks for optimized cluster head selection: a real time approach. Cluster Comput 22 (Suppl 5), 11351–11361 (2019). https://doi.org/10.1007/s10586-017-1392-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1392-4