Energy efficient clustering and routing are two well known problems in wireless sensor networks. In this paper, we propose genetic algorithm based approaches for clustering and routing in wireless sensor networks. The clustering is based on residual energy of the gateways and distance from sensor nodes to their corresponding cluster head. The routing scheme is also based on the residual energy of the gateways along with a trade-off between transmission distance and number of forwards. We perform extensive simulations of the proposed algorithms and compare the simulation results with that of the existing algorithms. The results demonstrate that the proposed algorithms outperform the existing algorithms in terms of various performance metrics including energy consumption, number of active nodes, first gateway die and number of dead gateway per round.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.
Abbasi, A. A., & Mohamad, Y. A. (2007). Survey on clustering algorithms for wireless sensor networks. Computer Communications, 30, 2826–2841.
Akkaya, K., & Younis, M. (2005). A survey on routing protocols for wireless sensor networks. Ad Hoc Networks, 3(3), 325–349.
Gupta, G., & Younis, M. (2003). Load-balanced clustering of wireless sensor networks. In: International conference on communication (ICC 2003), pp. 1848–1852.
Low, C. P., Fang, C., Ng, M. J., & Ang, H. Y. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31, 750–759.
Bari, A., Wazed, S., Jaekel, A., & Bandyopadhyay, S. (2009). A genetic algorithm based approach for energy efficient routing in two-tiered sensor networks. Ad Hoc Networks, 7, 665–676.
Kuila, P., & Jana, K. P. (2012). Improved load balanced clustering algorithm for wireless sensor networks. In: Advanced computing, networking and security—international conference (ADCONS 2011), LNCS 7135, pp. 399–404.
Kuila, P., & Jana, K. P. (2014). Approximation schemes for load balanced clustering in wireless sensor networks. Journal of Supercomputing, 68, 87–105.
Bari, A., Jaekel, A., & Bandyopadhyay, S. (2008). Clustering strategies for improving the lifetime of two-tiered sensor networks. Computer Communications, 31, 3451–3459.
Kuila, P., Gupta, S. K., & Jana, K. P. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.
Gupta, S. K., Kuila, P., & Jana, K. P. (2013). GAR: An energy efficient GA-based routing for wireless sensor networks. In: International conference on distributed computing and internet technologies (ICDCIT 2013), LNCS 7753, pp. 267–277.
Saleem, M., Caro, A. G., & Farooq, M. (2011). Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Information Sciences, 181, 4597–4624.
Chiang, S. S., Huang, H. C., & Chang, C. K. (2007). A minimum hop routing protocol for home security systems using wireless sensor networks. IEEE Transactions on Consumer Electronics, 53, 1483–1489.
Heinzelman, B. W., Chandrakasan, A. P., & Balakrishnan, H. (2002). Application specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1, 660–670.
Tyagi, S., & Kumar, N. (2013). A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. Journal of Network and Computer Applications, 36, 623–645.
Al-Refai, H., Awneh, A., Batiha, K., Abu, A. A., & Rahman, Y. M. E. (2011). Efficient routing LEACH (ER-LEACH) enhanced on LEACH protocol in wireless sensor networks. International Journal of Academic Research (Part I), 3, 42–48.
Kuila, P., & Jana, K. P. (2012). An energy balanced distributed clustering and routing algorithm for wireless sensor networks. In: Parallel, distributed and grid computing (PDGC 2012), IEEE Xplore, pp. 220–225.
Kuila, P., & Jana, K. P. (2012). Energy efficient load-balanced clustering algorithm for wireless sensor network. In: International conference on communication computing and security (ICCCS 2012), Procedia Technology 6, pp. 771–777.
Chakraborty, A., Mitra, K. S., & Naskar, K. M. (2011). A genetic algorithm inspired routing protocol for wireless sensor networks. International Journal of Computational Intelligence Theory and Practice, 6, 1–10.
Enan, A. K., & Attea, A. B. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation, 1, 195–203.
Singh, B., & Lobiyal, K. D. (2012). Energy-aware cluster head selection using particle swarm optimization and analysis of packet retransmission in WSN. Procedia Technology, 4, 171–176.
Abdul, M. N. A., Tsimenidis, C. C., & Sharif, B. S. (2007). Energy aware clustering for wireless sensor networks using particle swarm optimization. IEEE PIMRC, pp. 1–5.
Zungeru, M. A., Ang, M. L., & Seng, P. K. (2012). Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison. Journal of Network and Computer Applications, 35, 1508–1536.
Goldberg, E. D. (2007). Genetic algorithms: Search optimization and machine learning. Massachusetts: Addison Wesley.
Burhainah, A. F., & Hamza, A. A. (2008). Enhanced traveling salesman problem solving by genetic algorithm technique (TSPGA). World Academy of Science, Engineering and Technology, 38, 296–302.
Zakir, A. H. (2010). Genetic algorithm for the traveling salesman problem using sequential constructive crossover operator. International Journal of Biometrics & Bioinformatics (IJBB), 3(6), 96–105.
Tang, J., Hao, B., & Sen, A. (2006). Relay node placement in large scale wireless sensor networks. Computer Communications, 4(29), 490–501.
Shujuan, J., & Keqiu, L. (2009). LBCS: A load balanced clustering scheme in wireless sensor networks. In: Proceedings of third international conference on multimedia and ubiquitous engineering, pp. 221–225.
Habib, A. M., & Das, K. S. (2008). A trade-off between energy and delay in data dissemination for wireless sensor networks using transmission range slicing. Computer Communications, 31, 1687–1704.
Konak, A., Coit, W. D., & Smith, E. A. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering and System Safety, 91, 992–1007.
Kuila, P., & Jana, K. P. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.
About this article
Cite this article
Gupta, S.K., Jana, P.K. Energy Efficient Clustering and Routing Algorithms for Wireless Sensor Networks: GA Based Approach. Wireless Pers Commun 83, 2403–2423 (2015). https://doi.org/10.1007/s11277-015-2535-7
- Wireless sensor networks
- NP-hard problem
- Genetic algorithm