Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

  • Mohammed M. AhmedEmail author
  • Essam H. Houssein
  • Aboul Ella Hassanien
  • Ayman Taha
  • Ehab Hassanien


The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the locations of sink nodes in LSWSNs play a vital role in term of saving energy. Furthermore, sink nodes have extremely extra resources such as large memory, powerful batteries, long-range antenna, etc. This paper proposes a multi-objective whale optimization algorithm (MOWOA) to determine the lowest number of sink nodes that cover the whole network. The major aim of MOWOA is to reduce the energy consumption and prolongs the lifetime of LSWSNs. To achieve these objectives, a fitness function has been formulated to decrease energy consumption and maximize the network’s lifetime. The experimental results revealed that the proposed MOWOA achieved a better efficiency in reducing the total power consumption by 26% compared with four well-known optimization algorithms: multi-objective grasshopper optimization algorithm, multi-objective salp swarm algorithm, multi-objective gray wolf optimization, multi-objective particle swarm optimization over all networks sizes.


Large-scale wireless sensor networks (LSWSNs) Multiple sink node Multi-objective optimization (MOO) Pareto front Whale optimization algorithm (WOA) 



  1. 1.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications magazine, 40(8), 102–114.Google Scholar
  2. 2.
    Espinosa-Ramos, J. I., et al. (2012). A new objective function to build seismic networks using differential evolution. In 2012 IEEE congress on evolutionary computation (CEC) (pp. 1–7). IEEE.Google Scholar
  3. 3.
    Chattopadhyay, S., & Vijayalakshmi, G. (2014). Improving the lifetime of wireless sensor network through energy conservation. International Journal of Computer Science and Information Technologies, 5(2), 2345–2347.Google Scholar
  4. 4.
    Osamaa, A., El-Said, S. A., & Hassanien, A. E. (2016). Energy-efficient routing techniques for wireless sensors networks. In Handbook of research on emerging technologies for electrical power planning, analysis, and optimization (pp. 37–62). IGI Global.Google Scholar
  5. 5.
    Coello, C. A. C. (2009). Evolutionary multi-objective optimization: Some current research trends and topics that remain to be explored. Frontiers of Computer Science in China, 3(1), 18–30.Google Scholar
  6. 6.
    Deb, K. (2011). Multi-objective optimisation using evolutionary algorithms: An introduction. In Multi-objective evolutionary optimisation for product design and manufacturing (pp. 3–34). Springer.Google Scholar
  7. 7.
    Ewees, A. A., Elaziz, M. A., & Houssein, E. H. (2018). Improved grasshopper optimization algorithm using opposition-based learning. Expert Systems with Applications, 112, 156–172.Google Scholar
  8. 8.
    Tharwat, A., Houssein, E. H., Ahmed, M. M., Hassanien, A. E., & Gabel, T. (2017). Mogoa algorithm for constrained and unconstrained multi-objective optimization problems. Applied Intelligence, 1–16.Google Scholar
  9. 9.
    Pradhan, P. M., & Panda, G. (2012). Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making. Ad Hoc Networks, 10(6), 1134–1145.Google Scholar
  10. 10.
    Oyman, E. I., & Ersoy, C. (2004). Multiple sink network design problem in large scale wireless sensor networks. In 2004 IEEE international conference on communications (Vol. 6, pp. 3663–3667). IEEE.Google Scholar
  11. 11.
    Kim, H., Seok, Y., Choi, N., Choi, Y., & Kwon, T. (2005). Optimal multi-sink positioning and energy-efficient routing in wireless sensor networks. In International conference on information networking (pp. 264–274). Springer.Google Scholar
  12. 12.
    Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, 1(4), 660–670.Google Scholar
  13. 13.
    Ahmed, M. M., Taha, A., Hassanien, A. E., & Hassanien, E. (2018). An optimized k-nearest neighbor algorithm for extending wireless sensor network lifetime. In International conference on advanced machine learning technologies and applications (pp. 506–515). Springer.Google Scholar
  14. 14.
    Peiravi, A., Mashhadi, H. R., & Hamed Javadi, S. (2013). An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems, 26(1), 114–126.Google Scholar
  15. 15.
    Armano, G., & Farmani, M. R. (2016). Multiobjective clustering analysis using particle swarm optimization. Expert Systems with Applications, 55, 184–193.Google Scholar
  16. 16.
    Snasel, V., Kong, L., Tsai, P., & Pan, J.-S. (2016). Sink node placement strategies based on cat swarm optimization algorithm. Journal of Network Intelligence, 1(2), 52–60.Google Scholar
  17. 17.
    Ahmed, M. M., Houssein, E. H., Hassanien, A. E., Taha, A., & Hassanien, E. (2017). Maximizing lifetime of wireless sensor networks based on whale optimization algorithm. In International conference on advanced intelligent systems and informatics (pp. 724–733). Springer.Google Scholar
  18. 18.
    Fouad, M. M., Snasel, V., & Hassanien, A. E. (2015). Energy-aware sink node localization algorithm for wireless sensor networks. International Journal of Distributed Sensor Networks, 11(7), 810356.Google Scholar
  19. 19.
    Saravanan, M., & Madheswaran, M. (2014). A hybrid optimized weighted minimum spanning tree for the shortest intrapath selection in wireless sensor network. Mathematical Problems in Engineering.Google Scholar
  20. 20.
    Rani, K. S. S., & Devarajan, N. (2012). Optimization model for sensor node deployment. European Journal of Scientific Research, 70(4), 491–498.Google Scholar
  21. 21.
    Jena, R. (2014). Artificial bee colony algorithm based multi-objective node placement for wireless sensor network. International Journal of Information Technology and Computer Science (IJITCS), 6(6), 25.Google Scholar
  22. 22.
    Vincze, Z., Fodor, K., Vida, R., & Vidács, A. (2006). Electrostatic modelling of multiple mobile sinks in wireless sensor networks. In Proceedings of the IFIP networking workshop on performance control in wireless sensor networks (PWSN 2006), Coimbra, Portugal (pp. 30–37).Google Scholar
  23. 23.
    Fei, Z., Li, B., Yang, S., Xing, C., Chen, H., & Hanzo, L. (2017). A survey of multi-objective optimization in wireless sensor networks: Metrics, algorithms, and open problems. IEEE Communications Surveys & Tutorials, 19(1), 550–586.Google Scholar
  24. 24.
    Hussien, A. G., Hassanien, A. E., Houssein, E. H., Bhattacharyya, S., & Amin, M. (2019). S-shaped binary whale optimization algorithm for feature selection. In Recent trends in signal and image processing (pp. 79–87). Springer.Google Scholar
  25. 25.
    Blagojevic, M., Geilen, M., Basten, T., & Hendriks, T. (2012). Fast sink placement for gossip-based wireless sensor networks. In 2012 IEEE 31st international on performance computing and communications conference (IPCCC) (pp. 110–119). IEEE.Google Scholar
  26. 26.
    Abidin, H. Z., Din, N. M., & Jalil, Y. E. (2013). Multi-objective optimization (MOO) approach for sensor node placement in WSN. In 2013 7th International conference on signal processing and communication systems (ICSPCS) (pp. 1–5). IEEE.Google Scholar
  27. 27.
    Chen, F., & Li, R. (2013). Sink node placement strategies for wireless sensor networks. Wireless Personal Communications, 68(2), 303–319.Google Scholar
  28. 28.
    Hacioglu, G., Kand, V. F. A., & Sesli, E. (2016). Multi objective clustering for wireless sensor networks. Expert Systems with Applications, 59, 86–100.Google Scholar
  29. 29.
    Zitzler, E., Laumanns, M., & Bleuler, S. (2004). A tutorial on evolutionary multiobjective optimization. In Metaheuristics for multiobjective optimisation (pp. 3–37).Google Scholar
  30. 30.
    Binh, H. T. T., Hanh, N. T., Dey, N., et al. (2018). Improved cuckoo search and chaotic flower pollination optimization algorithm for maximizing area coverage in wireless sensor networks. Neural Computing and Applications, 30(7), 2305–2317.Google Scholar
  31. 31.
    Shankar, T., Shanmugavel, S., & Rajesh, A. (2016). Hybrid HSA and PSO algorithm for energy efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary Computation, 30, 1–10.Google Scholar
  32. 32.
    Marks, M. (2010). A survey of multi-objective deployment in wireless sensor networks. Journal of Telecommunications and Information Technology, 3, 36–41.Google Scholar
  33. 33.
    Iqbal, M., Naeem, M., Anpalagan, A., Qadri, N. N., & Imran, M. (2016). Multi-objective optimization in sensor networks: Optimization classification, applications and solution approaches. Computer Networks, 99, 134–161.Google Scholar
  34. 34.
    Abidin, H. Z., Din, N. M., & Radzi, N. A. M. (2013). Deterministic static sensor node placement in wireless sensor network based on territorial predator scent marking behaviour. International Journal of Communication Networks and Information Security (IJCNIS), 5(3), 186–192.Google Scholar
  35. 35.
    Zainol Abidin, H., & Din, N. M. (2013). Sensor node placement in wireless sensor network based on territorial predator scent marking algorithm. ISRN Sensor Networks.Google Scholar
  36. 36.
    Shareef, A. Q., & Mijwel, M. M. (2014). Improved accuracy distribution localization in wireless sensor networks. International Journal of Computer Science and Mobile Computing, 3(6), 286–296.Google Scholar
  37. 37.
    Chen, B., Jamieson, K., Balakrishnan, H., & Morris, R. (2002). Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. Wireless Networks, 8(5), 481–494.Google Scholar
  38. 38.
    Konstantinidis, A., & Yang, K. (2011). Multi-objective energy-efficient dense deployment in wireless sensor networks using a hybrid problem-specific MOEA/D. Applied Soft Computing, 11(6), 4117–4134.Google Scholar
  39. 39.
    Coello, C. A. (2000). An updated survey of GA-based multiobjective optimization techniques. ACM Computing Surveys (CSUR), 32(2), 109–143.Google Scholar
  40. 40.
    Van Veldhuizen, D. A., & Lamont, G. B. (1998). Multiobjective evolutionary algorithm research: A history and analysis. Technical report, TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.Google Scholar
  41. 41.
    Sierra, M. R., & Coello, C. C. (2005). Improving PSO-based multi-objective optimization using crowding, mutation and e-dominance. In Evolutionary multi-criterion optimization (Vol. 3410, pp. 505–519). Springer.Google Scholar
  42. 42.
    Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.Google Scholar
  43. 43.
    Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2017). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 1–16.Google Scholar
  44. 44.
    Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.Google Scholar
  45. 45.
    Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. d S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119.Google Scholar
  46. 46.
    Reyes-Sierra, M., & Coello, C. C. (2006). Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International journal of computational intelligence research, 2(3), 287–308.Google Scholar
  47. 47.
    Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182–197.Google Scholar
  48. 48.
    Wightman, P. M., & Labrador, M. A. (2011). A3Cov: A new topology construction protocol for connected area coverage in WSN. In 2011 IEEE on wireless communications and networking conference (WCNC) (pp. 522–527). IEEE.Google Scholar
  49. 49.
    Banka, H., & Jana, P. K., et al. (2016). PSO-based multiple-sink placement algorithm for protracting the lifetime of wireless sensor networks. In Proceedings of the second international conference on computer and communication technologies (pp. 605–616). Springer.Google Scholar
  50. 50.
    Dandekar, D. R., & Deshmukh, P. (2013). Energy balancing multiple sink optimal deployment in multi-hop wireless sensor networks. In 2013 IEEE 3rd international on advance computing conference (IACC) (pp. 408–412). IEEE.Google Scholar
  51. 51.
    Kaur, N., Bedi, R. K., & Gangwar, R. (2016). A new sink placement strategy for WSNs. In International Conference on ICT in business industry & government (ICTBIG) (pp. 1–5). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computers and InformationMinia UniversityMinyaEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityGizaEgypt
  3. 3.Scientific Research Group in Egypt (SRGE)GizaEgypt

Personalised recommendations