Your browser does not support JavaScript!

Binary grey wolf optimisation-based topology control for WSNs

Binary grey wolf optimisation-based topology control for WSNs

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Wireless Sensor Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Wireless sensor networks (WSNs) are composed of a large number of sensor nodes that are deployed at target locations. Topology control (TC) is one of the significant fundamental challenges in WSNs because of node energy and computing power constraints. TC algorithms try to produce reduced topology by preserving network connectivity. This study presents a novel TC algorithm based on binary Grey wolf optimisation. It uses the active and inactive schedules of sensor nodes in binary format as well as introduces fitness function to minimise the number of active nodes (ANs) for achieving the target of lifetime expansion of the nodes and network. The proposed algorithm is compared with other TC algorithms. The result reduces a minimum of 10% of ANs and energy consumption by 6.84%. The proposed approach also gives maximum coverage and connectivity. The designed fitness function also benefits in the process of selecting a node with low residual energy to join the active topology. The standard deviation in the remaining energy for the proposed algorithms is lower than the other TC schemes.


    1. 1)
      • 8. Yuanyuan, Z., Jia, X., Yanxiang, H.: ‘Energy efficient distributed connected dominating sets construction in wireless sensor networks’. Proc. ACM Int. Conf. Wireless Communications and Mobile Computing, Vancouver, BC, Canada, 2006, pp. 797802.
    2. 2)
      • 18. Goyal, S., Manjeet, S.P.: ‘Modified bat algorithm for localization of wireless sensor networks’, Wirel. Pers. Commun., 2016, 86, (2), pp. 657670.
    3. 3)
      • 11. Li, N., Hou, J.C., Sha, L.: ‘Design and analysis of an MST-based topology control algorithm’, IEEE Trans. Wirel. Commun., 2005, 4, (3), pp. 11951206.
    4. 4)
      • 7. Miguel, L., Wightman, P.M.: ‘Topology control in wireless sensor networks: with a companion simulation tool for teaching and research’ (Springer Science & Business Media, Berlin, Germany, 2009).
    5. 5)
      • 6. Wightman, P.M., Labrador, M.A.: ‘A3cov: a new topology construction protocol for connected area coverage in WSN’. Proc. IEEE Wireless Communications and Networking Conf., Cancun, Quintana Roo, 2011, pp. 522527.
    6. 6)
      • 16. Nikdel, A., Mahdi, M., Hagar, N.: ‘An intelligent PSO-based topology control protocol for wireless sensor networks’, Int. J. Smart Home, 2014, 8, (4), pp. 123138.
    7. 7)
      • 2. Panagiotakos, G., Sakavalas, D., Pagourtzis, A.: ‘Reliable broadcast with respect to topology knowledge’, Distrib. Comput., 2017, 30, (2), pp. 87102.
    8. 8)
      • 13. Liao, W., Yucheng, K., Ying-Shan, L.: ‘A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks’, Expert Syst. Appl., 2011, 38, (10), pp. 218012188.
    9. 9)
      • 19. Bingyu, Y., Chen, G., Guo, W.: ‘Topology control in wireless sensor networks based on discrete particle swarm optimization’. Proc. IEEE Int. Conf. Intelligent Computing and Intelligent Systems, Shanghai, China, 2009, pp. 269273.
    10. 10)
      • 15. Guo, W., Bin, Z., Guolong, C., et al: ‘A PSO-optimized minimum spanning tree-based topology control scheme for wireless sensor networks’, Int. J. Distrib. Sens. Netw., 2013, 9, (4), p. 985410.
    11. 11)
      • 10. Wang, Y., Li, F., Dahlberg, T.A.: ‘Energy-efficient topology control for three-dimensional sensor networks’, Int. J. Sens. Netw., 2008, 4, (1), pp. 6878.
    12. 12)
      • 5. Wightman, P.M., Miguel, L.: ‘Topology control in wireless sensor networks’. PhD dissertation, Department of Computer of Science and Engineering, University of South Florida, Tampa, USA, 2010.
    13. 13)
      • 9. Dai, F., Wu, J.: ‘An extended localized algorithm for connected dominating set formation in ad hoc wireless networks’, IEEE Trans. Parallel Distrib. Syst., 2004, 15, (10), pp. 908920.
    14. 14)
      • 3. Becker, M., Schaust, S., Wittmann, E.: ‘Performance of routing protocols for real wireless sensor networks’. Proc. Tenth Int. Symp. Performance Evaluation of Computer and Telecommunication Systems (SPECTS 07), San Diego, CA, USA, 2007.
    15. 15)
      • 14. Abreu, R.C., Jose, E.C.A.: ‘A particle swarm optimization algorithm for topology control in wireless sensor networks’. Proc. 30th Int. Conf. Chilean Computer Science Society, Curico, Chile, 2011, pp. 813.
    16. 16)
      • 12. Liu, L.F., Liu, Y.: ‘Topology control scheme based on simulated annealing algorithm in wireless sensor networks’, J. Commun., 2006, 27, (9), pp. 7176.
    17. 17)
      • 4. Gąsieniec, L., Peleg, D., Xin, Q.: ‘Faster communication in known topology radio networks’, Distrib. Comput., 2007, 19, (4), pp. 289300.
    18. 18)
      • 20. Mirjalili, S., Mirjalili, S.M., Lewis, A.: ‘Grey wolf optimizer’, Adv. Eng. Softw., 2014, 69, pp. 4661.
    19. 19)
      • 22. Cai, Y., Li, M., Wu, M.-Y.: ‘ACOS: an area-based collaborative sleeping protocol for wireless sensor networks’, Ad Hoc Sens. Wirel. Netw., 2007, 3, (1), pp. 7797.
    20. 20)
      • 21. Al-Aboody, N., Al-Raweshidy, H.: ‘Grey wolf optimization-based energy-efficient routing protocol for heterogeneous wireless sensor networks’. 2016 4th Int. Symp. on Computational and Business Intelligence (ISCBI), Olten, Switzerland, 2016, pp. 101107.
    21. 21)
      • 1. Shah, S.H., Yaqoob, I.: ‘A survey: Internet of things technologies, applications and challenges’. 2016 IEEE Smart Energy Grid Engineering (SEGE), Oshawa, Canada, 2016, pp. 381385.
    22. 22)
      • 17. Houssein, E.H., Wazery, Y.M.: ‘Vortex search topology control algorithm for wireless sensor network’, Int. J. Intell. Eng. Syst., 2017, 10, (6), pp. 8797.

Related content

This is a required field
Please enter a valid email address