Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon openaccess Multiple layers uneven clustering algorithm based on residual energy for wireless sensor networks

Since the node energy is limited, wireless sensor networks need efficient routing algorithms to reduce energy consumption. The authors proposed a multiple layer uneven clustering algorithm. This algorithm is used for single-hop network. Nodes submit data to the cluster heads, and then all cluster heads submit data to the sink node by single hop. The authors divide the whole network into several layers with different reign. The inner diameter and outer diameter of each layer are calculated by a sink node to meet the aim of balancing energy consumption. The algorithm chooses cluster heads according to the nodes’ residual energy in each layer. According to the reign of each layer, from the perspective of reducing energy consumption, the authors limit the minimum distance between two cluster heads, so that the cluster heads can be distributed evenly in one layer. Simulation results show that the algorithm can promote the lifetime of networks.

References

    1. 1)
      • 11. Afsar, M., Tayarani-N, M.H., Aziz, M.: ‘An adaptive competition-based clustering approach for wireless sensor networks’, Telecommun. Syst., 2016, 61, (1), pp. 181204.
    2. 2)
      • 3. Ennajari, H., Maissa, Y.B., Mouline, S.: ‘Energy efficient in-network aggregation algorithms in wireless sensor networks: a survey’. Advances in Ubiquitous Networking 2, Springer Singapore, 2017, vol. 397, pp. 135148.
    3. 3)
      • 10. Bao, X., Xie, J., Nan, L., et al: ‘WRECS: An improved cluster heads selection algorithm for WSNs’, Journal of Software, 2014, 9, (2), pp. 507514.
    4. 4)
      • 6. Pantazis, N.A., Nikolidakis, S.A., Vergados, D.D.: ‘Energy-efficient routing protocols in wireless sensor networks: a survey’, IEEE Commun. Surv. Tutorials, 2013, 15, (2), pp. 551591.
    5. 5)
      • 4. Singh, K.: ‘WSN LEACH based protocols: a structural analysis’. Int. Conf. and Workshop on Computing and Communication, Vancouver, Canada, December 2015.
    6. 6)
      • 13. Su, J.S., Guo, W.Z., Yu, C.L., et al: ‘Fault-tolerance clustering algorithm with load-balance aware in wireless sensor network’, Chin. J. Comput., 2014, 37, pp. 445456.
    7. 7)
      • 1. Kulkarni, R.V., Forster, A., Venayagamoorthy, G.K.: ‘Computational intelligence in wireless sensor networks: a survey’, IEEE Commun. Surv. Tutorials, 2011, 13, (1), pp. 6896.
    8. 8)
      • 9. Batra, P.K., Kant, K.: ‘LEACH-MAC: a new cluster head selection algorithm for wireless sensor networks’, Wirel. Netw., 2016, 22, (1), pp. 4960.
    9. 9)
      • 5. Kumar, S., Verma, S.K., Kumar, A.: ‘Enhanced threshold sensitive stable election protocol for heterogeneous wireless sensor network’, Wirel. Pers. Commun., 2015, 85, (4), pp. 26432656.
    10. 10)
      • 2. Misra, S., Kumar, R.: ‘A literature survey on various clustering approaches in wireless sensor network’. Int. Conf. Communication Control and Intelligent Systems IEEE, 2017, pp. 18–22.
    11. 11)
      • 8. Xia, H., Zhang, R.H., Yu, J., et al: ‘Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks’, Int. J. Wirel. Inf. Netw., 2016, 23, (2), pp. 110.
    12. 12)
      • 12. Baranidharan, B., Santhi, B.: ‘DUCF: distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach’, Appl. Soft Comput., 2016, 40, pp. 495506.
    13. 13)
      • 7. Su, S.B., Guo, H.F., Tian, H.M., et al: ‘A novel pattern clustering algorithm based on particle swarm optimization joint adaptive wavelet neural network model’, Mobile Netw. Appl., 2017, 22, (4), pp. 692701.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.8332
Loading

Related content

content/journals/10.1049/joe.2018.8332
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address