Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm

Optimal data aggregation tree in wireless sensor networks based on intelligent water drops algorithm

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Energy conservation is an important aspect in wireless sensor networks (WSNs) to extend the network lifetime. In order to obtain energy-efficient data transmission within the network, sensor nodes can be organised into an optimal data aggregation tree with optimally selected aggregation nodes to transfer data. Various nature-inspired optimisation methods have been shown to outperform conventional methods when solving this problem in a distributed manner, that is, each sensor node makes its own decision on routing the data. In this study, a novel optimisation algorithm called intelligent water drops (IWDs) is adopted to construct the optimal data aggregation trees for the WSNs. Further enhancement of the basic IWD algorithm is proposed to improve the construction of the tree by attempting to increase the probability of selecting optimum aggregation nodes. The computational experiment results show that the IWD algorithm is able to obtain a better data aggregation tree with a smaller number of edges representing direct communication between two nodes when compared with the well-known optimisation method such as ant colony optimisation. In addition, the proposed improved version of the IWD algorithm provides better performance in comparison with the basic IWD algorithm for saving the energy of WSNs.


    1. 1)
    2. 2)
    3. 3)
      • C.E. Perkins , E.M. Belding-Royer , S.R. Das . Ad hoc on-demand distance vector (AODV) routing.
    4. 4)
      • D.B. Johnson , D.A. Maltz , Y.C. Hu . (2007) The dynamic source routing protocol for mobile ad hoc networks (DSR).
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: `Energy-efficient communication protocol for wireless microsensor networks', Proc. 33rd Annual Hawaii Int. Conf. on System Sciences, January 2000, 2, p. 10.
    9. 9)
    10. 10)
    11. 11)
      • Hoang, D.C., Kumar, R., Panda, S.K.: `Fuzzy c means clustering protocol for wireless sensor networks', Proc. of Int. Symp. on Industrial Electronics, July 2010, p. 3477–3482.
    12. 12)
      • Al-Karaki, J., Ul-Mustafa, R., Kamal, A.: `Data aggregation in wireless sensor networks – exact and approximate algorithms', Workshop on High Performance Switching and Routing (HPSR), 2004, p. 241–245.
    13. 13)
      • Krishnamachari, L., Estrin, D., Wicker, S.: `The impact of data aggregation in wireless sensor networks', Proc. 22nd Int. Conf. on Distributed Computing Systems Workshops, 2002, p. 575–578.
    14. 14)
    15. 15)
      • Intanagonwiwat, C., Estriu, D., Govindan, R., Heidemann, J.: `Impact of network density on Data Aggregation in wireless sensor networks', Proc. 22nd Int. Conf. on Distributed Computing Systems Workshops, 2002, p. 457–458.
    16. 16)
      • Misra, R., Mandal, C.: `Ant-aggregation: ant colony algorithm for optimal data aggregation in wireless sensor networks', Int. Conf. on Wireless and Optical Communications Networks (IFIP), 2006, p. 5.
    17. 17)
    18. 18)
      • M. Dorigo , M. Birattari , T. Stützle . Ant colony optimization – artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. , 28 - 39
    19. 19)
    20. 20)
      • S.H. Hosseini . Problem solving by intelligent water drops. IEEE Congr. Evol. Comput. (CEC) , 3226 - 3231
    21. 21)
    22. 22)
      • H. Duan , S. Liu , X. Lei . Air robot path planning based on intelligent water drops optimization. IEEE IJCNN , 1397 - 1401
    23. 23)
      • Hosseini S.H.: ‘Optimization with the nature-inspired intelligent water drops algorithm’, Wellington Pinheiro dos Santos: ‘Evolutionary Computation’, Vienna, Intech, 2009.

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