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

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

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

Buy article PDF
£12.50
(plus tax if applicable)
Add to cart
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)
Add to cart

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
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
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.

© The Institution of Engineering and Technology
Published

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.

Inspec keywords: probability; telecommunication network routing; trees (mathematics); wireless sensor networks; telecommunication network reliability; ant colony optimisation

Other keywords: WSN; nature-inspired optimisation methods; intelligent water drops algorithm; optimal data aggregation tree; ant colony optimisation; wireless sensor networks; sensor nodes; IWD algorithm; optimisation algorithm; network lifetime; data routing; optimally selected aggregation nodes; direct communication; conventional methods; energy conservation; well-known optimisation method; computational experiment; probability; energy-efficient data transmission

Subjects: Communication network design, planning and routing; Optimisation techniques; Reliability; Other topics in statistics; Sensing devices and transducers; Combinatorial mathematics; Wireless sensor networks

References

    1. 1)
      • 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.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 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.
    6. 6)
      • S.H. Hosseini . Problem solving by intelligent water drops. IEEE Congr. Evol. Comput. (CEC) , 3226 - 3231
    7. 7)
    8. 8)
    9. 9)
      • C.E. Perkins , E.M. Belding-Royer , S.R. Das . Ad hoc on-demand distance vector (AODV) routing.
    10. 10)
    11. 11)
      • 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.
    12. 12)
      • M. Dorigo , M. Birattari , T. Stützle . Ant colony optimization – artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. , 28 - 39
    13. 13)
    14. 14)
      • 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.
    15. 15)
      • H. Duan , S. Liu , X. Lei . Air robot path planning based on intelligent water drops optimization. IEEE IJCNN , 1397 - 1401
    16. 16)
    17. 17)
    18. 18)
    19. 19)
      • 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.
    20. 20)
      • 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.
    21. 21)
    22. 22)
      • D.B. Johnson , D.A. Maltz , Y.C. Hu . (2007) The dynamic source routing protocol for mobile ad hoc networks (DSR).
    23. 23)
      • Hosseini S.H.: ‘Optimization with the nature-inspired intelligent water drops algorithm’, Wellington Pinheiro dos Santos: ‘Evolutionary Computation’, Vienna, Intech, 2009.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-wss.2011.0146
Loading

Related content

content/journals/10.1049/iet-wss.2011.0146
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address