DFRTP: Dynamic 3D Fuzzy Routing Based on Traffic Probability in Wireless Sensor Networks

DFRTP: Dynamic 3D Fuzzy Routing Based on Traffic Probability in Wireless Sensor Networks

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

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(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.

Routing protocols are used in wireless sensor network (WSN) to transmit data to a centre (e.g. a base station). In this study, the authors propose a routing protocol called dynamic three-dimensional fuzzy routing based on traffic probability to enhance network lifetime and increase packet delivery ratio. It uses a fuzzy-based procedure to transmit packets by hop-to-hop delivery from source nodes toward destination nodes. The proposed fuzzy system uses two input parameters including ‘distance’ and ‘number of neighbours’ and one output parameter denoted by ‘traffic probability’. When a node has a sensed data or buffered data packet, it selects one of its neighbours, called chosen node, from among a list of candidate nodes (CNs). Candidates are the neighbours which have power energy higher than average remaining energy and free buffer more than average available buffer size. Distance and number of neighbours for each CN are fed in the fuzzy system to calculate traffic probability. The CN having the lowest traffic probability is selected as an appropriate chosen node to transmit packets to the destination. Simulation results show that the proposed protocol surpasses the greedy and A* heuristic routing for wireless sensor networks in home automation, dynamic optimal progress routing, and A-star & Fuzzy methods in terms of network lifetime and packet delivery ratio.


    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 5. Burri, N., Rickenbach, P.V., Wattenhofer, R.: ‘Dozer: ultra-low power data gathering in sensor networks’. Proc. of Lecture Notes in Computer Science, April 2007, pp. 450459, ISBN 978-1-59593-638-7.
    6. 6)
      • 6. Yahya, B., Ben-othman, J.: ‘REER: robust and energy efficient multipath routing protocol for wireless sensor networks’. Proc. of Global Telecommunications Conf., December 2009, pp. 17, ISBN 978-1-4244-4148-8.
    7. 7)
    8. 8)
    9. 9)
      • 9. Gilg, M., Yousef, Y., Lorenz, P.: ‘Using image processing algorithms for energy efficient routing algorithm in sensor networks’. Proc. of Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, November 2009, pp. 132136, ISBN 978-0-7695-3862-4.
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 13. Okazaki, A.M., Frohlich, A.A.: ‘AD-ZRP: ant-based routing algorithm for dynamic wireless sensor networks’. Proc. of IEEE Telecommunications (ICT), May 2011, pp. 1520, ISBN 978-1-4577-0025-5.
    14. 14)
      • 14. Okazaki, A.M., Frohlich, A.A.: ‘Ant-based dynamic hop optimization protocol: a routing algorithm for mobile wireless sensor networks’. Proc. of IEEE GLOBECOM Workshops, December 2011, ISBN 978-1-4673-0039-1.
    15. 15)
      • 15. Kalantary, M., Meybodi, M.R.: ‘Energy-aware routing protocol for mobile sensor networks using learning automata algorithms’. Proc. of IEEE Wireless and Mobile Computing, Networking and Communications (WiMob), October 2010, pp. 492496, ISBN 978-1-4244-7743-2.
    16. 16)
      • 16. Gouvy, N., Hamouda, E., Mitton, N., et al: ‘Energy efficient multi-flow routing in mobile sensor networks’. Proc. of IEEE WCNC, April 2013, pp. 19681973, ISBN 978-1-4673-5938-2.
    17. 17)
      • 17. Falcon, R., Hai, L., Nayak, A., et al: ‘Controlled straight mobility and energy-aware routing in robotic wireless sensor networks’. Proc. of IEEE Distributed Computing in Sensor Systems (DCOSS), May 2012, pp. 150157, ISBN 978-1-4673-1693-4.
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 22. Shimokawara, E., Kaneko, T., Yamaguchi, T., et al: ‘Estimation of basic activities of daily living using zigbee 3d accelerometer sensor network’. Proc. of IEEE Int. Conf. Biometrics and Kansei Engineering (ICBAKE), July 2013, pp. 251256, doi 10.1109/ICBAKE.2013.36.
    23. 23)
    24. 24)
      • 24. Cococcioni, M., Foschini, L., Lazzerini, B., et al: ‘Complexity reduction of Mamdani fuzzy systems through multi-valued logic minimization’. Proc. of IEEE Systems Man and Cybernetics, October 2008, pp. 17821787, ISBN 978-1-4244-2384-2.
    25. 25)
    26. 26)

Related content

This is a required field
Please enter a valid email address