Skip to main content
Log in

Extended OLSR and AODV based on multi-criteria decision making method

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

Recently, multi-metric routing protocols have been proposed to enhance the performance of the typical single metric routing protocols in mobile ad-hoc networks. Generally, most of them employ simple value computation to derive a cost value as a way by combining multiple metrics. However, this simple approach fails to define the relationship or priorities between multiple metrics. To overcome this limitation, in this study, we apply the multi-criteria decision making (MCDM) method to determine the weight factors between the metrics. We define criteria to accommodate multiple metric in logical way and decide a better path. For the case studies, we extend the existing proactive and reactive routing protocols, that is, ad-hoc on-demand distance vector (AODV) and optimized link state routing protocol (OLSR). In AODV, we present a strategy for modifying the route request and route reply mechanism to generate a stable path using the MCDM. On the other hand, in OLSR, we propose a modification strategy of MPR selection algorithm to maintain a stable topology using the MCDM. The simulation results show that proposed routing scheme reduces the routing overhead by 15% and 13%, packet loss rate by 12% and 14%, and end-to-end delay by 21% and 19% approximately, compared with other routing schemes such as fixed weighted AODV, OLSR and MCDM based geographical routing protocol.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Clausen, T., & Jacquet, P. (2003). Optimized link state routing protocol (OLSR). Technical report.

  2. Perkins, C., Belding-Royer, E., & Das, S. (2003). Ad hoc on-demand distance vector (AODV) routing. Technical report.

  3. Patil, A. P., Sharanya, B., & Kumar, M. D. (2013). Design and implementation of combined energy metricaodv (cem\_aodv) routing protocol for manets. International Journal of Computer and Electrical Engineering, 5(1), 9.

    Article  Google Scholar 

  4. Khimsara, S., Kambhatla, KK., & Hwang, J. (2009). AM-AOMDV: Adaptive multi-metric ad-hoc on-demand multipath distance vector routing. In Proceedings of international conference on ad hoc networks (pp. 884–895). Springer.

  5. Moad, D., Djahel, S., & Naït-Abdesselam, F. (2012). Improving the quality of service routing in OLSR protocol. In Proceedings of communications and information technology (pp. 314–319). IEEE.

  6. Mohsin, A. H., Bakar, K. A., & Zainal, A. (2018). Optimal control overhead based multi-metric routing for manet. International Journal of Wireless Networks, 24(6), 2319–2335.

    Article  Google Scholar 

  7. Khatoon, N. (2019). A node stability based multi-metric weighted clustering algorithm for mobile ad hoc networks. In Proceedings of microelectronics, computing & communication systems (pp. 63–77). Springer.

  8. Li, W., Jia, B., & Li, Q. (2018). An energy efficient and lifetime aware routing protocol in ad hoc networks. In Proceedings of algorithms and architectures for parallel processing (pp. 378–387). Springer.

  9. Taha, A., Alsaqour, R., & Uddin, M. (2017). Energy efficient multipath routing protocol for mobile ad-hoc network using the fitness function. IEEE Access, 5, 10369–10381.

    Article  Google Scholar 

  10. Paraskevas, E., Manousakis, K., & Das, S. (2014). Multi-metric energy efficient routing in mobile ad-hoc networks. In Proceedings of military communications conference (pp. 1146–1151). IEEE.

  11. Abdelkabir, J., & Habbani, A. (2017). Multi-metric performance for olsr routing protocol in mobile ad-hoc networks. International Journal of Wireless and Mobile Networks, 9(3), 39–49.

    Article  Google Scholar 

  12. Kacem, I., Sait, B., & Mekhilef, S. (2018). A new routing approach for mobile ad hoc systems based on fuzzy petri nets and ant system. IEEE Access, 6, 65705–65720.

    Article  Google Scholar 

  13. Anuradha, M., & Mala, G. A. (2017). Cross-layer based congestion detection and routing protocol using fuzzy logic for manet. International Journal of Wireless Networks, 23(5), 1373–1385.

    Article  Google Scholar 

  14. Jinarajadasa, G., Rupasinghe, L., & Murray, L. (2018). A reinforcement learning approach to enhance the trust level of manets. In Proceedings of national information technology conference (pp. 1–7). IEEE.

  15. Tilwari, V., Dimyati, K., Hindia, M., Fattouh, A., & Amiri Sadegh, I. (2019). Mobility, residual energy, and link quality aware multipath routing in MANETs with Q-learning algorithm. Applied Sciences, 9(8), 1582.

    Article  Google Scholar 

  16. Kumari, N. D., & Shylaja, B. (2017). AMGRP: AHP-based multimetric geographical routing protocol for urban environment of vanets. Journal of King Saud University—Computer and Information Sciences, 31(1), 72–81.

    Article  Google Scholar 

  17. Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26.

    Article  Google Scholar 

  18. Afshari, A., Mojahed, M., & Yusuff, R. M. (2010). Simple additive weighting approach to personnel selection problem. International Journal of Innovation, Management and Technology, 1(5), 511.

    Google Scholar 

  19. Klir, G., & Yuan, B. (1995). Fuzzy sets and fuzzy logic (Vol. 4). Englewood Cliffs: Prentice Hall.

    Google Scholar 

  20. Hernandez-Cons, N., Kasahara, S., & Takahashi, Y. (2010). Dynamic Hello/Timeout timer adjustment in routing protocols for reducing overhead in MANETs. International Journal of Computer Communications, 33(15), 1864–1878.

    Article  Google Scholar 

  21. Benkic, K., Malajner, M., & Planinsic, P. (2008). Using RSSI value for distance estimation in wireless sensor networks based on zigbee. In Proceedings of systems, signals and image processing (pp. 303–306). IEEE.

Download references

Acknowledgements

This work has been supported by the Small-scale Mobile Adhoc Network with Bio-networking Technology Project of Agency for Defense Development(UD170097ED).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ki-Il Kim.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, BS., Roh, B., Ham, JH. et al. Extended OLSR and AODV based on multi-criteria decision making method. Telecommun Syst 73, 241–257 (2020). https://doi.org/10.1007/s11235-019-00609-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-019-00609-0

Keywords

Navigation