Advertisement

Energy-Efficient Modified Bellman Ford Algorithm for Grid and Random Network Topologies

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)

Abstract

Energy-efficient routing techniques are required for mobile ad hoc Networks (MANETs) to improve the lifetime of the network. The lifetime of the network depends on the battery capacity of nodes. The link failure due to the battery discharge of node can be avoided by considering the nodes having good residual energy (RE) with less change in their battery capacity. In this paper, the Bellman–Ford algorithm (BFA) is considered to find the shortest path for routing. Bellman–Ford algorithm is modified and the nodes whose change in battery capacity is less than a predefined threshold value are considered for routing to avoid the link failures and to enhance the lifetime of the network. In the proposed modified Bellman–Ford algorithm (MBFA), residual energy (RE) is considered as a metric to find the shortest path. IEEE 802.11 a/g standards using orthogonal frequency division multiplexing (OFDM) are considered for simulation. Energy consumed by the radio transceiver, processor, losses in the battery, and DC–DC converter are taken into consideration for energy calculation. The performance of BFA and MBFA for the grid and random network topologies is simulated by considering the network with multiple sources and destinations are compared with and without mobility by assuming various densities, i.e., 15, 30, 45, and 60. The mobility of the node increases the loss of orthogonality among the OFDM subcarriers and results inter carrier interference (ICI). The effect of mobility and network size on throughput, delay, jitters for the grid, and random network topologies using BFA and MBFA are compared. Simulation results show that the performance of proposed MBFA is better compared to BFA.

Keywords

Bellman ford algorithm Inter carrier interference Modified bellman ford algorithm OFDM Residual energy 

References

  1. 1.
    Singh, S., Woo, M., Raghavendra, C.S.: Power-aware routing in mobile ad hoc networks. In: Proceedings of MobiCom ’98, Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Dallas, pp. 181–190 (1998)Google Scholar
  2. 2.
    Toh, C.K.: Maximum battery life routing to support ubiquitous mobile computing in wireless ad-hoc networks. IEEE Commun. Mag. 39(6), 138–147 (2001). doi: 10.1109/35.925682 CrossRefGoogle Scholar
  3. 3.
    Chang, J-H., Tassiulas L.: Maximum lifetime routing in wireless sensor networks. IEEE/ACM Trans. Netw. 1(4), 609–619 (2004)Google Scholar
  4. 4.
    Tekbiyik, N., Uysal-Biyikoglu, E.: Energy efficient wireless unicast routing alternatives for machine-to-machine networks. J. Netw. Comput. Appl. 34(5), 1587–1614 (2011). doi: 10.1016/j.jnca.2011.02.005 CrossRefGoogle Scholar
  5. 5.
    Dehghan, M., Ghaderi, M., Goeckel, D.: Minimum-energy cooperative routing in wireless networks with channel variations. IEEE Trans. Wireless Commun. 10(11), 3813–3823 (2011)Google Scholar
  6. 6.
    Shivashankar, Suresh, H.N.: Design of novel protocol to increase network lifetime in MANETs. Int. J. Adv. Res. Comput. Commun. Eng. 2(10), 3974–3978 (2013)Google Scholar
  7. 7.
    Sergiou, C., Vassiliou, V.: Energy utilization of HTAP under specific node placements in wireless sensor networks. In: IEEE Wireless Conference (EW), pp. 482–487 (2010)Google Scholar
  8. 8.
    Rama Devi, B., Asha Rani, M., Kishan Rao, K.: Performance of static networks for power saving mode. Int. J. Adv. Eng. Global Technol. 2(6), 783–789 (2014)Google Scholar
  9. 9.
    Shakywar, H., Sharma, S., Sahu, S.: Performance analysis of DYMO, LANMAR, STAR routing protocols for grid placement model with varying network size. IJCTA 2(6) 1755–1760 (2011)Google Scholar
  10. 10.
    Rama Devi, B., Asha Rani, M., Kishan Rao, K.: Energy efficient cooperative node selection for OFDM systems based on SNR estimation. Int. J. Adv. Comput. Electr. Electron. Eng. 3(1), 32–36 (2014)Google Scholar
  11. 11.
    Bechkit, W., Challal, Y., Bouabdallah, A., Koudil, M., Souici, B., Benatchba, K.: A new weighted shortest path tree for converge cast traffic routing in WSN. In: IEEE Symposium on Computers and Communications (ISCC), pp. 187–192 (2012)Google Scholar
  12. 12.
    QualNet 6.1 Wireless Model Library.: Scalable Network Technologies, September 2012Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Rama Devi Boddu
    • 1
  • K.  Kishan Rao
    • 2
  • M.  Asha Rani
    • 3
  1. 1.Department of E.C.EKakatiya Institute of Technology and ScienceWarangalIndia
  2. 2.Department of E.C.EVaagdevi College of EngineeringWarangalIndia
  3. 3.Department of E.C.EJawaharlal Nehru Technological UniversityHyderabadIndia

Personalised recommendations