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Computation of Mesh Node Placements Using DE Approach to Minimize Deployment Cost with Maximum Connectivity

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Abstract

A node placement problem is formulated to ensure maximum connectivity and minimum deployment cost using differential evolution based node placement with traffic weight algorithm. A simulation study is performed to evaluate the performance of the network under four different client distribution patterns (Normal, Uniform, Exponential and Weibull). A maximum throughput of 95.3% and 96.2% of throughput is achieved in normal and weibull distributions than the conventional placement. It is observed from the results that the two distributions have good impact on network performance with minimum deployment cost and maximum connectivity. The packet delivery rate shows a percentage increase of 36.6% compared to the SA based placement scheme in normal distribution. It is also observed that a percentage increase of 28.9% of improvement is achieved when clients are distributed with weibull distribution and minimum end to end delay.

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References

  1. Denzinger, J., & Kidney, J. (2006). Evaluating different genetic operators in the testing for unwanted emergent behavior using evolutionary learning of behavior. In IEEE/WIC/ACM international conference on intelligent agent technology, pp. 23–29.

  2. Barolli, Fatos Xhafa Admir, Sánchez, Christian, & Barolli, Leonard. (2011). A simulated annealing algorithm for router nodes placement problem in wireless mesh. Networks Simulation Modelling Practice and Theory, 19, 2276–2284.

    Article  Google Scholar 

  3. Xhafa, F., Sánchez, C., Barolli, A., & Takizawa, M. (2015). Solving mesh router nodes placement problem in wireless mesh networks by tabu search algorithm. Journal of Computer and System Sciences, 81, 1417–1428.

    Article  MathSciNet  MATH  Google Scholar 

  4. Garey, M. R., & Johnson, D. S. (1979). Computers and Intractability—a guide to the theory of NP-completeness. San Francisco: Freeman.

    MATH  Google Scholar 

  5. Ilonen, J., Kamarainen, J. K., & Lampinen, J. (2003). Differential evolution training algorithm for feed-forward neural networks. Neural Processing Letters, 17(1), 93–105.

    Article  Google Scholar 

  6. Lim, A., Rodrigues, B., Wang, F., & Xua, Zh. (2005). k-Center problems with minimum coverage. Theoretical Computer Science, 332, 1–17.

    Article  MathSciNet  MATH  Google Scholar 

  7. Zhou, P., Wang, X., Manoj, B. S., & Rao R. (2010). On optimizing gateway placement for throughput in wireless mesh networks. EURASIP Journal on Wireless Communications and Networking, vol. 1, pp. 1–12.

  8. Price, K., Storn, R., & Lampinen, J. (2005). Differential evolution—A practical approach to global optimization. Berlin: Springer.

    MATH  Google Scholar 

  9. Oda, T., Barolli, A., Spaho, E., Barolli, L., Xhafa, F.& Iwashige, J. (2012). Node Placement in WMNs and visualization of evolutionary computation process using WMN-GA system. In 15th international conference on network-based information systems, (pp. 214–220).

  10. Tu, W. (2014). A multi-rate multi-channel multicast algorithm in wireless mesh networks. In 39th annual IEEE conference on local computer networks, (pp. 55–63).

  11. Xu, X., Tang, S., Mao, X., & Li, X. Y. (2010). distributed gateway placement for cost minimization in wireless mesh networks. In IEEE international conference on distributed computing systems, (pp. 507–515).

  12. Sheeba, G. M., Nachiappan, A., & Gokulnath, P. S. L. (2012). Improving link quality using OSPF routing protocol in a stable Wi-Fi mesh network. In communications and signal processing (ICCSP), 2012 international conference on (pp. 23–26). IEEE.

  13. Sheeba, G. M., & Nachiappan, A. (2013). An interworking implementation and performance evaluation in IEEE 802.11 s based campus mesh networks. Indian Journal Of Computer Science And Engineering, 4(1), 29–33.

    Google Scholar 

  14. Sheeba, G. M., & Nachiappan, A. (2015). Fuzzy differential evolution based gateway placements in WMN for cost optimization. Intelligent Systems Technologies and Applications, 385, 137–145.

    Article  Google Scholar 

  15. Sheeba, G. M., Nachiappan, A., & Kumar, P. H. (2015). placement of energy aware wireless mesh nodes for e-learning in green campuses. arXiv preprint. arXiv:1505.04713.

  16. Sheeba, G. M., & Nachiappan, A. (2015). Gateway placements in wmn with cost minimization and optimization using sa and de techniques. International Journal Of Pharmacy & Technology, 7(1), 8274–8281.

    Google Scholar 

  17. Sheeba, G. M., & Nachiappan, A. (2018). Performance evaluation of fuzzy DE based node placement in WMN. Journal of Engineering Research, 5(4), 106–120.

    Google Scholar 

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Authors and Affiliations

  1. Department of ETCE, Sathyabama University, Chennai, India

    G. Merlin Sheeba

  2. Department of EEE, Pondicherry Engineering College, Pondicherry, India

    Alamelu Nachiappan

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  1. G. Merlin Sheeba
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  2. Alamelu Nachiappan
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Correspondence to G. Merlin Sheeba.

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Merlin Sheeba, G., Nachiappan, A. Computation of Mesh Node Placements Using DE Approach to Minimize Deployment Cost with Maximum Connectivity. Wireless Pers Commun 107, 291–302 (2019). https://doi.org/10.1007/s11277-019-06255-8

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  • DOI: https://doi.org/10.1007/s11277-019-06255-8

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