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Optimized Relay Nodes Positioning to Achieve Full Connectivity in Wireless Sensor Networks

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Abstract

Wireless sensor networks (WSNs) located in certain environments may get segregated into disjoint segments due to breakdown of one or more sensor nodes. This results in the failure of the entire network. It is necessary to restore the connectivity among the disjoint segments to ensure the accuracy of the data collected by the sink node. Relay nodes (RNs) aid in improving network life time and reducing the data latency. We can ensure full connectivity of the network by positioning relay nodes at certain locations in the deployment area. Therefore optimal relay nodes locations need to be identified to design a fully connected WSN. In this paper, we use moth flame optimizer (MFO) algorithm, interior search algorithm (ISA) and bat algorithm (BA) to identify the optimal positions for the placement of RNs. The proposed work uses the heuristic fully connected network to check the connectivity of the network. Extensive simulations have been carried out and the results show the superiority of MFO compared to BA, ISA and minimum spanning tree based M1tRNP approach.

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References

  1. Lloyd, E. L., & Xue, G. (2007). Relay node placement in wireless sensor networks. IEEE Transactions on Computers, 56(1), 134–138.

    Article  MathSciNet  Google Scholar 

  2. Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), 28–39.

    Article  Google Scholar 

  3. Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845.

    Article  MathSciNet  MATH  Google Scholar 

  4. Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2009). Gsa: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248.

    Article  MATH  Google Scholar 

  5. Shah-Hosseini, H. (2012). An approach to continuous optimization by the intelligent water drops algorithm. Procedia-Social and Behavioral Sciences, 32, 224–229.

    Article  Google Scholar 

  6. Islam, M. M., Shareef, H., Mohamed, A., & Wahyudie, A. (2016). A binary variant of lightning search algorithm: Blsa. Soft Computing, 21(11), 1–20.

    Google Scholar 

  7. Kashan, A. H. (2014). League championship algorithm (lca): An algorithm for global optimization inspired by sport championships. Applied Soft Computing, 16, 171–200.

    Article  Google Scholar 

  8. Wang, G. G., Gandomi, A. H., Alavi, A. H., & Deb, S. (2016). A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Computing and Applications, 27(4), 989–1006.

    Article  Google Scholar 

  9. Wang, G. G., Hossein Gandomi, A., & Hossein Alavi, A. (2013). A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes, 42(6), 962–978.

    Article  MathSciNet  MATH  Google Scholar 

  10. Mirjalili, S., Jangir, P., & Saremi, S. (2016). Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 1–17.

    Google Scholar 

  11. Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119.

    Article  Google Scholar 

  12. Wang, G. G., Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2016). A new hybrid method based on krill herd and cuckoo search for global optimisation tasks. International Journal of Bio-Inspired Computation, 8(5), 286–299.

    Article  Google Scholar 

  13. Wang, G. G., Gandomi, A. H., Alavi, A. H., & Hao, G. S. (2014). Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Computing and Applications, 25(2), 297–308.

    Article  Google Scholar 

  14. Yang, X. S., & Hossein Gandomi, A. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations, 29(5), 464–483.

    Article  Google Scholar 

  15. Gandomi, A. H. (2014). Interior search algorithm (isa): A novel approach for global optimization. ISA Transactions, 53(4), 1168–1183.

    Article  Google Scholar 

  16. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.

    Article  Google Scholar 

  17. Cheng, X., Du, D. Z., Wang, L., & Baogang, Xu. (2008). Relay sensor placement in wireless sensor networks. Wireless Networks, 14(3), 347–355.

    Article  Google Scholar 

  18. Chen, S. H., Lee, C. H., Chen, T. Y., Wei, H. W., Hsu, T. S., & Shih, W. K. (2017). Analysis and improvement of the 3-star algorithm for the stp-msp problem in wireless sensor networks. In 2017 international conference on computing, networking and communications (ICNC), pp. 991–995.

  19. Liu, H., Wan, P., & Jia, X. (2006). On optimal placement of relay nodes for reliable connectivity in wireless sensor networks. Journal of Combinatorial Optimization, 11(2), 249–260.

    Article  MathSciNet  MATH  Google Scholar 

  20. Bredin, J. L., Demaine, E. D., Hajiaghayi, M. T., & Rus, D. (2005). Deploying sensor networks with guaranteed capacity and fault tolerance. In Proceedings of the 6th ACM international symposium on mobile ad hoc networking and computing (pp. 309–319). ACM.

  21. Xu, K., Wang, Q., Hassanein, H., & Takahara, G. (2005). Optimal wireless sensor networks (wsns) deployment: Minimum cost with lifetime constraint. In WiMob’2005, IEEE international conference on wireless and mobile computing, networking and communications, 2005 (Vol. 3, pp. 454–461). IEEE.

  22. Wang, Q., Xu, K., Hassanein, H., & Takahara, G. (2005). Minimum cost guaranteed lifetime design for heterogeneous wireless sensor networks (wsns). In PCCC 2005. 24th IEEE international performance, computing, and communications conference, 2005 (pp. 599–604). IEEE.

  23. Wang, Q., Takahara, G., Hassanein, H., & Xu, K. (2005). On relay node placement and locally optimal traffic allocation in heterogeneous wireless sensor networks. In The IEEE conference on local computer networks 30th anniversary (LCN’05) l (p. 8). IEEE.

  24. Lee, S., & Younis, M. (2012). Optimized relay node placement for connecting disjoint wireless sensor networks. Computer Networks, 56(12), 2788–2804.

    Article  Google Scholar 

  25. Lee, S., & Younis, M. (2011). Eqar: Effective qos-aware relay node placement algorithm for connecting disjoint wireless sensor subnetworks. IEEE Transactions on Computers, 60(12), 1772–1787.

    Article  MathSciNet  MATH  Google Scholar 

  26. Wang, X., Xu, L., & Zhou, S. (2015). Restoration strategy based on optimal relay node placement in wireless sensor networks. International Journal of Distributed Sensor Networks, 11(7), 409085.

    Article  Google Scholar 

  27. Ibrahim, A. S., Seddik, K. G., & Liu, K. J. R. (2009). Connectivity-aware network maintenance and repair via relays deployment. IEEE Transactions on Wireless Communications, 8(1), 356–366.

    Article  Google Scholar 

  28. Cheng, M. X., Ling, Y., & Sadler, B. M. (2017). Network connectivity assessment and improvement through relay node deployment. Theoretical Computer Science, 660, 86–101.

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhao, C., & Chen, P. (2007). Particle swarm optimization for optimal deployment of relay nodes in hybrid sensor networks. In 2007 IEEE congress on evolutionary computation, pp. 3316–3320.

  30. Hashim, H. A., Ayinde, B. O., & Abido, M. A. (2016). Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm. Journal of Network and Computer Applications, 64, 239–248.

    Article  Google Scholar 

  31. Magán-Carrión, R., Rodríguez-Gómez, R. A., Camacho, J., & García-Teodoro, P. (2016). Optimal relay placement in multi-hop wireless networks. Ad Hoc Networks, 46, 23–36.

    Article  Google Scholar 

  32. Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3–18.

    Article  Google Scholar 

  33. Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32(200), 675–701.

    Article  MATH  Google Scholar 

  34. Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1), 86–92.

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Saunhita Sapre.

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Sapre, S., Mini, S. Optimized Relay Nodes Positioning to Achieve Full Connectivity in Wireless Sensor Networks. Wireless Pers Commun 99, 1521–1540 (2018). https://doi.org/10.1007/s11277-018-5290-8

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