Skip to main content

Advertisement

Log in

A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network

  • Published:
Telecommunication Systems Aims and scope Submit manuscript

Abstract

A Parallel and Compact version of the Sine Cosine Algorithm (PCSCA) is proposed in this article. Parallel method can effectively improve search ability and increase the diversity of solutions. We develop three communication strategies based on parallelism idea to serve different types of optimization function to achieve the best performance. Furthermore, compact method uses statistical distribution to represent the solutions, which can save memory space and energy of the digital device. To check the optimization effect of the proposed PCSCA algorithm, it is tested on the CEC2013 benchmark function set and compared to SCA, parallel compact Cuckoo Search (PCCS) algorithms. The empirical study demonstrates that PCSCA has improved by 50.1% and 5.6%, compared to SCA and PCCS, respectively. Finally, we apply PCSCA to optimize the position accuracy of sensor node deployed in 3D actual terrain. Experimental results show that PCSCA can achieve lower localization error via Time Difference of Arrival method.

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

Similar content being viewed by others

References

  1. Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.

    Article  Google Scholar 

  2. Sayed, G. I., Darwish, A., Hassanien, A. E., & Pan, J. S. (2016). Breast cancer diagnosis approach based on meta-heuristic optimization algorithm inspired by the bubble-net hunting strategy of whales. In International conference on genetic and evolutionary computing (pp. 306–313). Cham: Springer.

  3. Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17–35.

    Article  Google Scholar 

  4. Pan, J. S., Song, P. C., Chu, S. C., & Peng, Y. J. (2020). Improved compact cuckoo search algorithm applied to location of drone logistics hub. Mathematics, 8(3), 333.

    Article  Google Scholar 

  5. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.

  6. Wang, H., Sun, H., Li, C., Rahnamayan, S., & Pan, J. S. (2013). Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences, 223, 119–135.

    Article  Google Scholar 

  7. Duan, H., & Qiao, P. (2014). Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing and Cybernetics.

  8. Tian, A. Q., Chu, S. C., Pan, J. S., Cui, H., & Zheng, W. M. (2020). A compact pigeon-inspired optimization for maximum short-term generation mode in cascade hydroelectric power station. Sustainability, 12(3), 767.

    Article  Google Scholar 

  9. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp. 1–10). Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department.

  10. TSai, P. W., Pan, J. S., Liao, B. Y., & Chu, S. C. (2009). Enhanced artificial bee colony optimization. International Journal of Innovative Computing, Information and Control, 5(12), 5081–5092.

  11. Yang, X. S. (2012). Flower pollination algorithm for global optimization. In International conference on unconventional computing and natural computation (pp. 240-249). Berlin: Springer.

  12. Zhuang, J., Luo, H., Pan, T. S., & Pan, J. S. Improved flower pollination algorithm for the capacitated vehicle routing problem.

  13. YYang, X. S., & Gandomi, A. H. (2012). Bat algorithm: A novel approach for global engineering optimization. Engineering Computations.

  14. Dao, T. K., Pan, J. S., Chu, S. C., & Shieh, C. S. (2014). Compact bat algorithm. In Intelligent data analysis and its applications (Vol. II, pp. 57-68). Cham: Springer.

  15. Parpinelli, R. S., Lopes, H. S., & Freitas, A. A. (2002). Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, 6(4), 321–332.

    Article  Google Scholar 

  16. Chu, S. C., Roddick, J. F., & Pan, J. S. (2004). Ant colony system with communication strategies. Information Sciences, 167(1–4), 63–76.

    Article  Google Scholar 

  17. Chu, S. C., Roddick, J. F., Su, C. J., & Pan, J. S. (2004). Constrained ant colony optimization for data clustering. In Pacific Rim international conference on artificial intelligence (pp. 534–543). Berlin: Springer.

  18. Mininno, E., Neri, F., Cupertino, F., & Naso, D. (2010). Compact differential evolution. IEEE Transactions on Evolutionary Computation, 15(1), 32–54.

    Article  Google Scholar 

  19. Neri, F., Mininno, E., & Iacca, G. (2013). Compact particle swarm optimization. Information Sciences, 239, 96–121.

    Article  Google Scholar 

  20. Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (2012). Global positioning system: Theory and practice. Berlin: Springer.

    Google Scholar 

  21. Wiley, W. C., & McLaren, I. H. (1955). Time-of-flight mass spectrometer with improved resolution. Review of Scientific Instruments, 26(12), 1150–1157.

    Article  Google Scholar 

  22. Liu, N., & Pan, J. S. (2019). A bi-population QUasi-Affine TRansformation evolution algorithm for global optimization and its application to dynamic deployment in wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 2019(1), 175.

    Article  Google Scholar 

  23. Chan, Y. T., Tsui, W. Y., So, H. C., & Ching, P. C. (2006). Time-of-arrival based localization under NLOS conditions. IEEE Transactions on Vehicular Technology, 55(1), 17–24.

    Article  Google Scholar 

  24. Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.

    Article  Google Scholar 

  25. Larrañaga, P., & Lozano, J. A. (Eds.). (2001). Estimation of distribution algorithms: A new tool for evolutionary computation, (Vol. 2). Berlin: Springer.

    Google Scholar 

  26. Chu, S. C. (2015). A compact artificial bee colony optimization for topology control scheme in wireless sensor networks.

  27. Pan, J. S., & Dao, T. K. (2019). A compact bat algorithm for unequal clustering in wireless sensor networks. Applied Sciences, 9(10), 1973.

    Article  Google Scholar 

  28. Liang, J. J., Qu, B. Y., Suganthan, P. N., & Hernández-Díaz, A. G. (2013). Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212(34), 281–295.

    Google Scholar 

  29. Song, P. C., Pan, J. S., & Chu, S. C. (2020). A parallel compact cuckoo search algorithm for three-dimensional path planning. Applied Soft Computing, 106443.

  30. Seljak, U., & Zaldarriaga, M. (1996). A line of sight approach to cosmic microwave background anisotropies. arXiv preprint astro-ph/9603033.

  31. Topcuoglu, H. R., Ermis, M., & Sifyan, M. (2010). Positioning and utilizing sensors on a 3-D terrain part I-Theory and modeling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(3), 376–382.

  32. Sun, C., Jin, Y., Cheng, R., Ding, J., & Zeng, J. (2017). Surrogate-assisted cooperative swarm optimization of high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation, 21(4), 644–660.

    Article  Google Scholar 

  33. Pan, J. S., Hu, P., & Chu, S. C. (2019). Novel parallel heterogeneous meta-heuristic and its communication strategies for the prediction of wind power. Processes, 7(11), 845.

    Article  Google Scholar 

  34. Pan, J. S., Kong, L., Sung, T. W., Tsai, P. W., & Snís̆el, V. . (2018). A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set. Journal of Internet Technology, 19(4), 1111–1118.

  35. Shieh, C. S., Sai, V. O., Lee, T. F., Le, Q. D., Lin, Y. C., & Nguyen, Trong-The. (2017). Node localization in WSN using heuristic optimization approaches. Journal of Network Intelligence, 2(3), 275–286.

    Google Scholar 

  36. Tang, Z., Xue, X., Wang, J., & Hang, Z. The logic sense request of WSN and its analysis model.

  37. Meng, Z., Pan, J. S., & Tseng, K. K. (2019). PaDE: An enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowledge-Based Systems, 168, 80–99.

    Article  Google Scholar 

  38. Li, N., Li, G., & Deng, Z. (2017, July). An improved sine cosine algorithm based on levy flight. In Ninth international conference on digital image processing (ICDIP 2017) (Vol. 10420, p. 104204R). International Society for Optics and Photonics.

  39. Censor, Y., & Zenios, S. A. (1997). Parallel optimization: Theory, algorithms, and applications. Oxford: Oxford University Press on Demand.

    Google Scholar 

  40. Bäck, T. (1994, October). Parallel optimization of evolutionary algorithms. In International conference on parallel problem solving from nature (pp. 418–427). Berlin: Springer.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeng-Shyang Pan.

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

Zhang, S., Fan, F., Li, W. et al. A parallel compact sine cosine algorithm for TDOA localization of wireless sensor network. Telecommun Syst 78, 213–223 (2021). https://doi.org/10.1007/s11235-021-00804-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11235-021-00804-y

Keywords

Navigation