Skip to main content
SpringerLink
Log in

New parallel swarm algorithm for smart sensor systems redundancy allocation problems in the Internet of Things

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In recent years, various smart sensor systems have been integrated into the “Internet of Things (IOT)” with the advancement of sensing technology. A redundancy allocation is the safest, most convenient, and most economical way to increase the reliability of smart sensor systems. To solve the smart sensor systems redundancy allocation problem (RAP) in IOT, a cooperative parallel simplified swarm algorithm (pSSO) is presented in this study. This pilot study includes several innovative points. First, research is conducted to use the RAP in IOT. Second, the proposed pSSO is the first parallel algorithm to solve the RAP and the first one to parallelize the simplified swarm optimization (SSO) with the Taguchi method. A simple real-life example regarding shopping and shipping in TAOBAO is given to describe the way how to model the IOT used the RAP. As proof of the success of the proposed pSSO, detailed computational results from solving a series-parallel redundancy allocation problem with a mix of components is presented. The computational results reflect the efficiency of the pSSO proposed.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Nemhauser GL, Rinnooy KAHG, Todd MJ (eds) (1989) Optimization. Handbooks in Operations Research and Management Science, vol 1. North-Holland Publishing Co., Amsterdam

  2. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Prentice-Hall, Inc, USA

    MATH  Google Scholar 

  3. Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Commun ACM 37(3):77–84

    Article  Google Scholar 

  4. Tabli EG (2002) A taxonomy of hybrid of metaheuristics. J Heuristics 8:541–544

    Article  Google Scholar 

  5. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35:268–308

    Article  Google Scholar 

  6. Blum C, Roli A (2008) Hybrid Metaheuristics: an introduction. In: Blum C, Blesa Aguilera MJ, Roli A, Sampels M (eds) Hybrid Metaheuristics. Springer, Berlin, pp 1–30

    Chapter  Google Scholar 

  7. McCulloch W, Pitts W (1943) A logical calculus of ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MathSciNet  MATH  Google Scholar 

  8. Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  MATH  Google Scholar 

  9. Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8:156–166

    Article  Google Scholar 

  10. Holland JH (1992) Adaptation in natural and artificial systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Science, Cambridge

    Google Scholar 

  11. Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. actes de la première conférence européenne sur la vie artificielle. Elsevier Publishing, Paris, France, pp 134–142

  12. Kennedy J, Eberhart R (1995) Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol IV, pp 1942–1948

  13. Larrañaga P, Lozano JA (eds) (2002) Estimation of distribution algorithms: a new tool for evolutionary computation. Kluwer Academic Publishers, Boston

    MATH  Google Scholar 

  14. Storn R (1996) On the usage of differential evolution for function optimization. Biennial Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp 519–523

  15. Karaboga DD (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department

  16. Yeh WC (2008) Study on Quickest Path Networks with Dependent Components and Apply to RAP. NSC97-2221-E-007-099-MY3 (Individual & Basic Research), Multiyear Research Project, “Distinguished Scholars Research Project” granted by National Science Council, Taiwan (2008/08/01\(\sim \)2011/07/31)

  17. Yeh WC (2009) A two-stage discrete particle swarm optimization for the problem of multiple multi-level redundancy allocation in series systems. Expert Syst Appl 36:9192–9200

    Article  Google Scholar 

  18. Yeh WC (2012) Simplified swarm optimization in disassembly sequencing problems with learning effects. comput oper res 39:2168–2177

    Article  MATH  Google Scholar 

  19. Yeh WC (2012) Novel swarm optimization for mining classification rules on thyroid gland data. Inf Sci 197:65–76

    Article  Google Scholar 

  20. Yeh WC (2013) A new parameter-free simplified swarm optimization for artificial neural network training and its application in prediction of time-series. IEEE Trans Neural Netw Learn Syst 24:661–665

    Article  Google Scholar 

  21. Yeh WC (2014) Orthogonal simplified swarm optimization for the series-parallel redundancy allocation problem with a mix of components. Knowl Based Syst 64:1–12

    Article  Google Scholar 

  22. Crainic TG, Toulouse M (2003) Parallel strategies for meta-heuristics. In: Glover F, Kochenberger G (eds) Handbook of Metaheuristics. Kluwer, Norwell, pp 475–513

    Chapter  Google Scholar 

  23. Cantú-Paz E (2011) Migration policies, selection pressure, and parallel evolutionary algorithms. J Heuristics 7:311–334

    Article  MATH  Google Scholar 

  24. Hung YF, Chen WC (2011) A heterogeneous cooperative parallel search of branch-and-bound method and tabu search algorithm. J Global Optim 51:133–148

    Article  MathSciNet  MATH  Google Scholar 

  25. Ho SJ, Ho SY, Shu LS (2004) OSA: orthogonal simulated annealing algorithm and Its application to designing mixed H2/H\(\infty \) optimal controllers. IEEE Trans Syst Man Cybern Part A 34:588–600

    Article  Google Scholar 

  26. Fyffe DE, Hines WW, Lee NK (1968) System reliability allocation and a computation algorithm. IEEE Trans Reliab R-17:64–69

  27. Chern MS (1992) On the computational complexity of reliability redundancy allocation in a series system. Oper Res Lett 11:309–315

    Article  MathSciNet  MATH  Google Scholar 

  28. Liang YC, Wu CC (2005) A variable neighborhood descent algorithm for the redundancy allocation problem. Ind Eng Manage Syst 4:109–16

    Google Scholar 

  29. Hsieh YC (2002) A linear approximation for redundant reliability problems with multiple component choices. Comput Ind Eng 44:91–103

    Article  Google Scholar 

  30. Coit DWC, Smith AE (1996) Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach. Comput Oper Res 23:515–526

    Article  MATH  Google Scholar 

  31. Liang YH, Smith AE (2004) An ant colony optimization algorithm for the redundancy allocation problem (RAP). IEEE Trans Reliab 53(3):417–423

    Article  Google Scholar 

  32. Kulturel-Konak S, Smith A, Coit D (2003) Efficiently solving the redundancy allocation problem using Tabu search. IIE Trans 35(6):515–526

    Article  Google Scholar 

  33. Liang YC, Chen YC (2007) Redundancy allocation of series-parallel systems using a variable neighborhood search algorithm. Reliab Eng Syst Saf 92:323–331

    Article  Google Scholar 

  34. Onishi J, Kimura S, James RJW, Nakagawa Y (2007) Solving the redundancy allocation problem with a mix of components using the improved surrogate constraint method. IEEE Trans Reliab 56(1):94–101

    Article  Google Scholar 

  35. Yeh WC, Hsieh TJ (2011) Solving reliability redundancy allocation problems using an artificial bee colony algorithm. Comput Oper Res 38(1):1465–1473

Download references

Acknowledgments

I wish to thank the anonymous editor and the reviewers for their constructive comments and recommendations, which have significantly improved the presentation of this paper. This research was supported in part by the National Science Council of Taiwan, R.O.C. under Grant NSC101-2221- E-007-079- MY3 and NSC 102-2221-E-007-086-MY3.

Author information

Authors and Affiliations

  1. Integration and Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Wei-Chang Yeh

  2. Police Information and Scientific Decision-Making Laboratory, Department of Information Management, Central Police University, Taoyuan, Taiwan

    Jsen-Shung Lin

Authors
  1. Wei-Chang Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jsen-Shung Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei-Chang Yeh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yeh, WC., Lin, JS. New parallel swarm algorithm for smart sensor systems redundancy allocation problems in the Internet of Things. J Supercomput 74, 4358–4384 (2018). https://doi.org/10.1007/s11227-016-1903-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-016-1903-8

Keywords

Search

Navigation