Skip to main content

A Coverage Construction Method Based Hill Climbing Approach for Mesh Router Placement Optimization

  • Conference paper
  • First Online:
Advances on Broad-Band Wireless Computing, Communication and Applications (BWCCA 2020)

Abstract

Wireless mesh networks (WMNs) are one of the wireless network technologies that have received much attention in recent years, and as the name implies, routers can provide a stable network over a wide area by configuring the network like a mesh. In order to provide a lower cost and more stable network, various methods for optimizing the placement of mesh routers are being studied. In a previous work, we proposed a Coverage Construction Method (CCM) for this mesh router placement problem. In this paper, we propose a CCM based Hill Climbing (HC) for mesh router placement optimization problem.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Akyildiz, I.F., et al.: Wireless mesh networks: a survey. Comput. Netw. 47(4), 445–487 (2005)

    Article  Google Scholar 

  2. Jun, J., et al.: The nominal capacity of wireless mesh networks. IEEE Wireless Commun. 10(5), 8–15 (2003)

    Article  Google Scholar 

  3. Oyman, O., et al.: Multihop relaying for broadband wireless mesh networks: from theory to practice. IEEE Commun. Mag. 45(11), 116–122 (2007)

    Article  Google Scholar 

  4. Oda, T., et al.: WMN-GA: a simulation system for WMNs and its evaluation considering selection operators. J. Ambient Intell. Humanized Comput. 4(3), 323–330 (2013)

    Article  Google Scholar 

  5. Ikeda, M., et al.: Analysis of WMN-GA simulation results: WMN performance considering stationary and mobile scenarios. In: Proc. of The 28-th IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA-2014), pp. 337–342. IEEE (2014)

    Google Scholar 

  6. Oda, T., et al.: Analysis of mesh router placement in wireless mesh networks using Friedman test considering different meta-heuristics. Int. J. Commun. Netw. Distrib. Syst. 15(1), 84–106 (2015)

    Google Scholar 

  7. Oda, T., et al.: A genetic algorithm-based system for wireless mesh networks: analysis of system data considering different routing protocols and architectures. Soft Comput. 20(7), 2627–2640 (2016)

    Article  Google Scholar 

  8. Sakamoto, S., et al.: Performance evaluation of intelligent hybrid systems for node placement in wireless mesh networks: a comparison study of WMN-PSOHC and WMN-PSOSA. In: Proc. of The 11-th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2017), pp. 16–26 (2017)

    Google Scholar 

  9. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  10. Skalak, D.B.: Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: Proc. of The 11-th International Conference on Machine Learning (ICML-1994), pp. 293–301 (1994)

    Google Scholar 

  11. Kirkpatrick, S., et al.: Optimization by simulated annealing. Sci. 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  12. Glover, F.: Tabu search: a tutorial. Interfaces 20(4), 74–94 (1990)

    Article  Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proc. of The IEEE International Conference on Neural Networks (ICNN-1995), pp. 1942–1948 IEEE (1995)

    Google Scholar 

  14. Hirata, A., et al.: Approach of a solution construction method for mesh router placement optimization problem. In: Proc. of The IEEE 9-th Global Conference on Consumer Electronics (IEEE GCCE-2020), accepted to appear, pp. 1–2. IEEE (2020)

    Google Scholar 

  15. Tarjan, R.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146–160 (1972)

    Article  MathSciNet  Google Scholar 

  16. Lu, K., et al.: The depth-first optimal strategy path generation algorithm for passengers in a metro network. Sustain. 12(13), 1–16 (2020)

    Article  Google Scholar 

  17. Xhafa, F., et al.: Solving mesh router nodes placement problem in Wireless Mesh Networks by Tabu Search algorithm. J. Comput. Syst. Sci. 81(8), 1417–1428 (2015)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

This work was supported by JSPS KAKENHI Grant Number 20K19793.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aoto Hirata .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hirata, A., Oda, T., Saito, N., Hirota, M., Katayama, K. (2021). A Coverage Construction Method Based Hill Climbing Approach for Mesh Router Placement Optimization. In: Barolli, L., Takizawa, M., Enokido, T., Chen, HC., Matsuo, K. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2020. Lecture Notes in Networks and Systems, vol 159. Springer, Cham. https://doi.org/10.1007/978-3-030-61108-8_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61108-8_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61107-1

  • Online ISBN: 978-3-030-61108-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics