Skip to main content
Log in

Distributed Approximation Algorithms for Spectrum Allocation in Wireless ad Hoc Networks

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Spectrum allocation is a difficult and hot issue in wireless ad hoc networks. An efficient method of spectrum allocation is a key factor to improve quality of service and performance of wireless networks. In this paper, we consider the spectrum allocation problem which asks how to allocate the least number of spectrum blocks in a field to ensure the service on any random k locations simultaneously. Our solution to the spectrum allocation problem is the minimum k-Roman dominating set. We propose two distributed algorithms for the issue of spectrum allocation in wireless ad hoc networks. One is a distributed 6k-approximation algorithm for the spectrum allocation of satisfying any random k (k ≥ 2) locations in the class of unit ball graphs. The other one is a better distributed algorithm for finding a (1 + ε)-approximation for the spectrum allocation problem of serving any random two locations, in the class of growth-bounded graphs. We also describe the simulation results and analyze them.

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
Fig. 9

Similar content being viewed by others

References

  1. Chen J, Li H, Wu J (2011) SES: Stable and efficient solution for rate control and spectrum allocation in wireless LANs. Wirel Pers Commun 66:81–99

    Article  Google Scholar 

  2. Cockayne EJ, Dreyer Jr PA, Hedetniemi SM, Hedetniemi ST (2004) ROman domination in graphs. Discret Math 278:11–22

  3. Chambers EW, Kinnersley B, Prince N, West DB (2009) Extremal problem for rOman domination. SIAM J Discret Math 23:1575–1586

    Article  MathSciNet  MATH  Google Scholar 

  4. Feng ZH, Yang YL (2009) Joint transport, Routing and Spectrum Sharing Optimization for Wireless Networks with Frequency Agile Radios. In: Proceedings of IEEE INFOCOM, pp 1665–1673

  5. Henning MA (2003) Defending the roman empire from multiple attacks. Discret Math 271:101–115

    Article  MathSciNet  MATH  Google Scholar 

  6. Huang H, Richa AW, Segal M (2004) Approximation Algorithms for the Mobile Piercing Set Problem with Applications to Clustering in Ad-Hoc Networks, vol 9. ACM Springer Mobile Networks and Applications (MONET), pp 151–161

  7. Kuhn F, Nieberg T, Moscibroda T, Wattenhofer R (2005) Local approximation schemes for ad hoc and sensor networks, DIALM-POMC, 97-103 Germany

  8. Linial N (1992) Locality in distributed graph algorithms. SIAM J Comput 21:193–201

    Article  MathSciNet  MATH  Google Scholar 

  9. Liu CH, Chang GJ (2012) ROman domination on 2-connected graphs. SIAM J Discret Math 26:193–205

    Article  MathSciNet  MATH  Google Scholar 

  10. Moscibroda T, Chandra R, Wu Y, Sengupta S, Bahl P, Yuan Y (2008) Load-aware spectrum distribution in wireless LANs. In: Proceedings IEEE Int’l Conference Network Protocols (ICNP), pp 137–146

  11. Nieberg T, Hurink J (2004) Wireless communication graphs. In: Proceedings 2004 Intelligent Sensors, Sensor Networks Information Processing Conference, pp 367–372

  12. Peleg D (2000) Distributed Computing-A Locality-Sensitive approach. SIAM, Philadelphia, PA

    Book  MATH  Google Scholar 

  13. Rayanchu S, Shrivastava V, Banerjee S, Chandra R (2012) FLUID: Improving Throughputs in enterprise wireless LANs through flexible channelization. IEEE Tran Mobile Comput 11:1455–1469

    Article  Google Scholar 

  14. ReValle CS (1997) Can you protect the roman empire?. Johns Hopkins Magazine, pp 40–40

  15. Schneider J, Wattenhofer R (2008) A log-star distributed maximal independent set algorithm for growth-bounded graphs

  16. Shang WP, Hu XD (2007) The roman domination Problem in Unit Disk Graphs. Lect Notes Comput Sci 4489:305–312

    Article  Google Scholar 

  17. Stewart I (1999) Defend the roman empire!. Sci Am 281:136–138

    Article  Google Scholar 

  18. Tandra R, Sahai A (2005) Fundamental limits on detection in low SNR under noise uncertainty. In: Proceedings of the Int'l Conference on Wireless Networks, Communications and Mobile Computing, pp 464–469

  19. Yang L, Hou W, Zhao BY (2010). In: Proceedings of the 7th USENIX conference on Networked systems design and implementation, pp 5–5

  20. Yuan Y, Bahl P, Chandra R, Moscibroda T, Wu Y (2007) Allocating dynamic time-spectrum blocks in cognitive radio networks. In: Proceedings of the 8th ACM Int?l Symp. on Mobile Ad Hoc Networking and Computing, pp 130–139

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable suggestions and comments that have helped a lot to improve the quality of the paper. This work is partially supported by the National Science Foundation of China under Grant No.61301159, No.61273047 and No.11471003, China Postdoctoral Science Foundation No.2015M571635, and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China No.13KJB1100188.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaoyan Zhang.

Additional information

Dr. Yalin Shi and Dr. Jian Chen are Co-Primary authors of the paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Y., Chen, J., Wang, L. et al. Distributed Approximation Algorithms for Spectrum Allocation in Wireless ad Hoc Networks. Mobile Netw Appl 21, 962–973 (2016). https://doi.org/10.1007/s11036-016-0714-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-016-0714-8

Keywords

Navigation