Skip to main content
SpringerLink
Log in

On-demand chaotic neural network for broadcast scheduling problem

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

Abstract

This paper presents a novel approach to optimizing network packet transfer scheme through introducing a new method for on-demand chaotic noise injection strategy for the Broadcast Scheduling Problem (BSP). Packet radio networks have many applications, while finding an optimized scheduling to transmit data is proven to be a NP-hard problem. The objective of the proposed method is to find an optimal time division multiple access (TDMA) frame, based on maximizing the channel utilization. The proposed method benefits from an on-demand noise injection policy, which injects noise based on the status of neuron and its neighborhoods. The method is superior to other Noise Chaotic Neural Networks (NCNN) that suffer from blind injection policy. The experimental result shows that, in most cases, the proposed on-demand noise injection algorithm finds the best solution with minimal average time delay and maximum channel utilization.

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

Similar content being viewed by others

References

  1. Leiner BM, Nielson DL, Tobagi FA (1987) Issues in packet radio network designing. Proc IEEE 75(1):6–20

    Article  Google Scholar 

  2. Ephremides A, Truong TV (1990) Scheduling broadcast in multihop radio networks. IEEE Trans Commun 38(6):456–460

    Article  Google Scholar 

  3. Funabiki N, Takefuji Y (1993) A parallel algorithm for broadcast scheduling problems in packet radio networks. IEEE Trans Commun 41(6):828–831

    Article  Google Scholar 

  4. Wang G, Ansari N (1997) Optimal broadcast scheduling in packet radio networks using mean field annealing. IEEE J Sel Areas Commun 15(2):250–260

    Article  Google Scholar 

  5. Chakraborty G, Hirano Y (1998) Genetic algorithm for broadcast scheduling in packet radio networks. In: IEEE World Congr Computational Intelligence, pp 183–188

  6. Funabiki N, Kitamichi J (1999) A gradual neural network algorithm for broadcast scheduling problems in packet radio networks. IEICE Trans Fund E82-A(5):815–824

    Google Scholar 

  7. Yeo J, Lee H, Kim S (2002) An efficient broadcast scheduling algorithm for TDMA ad-hoc networks. Comput Oper Res (29):1793–1806

  8. Salcedo-Sanz S, Bousoño-Calzón C, Figueiras-Vidal AR (2002) A mixed neural-genetic algorithm for the broadcast scheduling problem. IEEE Trans Wirel Commun 2(2):277–283

    Article  Google Scholar 

  9. Wang L, Shi H (2006) A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks. IEEE Trans Neural Netw 17(4):989–1001

    Article  Google Scholar 

  10. Nozawa H (1992) A neural-network model as a globally coupled map and applications based on chaos. Chaos 2(3):377–386

    Article  MATH  MathSciNet  Google Scholar 

  11. Chen L, Aihara K (1997) Chaos and asymptotical stability in discrete time neural networks. Physica D 104:286–325

    Article  MATH  MathSciNet  Google Scholar 

  12. Wang L, Li S, Tian F, Fu X (2004) A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing. IEEE Trans Syst Man Cybern 34(5):2119–2125

    Article  Google Scholar 

  13. Aihara K, Takabe T, Toyoda M (1990) Chaotic neural networks. Phys Lett A 144(6–7):33–340

    MathSciNet  Google Scholar 

  14. Yamada T, Aihara K, Kotani M (1993) Chaotic neural networks and the travelling salesman problem. In: Proc Int Joint Conf Neural Networks, pp 1549–1552

  15. Hopfield JJ, Tank DW (1985) Neural computation of decisions in optimization problems. Biol Cybern 52:141–152

    MATH  MathSciNet  Google Scholar 

  16. Chen L, Aihara K (1995) Chaotic simulated annealing by a neural network model with transient chaos. Neural Netw 8(6):915–930

    Article  Google Scholar 

  17. Wang L, Smith K (1998) On chaotic simulated annealing. IEEE Trans Neural Netw 9:716–718

    Article  Google Scholar 

  18. Xuhua D, Shuhong W, Baihua Z (2006) Secure real-time user preference collection for broadcast scheduling. In: Securecomm and Workshops, pp 1–10

  19. Zahng X (2007) Efficient broadcast scheduling based on fuzzy clustering and hopfield network for ad hoc networks. Mach Learn Cybern 6:3255–3260

    Google Scholar 

  20. Yongrui Q, Weiwei S, Zhuoyao Z, Ping Y (2009) An efficient document-split algorithm for on-demand XML data broadcast scheduling. Wirel Mob Sensor Netw, pp 759–762

  21. Li K, Min G, Wei T (2009) High performance computing and communications. J Supercomput 51:95–96

    Article  Google Scholar 

  22. Waluyo AB, Srinivasan B, Taniar D (2003) Optimal broadcast channel for data dissemination in mobile database environment. Adv Parallel Proces Technol 2834:655–664

    Article  Google Scholar 

  23. Waluyo AB, Srinivasan B, Taniar D (2008) Indexing schemes for multichannel data broadcasting in mobile databases. Int J Wirel Mob Comput 3:1741–1084

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of Computer Science, University of Calgary, Calgary, Canada

    Marina Gavrilova & Kushan Ahmadian

Authors
  1. Marina Gavrilova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kushan Ahmadian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marina Gavrilova.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gavrilova, M., Ahmadian, K. On-demand chaotic neural network for broadcast scheduling problem. J Supercomput 59, 811–829 (2012). https://doi.org/10.1007/s11227-010-0473-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-010-0473-4

Keywords

Search

Navigation