Skip to main content

Advertisement

SpringerLink
Log in

DLRDG: distributed linear regression-based hierarchical data gathering framework in wireless sensor network

  • ISNN2012
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

For many applications in wireless sensor network (WSN), the gathering of the holistic sensor measurements is difficult due to stringent constraint on network resources, frequent link, indeterminate variations in sensor readings, and node failures. As such, sensory data extraction and prediction technique emerge to exploit the spatio-temporal correlation of measurements and represent samples of the true state of the monitoring area at a minimal communication cost. In this paper, we present DLRDG strategy, a distributed linear regression-based data gathering framework in clustered WSNs. The framework can realize the approximate representation of original sensory data by less than a prespecified threshold while significantly reducing the communication energy requirements. Cluster-head (CH) nodes in WSN maintain linear regression model and use historical sensory data to perform estimation of the actual monitoring measurements. Rather than transmitting original measurements to sink node, CH nodes communicate constraints on the model parameters. Relying on the linear regression model, we improved the CH node function of representative EADEEG (an energy-aware data gathering protocol for WSNs) protocol for estimating the energy consumption of the proposed strategy, under specific settings. The theoretical analysis and experimental results show that the proposed framework can implement sensory data prediction and extracting with tolerable error bound. Furthermore, the designed framework can achieve more energy savings than other schemes and maintain the satisfactory fault identification rate on case of occurrence of the mutation sensor readings.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Wander AS, Gura N, Eberle H et al (2005) Energy analysis of public-key cryptography for wireless sensor networks. In: Proceedings of the third IEEE international conference on computing and communications, Seattle, pp 324–328

  2. Estrin D (2005) Wireless sensor networks tutorial part IV: sensor network protocols. Invited speech of International Conference on Mobile Computing and Networking (Mobicom), Atlanta

  3. Anastasi G, Marco C, Di Francesco M, Passarella A (2009) Energy conservation in wireless sensor network: a survey. Ad Hoc Netw 7(3):537–568

    Article  Google Scholar 

  4. Xu XH, Li XY, Mao XF et al (2011) A delay-efficient algorithm for data aggregation in multihop wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(1):163–175

    Article  Google Scholar 

  5. Wu YW, Li XY, Liu YH et al (2010) Energy-efficient wake-up scheduling for data collection and aggregation. IEEE Trans Parallel Distrib Syst 21(2):275–287

    Article  Google Scholar 

  6. Liu B, Ren FY, Lin C, Jiang X (2008) Performance analysis of sleep scheduling schemes in sensor networks using stochastic petri net. In: Proceedings of IEEE international conference on communications. Beijing, pp 4278–4283

  7. Monaco U, Cuomo F, Melodia T et al (2006) Understanding optimal data gathering in the energy and latency domains of a wireless sensor network. Comput Netw 50(18):3564–3584

    Article  MATH  Google Scholar 

  8. Bista R, Kim YK, Chang JW (2009) A new approach for energy-balanced data aggregation in wireless sensor networks. In: Proceedings of ninth IEEE international conference on computer and information technology, Xiamen, vol 2, pp 9–15

  9. Ren HL, Meng MMQH (2006) Biologically inspired approaches for wireless sensor networks. In: Proceedings of IEEE international conference on mechatronics and automation, Luoyang, pp 762–768

  10. Saleem M, Di Caro GA, Farooq M (2010) Swarm intelligence based routing protocol for wireless sensor networks: survey and future directions. Inf Sci 181(20):4597–4624

    Article  Google Scholar 

  11. Al-Karaki JN, Ul-Mustafa R, Kamal AE (2009) Data aggregation and routing in wireless sensor networks: optimal and heuristic algorithms. Comput Netw 53(7):945–960

    Article  MATH  Google Scholar 

  12. Zheng J, Wang P, Li C (2010) Distributed data aggregation using slepian-wolf coding in cluster-based wireless sensor networks. IEEE Trans Veh Technol 59(5):2564–2574

    Article  Google Scholar 

  13. Konstantopoulos C, Mpitziopoulos A, Gavalas D et al (2010) Effective determination of mobile agent itineraries for data aggregation on sensor networks. IEEE Trans Knowl Data Eng 22(12):1679–1693

    Article  Google Scholar 

  14. Lin K, Chen M, Zeadally S, Rodrigues JJPC (2012) Balancing energy consumption with mobile agents in wireless sensor networks. Future Gener Comput Syst 28(2):446–456

    Article  Google Scholar 

  15. Jiang HB, Jin SD, Wang CG (2010) Parameter-Based data aggregation for statistical information extraction in wireless sensor networks. IEEE Trans Veh Technol 59(8):3992–4001

    Article  Google Scholar 

  16. Deligiannakis A, Kotidis Y, Roussopoulos N (2007) Dissemination of compressed historical information in sensor networks. VLDB J 16(4):439–461

    Article  Google Scholar 

  17. Srisooksai T, Keamarungsi K, Lamsrichan P, Araki K (2011) Practical data compression in wireless sensor networks: a survey. J Netw Comput Appl 35(1):37–59

    Article  Google Scholar 

  18. Jiang HB, Jin SD, Wang CG (2011) Prediction or not? an energy-efficient framework for clustering-based data collection in wireless sensor networks. IEEE Trans Parallel Distrib Syst 22(6):1064–1071

    Article  Google Scholar 

  19. Zhu H, Schizas ID, Giannakis GB (2009) Power-efficient dimensionality reduction for distributed channel-aware kalman tracking using WSNs. IEEE Trans Signal Process 57(8):3193–3207

    Article  MathSciNet  Google Scholar 

  20. Zhang HG, Quan YB (2001) Modeling, identification, and control of a class of nonlinear systems. IEEE Trans Fuzzy Syst 9(2):349–354

    Article  Google Scholar 

  21. Zheng HP, Kulkarni SR, Poor HV (2011) Attribute-distributed learning: models, limits, and algorithms. IEEE Trans Signal Process 59(1):386–398

    Article  MathSciNet  Google Scholar 

  22. Guestrin C, Bodik P, Thibaux R et al (2004) Distributed regression: an efficient framework for modeling sensor network data. In: Proceedings of Third International Symposium on Information Processing in Sensor Networks, California, USA, pp 1-10

  23. Mateos G, Bazerque JA, Giannakis GB (2010) Distributed sparse linear regression. IEEE Trans Signal Process 58(10):5262–5276

    Article  MathSciNet  Google Scholar 

  24. Zhang HG, Liu JH, Ma DZ, Wang ZS (2011) Data-core-based fuzzy min–max neural network for pattern classification. IEEE Trans Neural Netw 22(12):2339–2352

    Article  Google Scholar 

  25. Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1(4):660–670

    Article  Google Scholar 

  26. Liu M, Cao JN et al (2007) EADEEG: an energy-aware data gathering protocol for wireless sensor networks. J Softw 18(5):1092–1109 (in Chinese)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

The research work was supported by the National Natural Science Foundation of China under Grant No. 61070162 and 71071028, and open research fund of Key Laboratory of Complex System and Intelligence Science, Institute of Automation, Chinese Academy of Sciences under grant No. 20100106.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Northeastern University, 110819, Shenyang, China

    Xin Song, Cuirong Wang, Jing Gao & Xi Hu

  2. The Key Laboratory of Complex System and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Xin Song

Authors
  1. Xin Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cuirong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xi Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Song.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Song, X., Wang, C., Gao, J. et al. DLRDG: distributed linear regression-based hierarchical data gathering framework in wireless sensor network. Neural Comput & Applic 23, 1999–2013 (2013). https://doi.org/10.1007/s00521-012-1248-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1248-z

Keywords

Search

Navigation