Skip to main content

Advertisement

SpringerLink
Log in

Autoregressive energy-efficient context forwarding in wireless sensor networks for pervasive healthcare systems

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

Driven by technological advances in low-power network systems and medical sensors, we have witnessed during the recent years the adoption of wireless sensor networks (WSNs) in electronic healthcare. Improving the quality of electronic healthcare and the prospects of ‘ageing in place’ through WSNs requires solving difficult problems in scale, energy management, and data acquisition. Medical and pervasive healthcare application (or mobile healthcare application) based on WSNs is influenced by many factors such as transmission errors and power consumption. We propose a multivariate context forwarding model that achieves energy-efficient WSN operation. A node adopts multivariate autoregression for forecasting contextual information (bio-signals or vital parameters) and locally decides whether context retransmission is required or not. This scheme is applied in patient telemonitoring systems where accurate yet energy-aware transmission of bio-signals to a remote control unit is crucial. Simulation results are reported indicating the capability of the proposed model in minimizing energy consumption in WSNs having as application domain the electronic healthcare systems.

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
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Chipara O, Lu C, Bailey TC, Roman G-C (2009) Reliable patient monitoring: a clinical study in a step-down hospital unit. Dept. Comput. Sci. Eng., Washington Univ. St. Louis, St. Louis, MO, technical report WUCSE-2009-82

  2. Virone G, Wood A, Selavo L, Cao Q, Fang L, Doan T, He Z, Stankovic JA (2006) An advanced wireless sensor network for health monitoring. In: Proceedings of transdisciplinary conferences on distributed diagnosis and home healthcare, April 2006, pp 95–100

  3. Patrick K (2007) A tool for geospatial analysis of physical activity: physical activity location measurement system (PALMS). NIH GEI project at the University of California at San Diego

  4. Lymberopoulos D, Bamis A, Savvides A (2008) Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. In Proceedings of ACM 1st international conference on pervasive technologies related to assistive environments (PETRA ’08), article 29

  5. Wood A, Stankovic J, Virone G, Selavo L, He Z, Cao Q, Doan T, Wu Y, Fang L, Stoleru R (2008) Context-aware wireless sensor networks for assisted living and residential monitoring. IEEE Netw 22(4):26–33

    Article  Google Scholar 

  6. Mileo A, Merico D, Bisiani R (2008) Wireless sensor networks supporting context-aware reasoning in assisted living. In Proceedings of ACM 1st international conference on pervasive technologies related to assistive environments (PETRA ’08), article 54

  7. Michalopoulos M, Anagnostopoulos C, Doukas C, Maglogiannis I, Hadjiefthymiades S (2010) Optimizing pervasive sensor data acquisition utilizing missing values substitution. In Proceedings of ACM 1st international conference on pervasive technologies related to assistive environments (PETRA ’08), article 11

  8. Akyildiz I, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422

    Article  Google Scholar 

  9. Anagnostopoulos C, Anagnostopoulos T, Hadjiefthymiades S (2010) An adaptive data forwarding scheme for energy efficiency. In: IEEE conference of intelligent systems wireless sensor networks, 2010, pp 396–401

  10. Doukas C, Maglogiannis I (2011) emergency fall incidents detection in assisted living environments utilizing motion, sound and visual perceptual components. IEEE Trans Inf Technol Biomed 15(2):277–289

    Article  Google Scholar 

  11. Doukas C, Maglogiannis I (2008) Intelligent pervasive healthcare systems. Adv Comput Intel Paradig Healthc Stud Comput Intel 107:95–115

    Article  Google Scholar 

  12. Maglogiannis I (2009) Introducing intelligence in electronic healthcare systems: state of the art and future trends. Artif Intel Int Perspect LNAI 5640

  13. Luetkepohl H (1991) Introduction to multiple time series analysis. Springer, New York, pp 368–370

    Book  MATH  Google Scholar 

  14. Neumaier A, Schneider T (2001) Estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw 27(1):27–57

    Article  MATH  Google Scholar 

  15. ARfit, ARfit: multivariate autoregressive model fitting. http://www.gps.caltech.edu/tapio/arfit/

  16. Manjeshwar A, Agrawal D April (2001) TEEN: A protocol for enhanced efficiency in wireless sensor networks. In: Proceedings of the 1st international workshop on parallel and distributed computing issues in wireless networks and mobile computing

  17. He T, Krishnamurthy S, Stankovic JA, Abdelzaher T, Luo L, Stoleru R, Yan T, Gu L, Hui J, Krogh B Energy efficient surveillance system using wireless sensor networks. In: 2nd ACM international conference on mobile systems, applications, and services, (ACM MobiSys 04), pp 270–283

  18. Schneider T, Neumaier A (2001) Algorithm 808: ARFIT—a matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM Trans Math Softw 27(1):58–65

    Article  MATH  Google Scholar 

  19. Björck A (1996) Numerical methods for least squares problems. Society for Industrial and Applied Mathematics, Philadelphia, PA

  20. Intanagonwiwat C, Govindan R, Estrin D (2000) Directed diffusion: a scalable and robust communication paradigm for sensor networks. In Proceedings of the ACM MobiCom ’00

  21. Shah R, Rabaey J March (2002) Energy aware routing for low energy Ad Hoc sensor networks. In: Proceedings of the IEEE wireless communications and networking conference, vol 1, pp 350–355

  22. Manjeshwar A, Agrawal D (2002) APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks. In: Proceedings of the parallel and distributed processing symposium, IPDPS 2002, pp 195–202

  23. Tung Chou C, Rana R, Hu W (2009) Energy efficient information collection in wireless sensor networks using adaptive compressive sensing. In: Proceedings of IEEE conference on local computer networks, pp 443–450

  24. Lee H, Klappenecker A, Lee K, Lin L (2005) Energy efficient data management for wireless sensor networks with data sink failure. In: Proceedings of the IEEE international conference on mobile adhoc and sensor systems conference, 7 Nov 2005, p 210

  25. Arroyo-Valles R, Marques A, Cid-Sueiro J (2009) Optimal selective transmission under energy constraints in sensor networks. IEEE Trans Mob Comput 8(11):1524–1538

    Article  Google Scholar 

  26. Anagnostopoulos C, Hadjiefthymiades S, Georgas P (2012) PC3: principal component-based context compression: improving energy efficiency in wireless sensor networks. J Parallel Distrib Comput 72(2):155–170

    Article  MATH  Google Scholar 

  27. Anagnostopoulos C, Hadjiefthymiades S (2011) Context compression: using principal component analysis for efficient wireless communications. IEEE Mob Data Manag (1):206–215

  28. Rout S, Turuk AK, Sahoo B (2009) Energy efficiency in wireless network: through alternate path routing. In: Proceedings of the 4th innovative conference on embedded systems, mobile communication and computing, 2009, pp 3–8

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Ionian University, 49100, Corfu, Greece

    Christos Anagnostopoulos

  2. Department of Informatics and Telecommunications, University of Athens, 15784, Athens, Greece

    Stathes Hadjiefthymiades & Antonis Katsikis

  3. Department of Computer Science and Biomedical Informatics, University of Central Greece, 35100, Lamia, Greece

    Ilias Maglogiannis

Authors
  1. Christos Anagnostopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stathes Hadjiefthymiades
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonis Katsikis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ilias Maglogiannis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Anagnostopoulos.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Anagnostopoulos, C., Hadjiefthymiades, S., Katsikis, A. et al. Autoregressive energy-efficient context forwarding in wireless sensor networks for pervasive healthcare systems. Pers Ubiquit Comput 18, 101–114 (2014). https://doi.org/10.1007/s00779-012-0621-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-012-0621-3

Keywords

Search

Navigation