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

  • Authors:

  • Christos Anagnostopoulos;Stathes Hadjiefthymiades;Antonis Katsikis;Ilias Maglogiannis

  • Affiliations:

  • Department of Informatics, Ionian University, Corfu, Greece 49100;Department of Informatics and Telecommunications, University of Athens, Athens, Greece 15784;Department of Informatics and Telecommunications, University of Athens, Athens, Greece 15784;Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia, Greece 35100

  • Venue:
  • Personal and Ubiquitous Computing
  • Year:
  • 2014

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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.