TY - JOUR
TI - Autoregressive energy-efficient context forwarding in wireless sensor networks for pervasive healthcare systems
AU - Anagnostopoulos, C.
AU - Hadjiefthymiades, S.
AU - Katsikis, A.
AU - Maglogiannis, I.
JO - Personal and Ubiquitous Computing
PY - 2014
VL - 18
TODO - 1
SP - 101-114
PB - Springer, London
SN - null
TODO - 10.1007/s00779-012-0621-3
TODO - Data acquisition;  Energy efficiency;  Energy utilization;  Health care;  Low power electronics;  Power management (telecommunication);  Regression analysis;  Remote control, Biosignals;  Contextual information;  Electronic healthcare;  Health care application;  Multivariate autoregressive models;  Pervasive healthcare application;  Pervasive healthcare systems;  Transmission control, Wireless sensor networks
TODO - 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. © 2012 Springer-Verlag London.
ER -