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Sensors 2014, 14(3), 4932-4947;

Use of Local Intelligence to Reduce Energy Consumption of Wireless Sensor Nodes in Elderly Health Monitoring Systems

School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg 5412, Austria
Department of Applied Computing, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Laboratory of Array Signal Processing, Department of Electrical Engineering, University of Brasilia, Campus Universitário Darcy Ribeiro, S/N, Asa Norte, Brasília 70910-900, DF, Brazil
School of Information Science, Computer and Electrical Engineering, Halmstad Universtity, Kristian IV:s väg 3, Halmstad 301 18, Sweden
Author to whom correspondence should be addressed.
Received: 17 January 2014 / Revised: 4 March 2014 / Accepted: 4 March 2014 / Published: 11 March 2014
PDF [512 KB, uploaded 21 June 2014]


The percentage of elderly people in European countries is increasing. Such conjuncture affects socio-economic structures and creates demands for resourceful solutions, such as Ambient Assisted Living (AAL), which is a possible methodology to foster health care for elderly people. In this context, sensor-based devices play a leading role in surveying, e.g., health conditions of elderly people, to alert care personnel in case of an incident. However, the adoption of such devices strongly depends on the comfort of wearing the devices. In most cases, the bottleneck is the battery lifetime, which impacts the effectiveness of the system. In this paper we propose an approach to reduce the energy consumption of sensors’ by use of local sensors’ intelligence. By increasing the intelligence of the sensor node, a substantial decrease in the necessary communication payload can be achieved. The results show a significant potential to preserve energy and decrease the actual size of the sensor device units. View Full-Text
Keywords: sensors; wireless; energy; AAL; health care sensors; wireless; energy; AAL; health care
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Lampoltshammer, T.J.; Pignaton de Freitas, E.; Nowotny, T.; Plank, S.; Da Costa, J.P.C.L.; Larsson, T.; Heistracher, T. Use of Local Intelligence to Reduce Energy Consumption of Wireless Sensor Nodes in Elderly Health Monitoring Systems. Sensors 2014, 14, 4932-4947.

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