title: Use of local intelligence to reduce energy consumption of wireless sensor nodes in elderly health monitoring systems.
abstract: 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.
authors.affiliation: School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg 5412, Austria. Thomas.Lampoltshammer@fh-salzburg.ac.at.
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Lampoltshammer
authors.affiliation: Department of Applied Computing, Federal University of Santa Maria, Santa Maria 97105-900, Brazil. edisonpignaton@unb.br.
authors.forename: Edison
authors.initials: E
authors.lastname: Pignaton de Freitas
authors.affiliation: School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg 5412, Austria. tnowotny.lba@fh-salzburg.ac.at.
authors.forename: Thomas
authors.initials: T
authors.lastname: Nowotny
authors.affiliation: School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg 5412, Austria. stefan.plank@sbg.at.
authors.forename: Stefan
authors.initials: S
authors.lastname: Plank
authors.affiliation: 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. joaopaulo.dacosta@ene.unb.br.
authors.forename: João Paulo Carvalho Lustosa
authors.initials: JP
authors.lastname: da Costa
authors.affiliation: School of Information Science, Computer and Electrical Engineering, Halmstad Universtity, Kristian IV:s väg 3, Halmstad 301 18, Sweden. tony.larsson@hh.se.
authors.forename: Tony
authors.initials: T
authors.lastname: Larsson
authors.affiliation: School of Information Technology and Systems Management, Salzburg University of Applied Sciences, Urstein Süd 1, Puch/Salzburg 5412, Austria. Thomas.Heistracher@fh-salzburg.ac.at.
authors.forename: Thomas
authors.initials: T
authors.lastname: Heistracher
mesh_terms: D000368:Aged; D001185:Artificial Intelligence; D003195:Computer Communication Networks; D006262:Health; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D016571:Neural Networks, Computer; D013816:Thermodynamics; D059015:Wireless Technology / Q000295:instrumentation*