The use of wireless sensor networks in healthcare has the potential to enhance the services provided to citizens. In particular, they play an important role in the development of state-of-the-art patient monitoring applications. Nevertheless, due to the critical nature of the data conveyed by such patient monitoring applications, they have to fulfil high standards of quality of service in order to obtain the confidence of all players in the healthcare industry. In such context, vis-à-vis the quality of service being provided by the wireless sensor network, this work presents an adaptive and context-aware method to detect and classify performance degradation events. The proposed method has the ability to catch the most significant and damaging variations on the metrics being used to quantify the quality of service provided by the network without overreacting to small and innocuous variations on the metric’s value.
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8 June 2016
INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2015 (ICNAAM 2015)
22–28 September 2015
Rhodes, Greece
Research Article|
June 08 2016
Adaptive and context-aware detection and classification of potential QoS degradation events in biomedical wireless sensor networks
Carlos Abreu;
Carlos Abreu
*
Instituto Politécnico de Viana do Castelo
, Viana do Castelo, Portugal
†CMEMS-UMINHO,
Universidade do Minho
, Braga, Portugal
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Francisco Miranda;
Francisco Miranda
*
Instituto Politécnico de Viana do Castelo
, Viana do Castelo, Portugal
**CIDMA,
Universidade de Aveiro
, Aveiro, Portugal
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Paulo M. Mendes
Paulo M. Mendes
†CMEMS-UMINHO,
Universidade do Minho
, Braga, Portugal
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Carlos Abreu
1,2
Francisco Miranda
1,3
Paulo M. Mendes
2
*
Instituto Politécnico de Viana do Castelo
, Viana do Castelo, Portugal
†CMEMS-UMINHO,
Universidade do Minho
, Braga, Portugal
**CIDMA,
Universidade de Aveiro
, Aveiro, Portugal
AIP Conf. Proc. 1738, 370012 (2016)
Citation
Carlos Abreu, Francisco Miranda, Paulo M. Mendes; Adaptive and context-aware detection and classification of potential QoS degradation events in biomedical wireless sensor networks. AIP Conf. Proc. 8 June 2016; 1738 (1): 370012. https://doi.org/10.1063/1.4952157
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