Doctoral thesis (Dissertations and theses)
CONFIDENCE-BASED DECISION-MAKING SUPPORT FOR MULTI-SENSOR SYSTEMS
Neyens, Gilles
2019
 

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Keywords :
Proactive Computing; Multi-sensor Systems; e-Health; Robots; Autonomic Computing; Sensor Fusion
Abstract :
[en] We live in a world where computer systems are omnipresent and are connected to more and more sensors. Ranging from small individual electronic assistants like smartphones to complex autonomous robots, from personal wearable health devices to professional eHealth frameworks, all these systems use the sensors’ data in order to make appropriate decisions according to the context they measure. However, in addition to complete failures leading to the lack of data delivery, these sensors can also send bad data due to influences from the environment which can sometimes be hard to detect by the computer system when checking each sensor individually. The computer system should be able to use its set of sensors as a whole in order to mitigate the influence of malfunctioning sensors, to overcome the absence of data coming from broken sensors, and to handle possible conflicting information coming from several sensors. In this thesis, we propose a computational model based on a two layer software architecture to overcome this challenge. In a first layer, classification algorithms will check for malfunctioning sensors and attribute a confidence value to each sensor. In the second layer, a rule-based proactive engine will then build a representation of the context of the system and use it along some empirical knowledge about the weaknesses of the different sensors to further tweak this confidence value. Furthermore, the system will then check for conflicting data between sensors. This can be done by having several sensors that measure the same parameters or by having multiple sensors that can be used together to calculate an estimation of a parameter given by another sensor. A confidence value will be calculated for this estimation as well, based on the confidence values of the related sensors. The successive design refinement steps of our model are shown over the course of three experiments. The first two experiments, located in the eHealth domain, have been used to better identify the challenges of such multi-sensor systems, while the third experiment, which consists of a virtual robot simulation, acts as a proof of concept for the semi-generic model proposed in this thesis.
Disciplines :
Computer science
Author, co-author :
Neyens, Gilles ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Language :
English
Title :
CONFIDENCE-BASED DECISION-MAKING SUPPORT FOR MULTI-SENSOR SYSTEMS
Defense date :
31 October 2019
Number of pages :
106
Institution :
Unilu - University of Luxembourg, Luxembourg
Degree :
DOCTEUR DE L’UNIVERSITÉ DU LUXEMBOURG EN INFORMATIQUE
Jury member :
Waber, Jens
Cleve, Anthony
Navet, Nicolas 
Focus Area :
Computational Sciences
Available on ORBilu :
since 11 January 2020

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