A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault Detection and Diagnosis Framework <s> Ambient intelligence (AmI) systems are smart interactive systems that perceive their surroundings using sensors and act upon them using actuators. One of the most common applications of such systems is Smart Homes. In this context, the ambient system can offer a great level of dependability if it is able to exploit available sensor data in order to autonomously perform diagnosis. However, ambient environments are dynamic in a sense that components, in general, and actuators and sensors, in particular, can be added or removed from the system at run-time. This dynamicity raises new challenges not addressed in the state of the art of fault detection and diagnosis techniques. Unlike classical control theory methods, control-loops between ambient system components cannot be pre-determined at design time. In this paper we propose a new approach based on the modeling of physical phenomena, allowing one to use available resources to predict the values that are supposed to be read by sensors. Comparing the predictions and the real readings allows us to detect potential faults. Fault detection may be followed by fault isolation, which tries to identify the faulty component precisely. <s> BIB001 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault Detection and Diagnosis Framework <s> As the population in many countries is steadily aging, allowing elderly people to stay longer at home is a growing concern. Ambient Assisted Living (AAL) proposes new techniques to help people remain autonomous, based on ambient intelligence. We present an ontology-based framework in which ontologies enable the expression of users' preferences in order to personalize the system behavior. They are also used for the discovery and interconnection of devices, the storage and retrieval of collected data and the transmission of actions. Basing everything on ontologies allows the designer to express the behavior of the system using high-level logic rules. To render AAL systems as autonomous as possible, devices that fail should be detected at runtime. For this reason, the framework offers a diagnosis service that builds a prediction model of the values detected by sensors. It is based on information discovered opportunistically at run-time and knowledge about physical laws. The framework monitors the run-time behavior of the AAL system and uses the prediction model to detect inconsistencies and hence faults. Therefore, fault detection is totally dynamic and opportunistic; there are no pre-defined control loops. This paper describes an actual implementation, with precise technological details, in order to prove the feasibility of the technical choices, and provide implementation ideas for future projects. <s> BIB002 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault Detection and Diagnosis Framework <s> The aging of the population makes it compulsory to develop innovative solutions to foster aging in place. Aiming to offer a solution to this problem, our team works on the development of an autonomic Do-it-Yourself Adaptable Intelligent Domestic Environment (DIY-AIDE) system. In this paper, we propose a self-diagnosis framework that allows the DIY-AIDE system to detect and diagnose its faults. The framework is built based on the Bayesian Network modeling approach. Although powerful, when using BN we have to deal with the construction of the BN structure which happens to be a complex task because of the customized service offered by the DIY-AIDE system. To overcome this challenge, we developed the AAL-based Bayesian Network Construction Algorithm (AAL-BNCA). Through a case study, we show the operation mode of our framework and the performance of AAL-BNCA in diagnosing a system fault in a given assistance scenario. <s> BIB003
The reviewed fault detection and diagnosis frameworks BIB001 BIB002 BIB003 were designed to only suit AAL systems involved with sensor-actuator feedback.