A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Fault Detection and Diagnosis Framework for AAL <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 for AAL <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 for AAL <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
A fault detection and diagnosis framework for Ambient Intelligent systems was presented by Mohamed, Jacquet and Bellik BIB001 BIB002 , however, it is concerned only with the tasks performed by the systems through the actuators. The approach is based on modeling the physical phenomena that are supposed to occur in the environment due to the activation of a particular actuator. The system then automatically creates links between actuators and sensors at run-time using the models. It predicts the expected sensor reading due to the activation of an actuator and compares it with the actual sensor reading to detect if a fault has occurred. Simulations were performed to illustrate the operation of the system and show the ability of the system to discover new components at run-time. The basic idea of the diagnoser model was presented without details. A self-diagnosis framework was proposed by Oliveira et al. BIB003 , where a Bayesian network construction algorithm is used to create a Bayesian network for each scenario that is supposed to be fulfilled by the AAL system to assist the user. The algorithm takes as inputs the rules file that specifies the causal relations between variables, and the scenario description file that specifies the required assistance and the home description. Conditional probability distribution is calculated for each child node. The real values are then compared with the predicted ones and a fault is flagged if the readings are different. Using the causality relations and conditional rules, a diagnostic is reached. A case study was investigated to show the ability of the proposed framework to detect and diagnose faults. However, like the previous system BIB002 , the framework would only work fine for the tasks that involve a sensor-actuator feedback.