A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> • <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> • <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> • <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 majority of the developed algorithms use parameters or thresholds that need to be chosen by an expert rather than being deduced automatically. • Differentiating between failed sensors and anomalies in human behaviour is still a challenge that needs to be addressed. Limitations regarding the datasets: • The public datasets used for the training and testing phases are limited to short duration, low sensor node redundancy and single resident apartments. • Also, the data in the publicly available datasets was originally collected for activity detection with labelled activities, thus, failures or anomalies were not labelled. Instead, sensor failures were manually injected and simulated by the researchers, which may not be representative of real-home sensor failures rate and percentage. Limitations regarding the experimental methodology: • It is difficult to compare between the efficiency of the presented approaches because not all the authors use the same evaluation criteria and same testing data. Thus, there is a need for standardized evaluation criteria. • Beside the accuracy, precision and recall, the sensor failure detection latency is an important criterion to be considered. • Real-time online evaluation of the algorithms was not carried out, instead the data collected from previous experiments or datasets were fed to the algorithms. The proposed approaches should additionally be evaluated on data collected from elderlies with physical and/or cognitive deficiencies. BIB001 BIB002 fault detection and diagnosis framework for AAL modeling the physical phenomena that are supposed to be detected by sensor due to the activation of an actuator not applicable simulating a scenario in presence of sensor failure not specified ability to detect system fault BIB003 self-diagnosis framework for AAL Bayesian network for each scenario that is supposed to be fulfilled by the AAL system to assist the user bayesian network construction algorithm scenario of inhabitant in the presence of sensor failure fail-stop ability to detect system fault As illustrated by the number and importance of the limitations of the existing works, fault-tolerance in AAL is still in its early phase. Thus, intensive research work is still needed to tackle them. The research topics to be addressed can be grouped in the three following research questions: • Can novel machine learning techniques tackle the problem of sensor failure detection in AAL without the need for expert knowledge? • Should the research priority be directed towards enhancing the accuracy of binary sensors or instead towards dealing with the faulty sensors data through fault-tolerant systems? • Would differentiating between behaviour anomalies of residents and sensor anomalies be possible? As a conclusion, as highlighted by this systematic literature review, methods for fault-tolerant Ambient Assisted Living are still in their infancy stage. Also, intensive research works would be needed to ensure the development and implementation of a robust sensor fault detection and diagnosis system for Ambient Assisted Living in a near future.