A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Correlation-Based Fault Detection Systems <s> Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average. <s> BIB001 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Correlation-Based Fault Detection Systems <s> This paper describes a data-driven approach to sensor data validation. The data originates from a network of sensors embedded in an indoor environment such as an office, home, factory, public mall or airport. Data analysis is performed to automatically detect events and classify activities taking place within the environment. Sensor failure and in particular intermittent failure, caused by electrical interference, undermines the inference processes. PCA and CCA are compared for detecting intermittent faults and masking such failures. The fault detection relies on models built from historical data. As new sensor observations are collected the model is updated and compared to that previously estimated, where a difference is indicative of a failure. <s> BIB002 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Correlation-Based Fault Detection Systems <s> Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper explores generic solutions towards detecting missing data on event-driven sensors using both temporal correlation and time series analysis. The solutions are evaluated on a real-world dataset and achieve promising results with accuracy around 80%. <s> BIB003 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Correlation-Based Fault Detection Systems <s> Experiments in assisted living confirm that such systems can provide context-aware services that enable occupants to remain active and independent. They also demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a technique that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets that include multiple individuals, and show consistent rates of anomaly detection across different environments. <s> BIB004 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Correlation-Based Fault Detection Systems <s> IoT enabled smart environments are expected to proliferate significantly in the near future, particularly in the context of monitoring services for wellness living, patient healthcare and elderly care. Timely maintenance of failed sensors is of critical importance in such deployments to ensure minimal disruption to monitoring services. However, maintenance of large and geographically spread deployments can be a significant challenge. We present Idea that significantly increases the vtime-before-repair for a smart home deployment, thereby reducing the maintenance overhead. Specifically, our approach leverages the facts that (a) there is inherent sensor redundancy when combinations of sensors monitor activities of daily living (ADLs) in smart environments, and (b) the impact of each sensor failure depends on the activities being monitored and the functional redundancy afforded by rest of the heterogeneous sensors available for detecting the activities. Consequently, Idea identifies homes that need to be fixed based on expected degradation in ADL detection performance, and optimizes maintenance scheduling accordingly. We demonstrate that our approach leads to 3--40 times fewer maintenance personnel than a scheme in which failed sensors are fixed without considering their impact. <s> BIB005 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Correlation-Based Fault Detection Systems <s> Abstract With a rising ageing population, smart home technologies have been demonstrated as a promising paradigm to enable technology-driven healthcare delivery. Smart home technologies, composed of advanced sensing, computing, and communication technologies, offer an unprecedented opportunity to keep track of behaviours and activities of the elderly and provide context-aware services that enable the elderly to remain active and independent in their own homes. However, experiments in developed prototypes demonstrate that abnormal sensor events hamper the correct identification of critical (and potentially life-threatening) situations, and that existing learning, estimation, and time-based approaches to situation recognition are inaccurate and inflexible when applied to multiple people sharing a living space. We propose a novel technique, called CLEAN, that integrates the semantics of sensor readings with statistical outlier detection. We evaluate the technique against four real-world datasets across different environments including the datasets with multiple residents. The results have shown that CLEAN can successfully detect sensor anomaly and improve activity recognition accuracies. <s> BIB006
Next, we discuss the pros and cons of the correlation-based fault detection approaches. FailureSense  has good average precision and recall for the examined fail-stop, obstructed-view and moved-location failures. Also, the experiments show consistent performance for failure detection with increasing the number of sensors that had fail-stop failures. However, the method does not work well if the failed sensor is not associated with any electrical appliance. In addition, its average failure detection latency is not suitable for emergency situations. Another limitation of the system is that, it is based on the assumption that the resident has to be physically beside the appliance to turn it on. In addition, the system performance depends on the behaviour of residents (i.e., the residents turn on/off electrical appliances remotely or their behaviour pattern in using electrical appliances). Using temporal correlations and/or time-series analysis in BIB003 only relies on sensors firing to detect missing sensor events. The temporal correlation method achieves better results than using time-series analysis. However, the average precision and recall on the examined dataset with random non-fail-stop failures are not as good when increasing the error rate percentage, except when the training data percentage was increased to 90% . This makes the performance of the proposed method still questionable and needs to be evaluated on other larger datasets. The approach of the Idea system BIB005 is designed to suit homes equipped with functionally redundant sensors per activity of daily living. Otherwise, it will not work as expected. In this work, only fail-stop failures were considered. The reduction in the ADL detection accuracy in the presence of sensor failures is relatively low. Thus, an efficient fault tolerant activity recognition seems to be promising using the proposed approach. However, the effect of monitoring multiple ADLs to detect sensor failures on the failure latency detection, and the effect of rarity threshold on the false positive alerts were the only assessments used for the sensor failure detection subsystem. Those assessments are not enough to be able to see the efficiency of the sensor failure detection. Also, non-fail-stop failures need to be considered in the experiments. In our opinion, detecting failures using time elapsed is not an efficient solution and using the rarity score assumes that the system has not misclassified the activity. Similarly, the detection of sensor failures using the proposed approach in BIB002 was not thoroughly evaluated. The experiments were only concerned with the ability of the system to detect and isolate a faulty sensor, without any quantitative evaluation of the performance. Another drawback is that the injected faults in the experiments were applied on only a single sensor. The advantage of using clustering approach as in BIB004 BIB006 , is that no training phase is required. However, the proposed method aims to detect false sensor triggers, but it can not detect missing sensor data. Another limitation is that the failure detection takes place in a post-processing step on the collected data. Also, the false positive trigger is less likely to be detected if it is associated with a sensor that has similar features to other correctly working sensors. Using multiple classifier instances BIB001  produced promising results for sensor failure detection and fault-tolerant activity recognition. However, the disadvantage of this approach is that the training effort is large and it increases proportionally with the number of installed sensors, thus the system is non-scalable.