A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Background on Sensors Failures in Smart homes and AAL <s> In the context of a constantly increasing aging population with cognitive deficiencies, insuring the autonomy of the elders at home becomes a priority. The DOMUS laboratory is addressing this issue by conceiving a smart home which can both assist people and preserve their quality of life. Obviously, the ability to monitor properly the occupant's activities and thus provide the pertinent assistance depends highly on location information inside the smart home. This paper proposes a solution to localize the occupant thanks to Bayesian filtering and a set of anonymous sensors disseminated throughout the house. The localization system is designed for a single person inside the house. It could however be used in conjunction with other localization systems in case more people are present. Our solution is functional in real conditions. We conceived an experiment to estimate precisely its accuracy and evaluate its robustness. The experiment consists of a scenario of daily routine meant to maximize the occupant's motion in meaningful activities. It was performed by 14 subjects, one subject at a time. The results are satisfactory: the system's accuracy exceeds 85% and is independent of the occupant's profile. The system works in real time and behaves well in presence of noise. <s> BIB001 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Background on Sensors Failures in Smart homes and AAL <s> Little of the work in sensor network studies related to data quality has presented a detailed study of sensor faults and fault models. We provide a comprehensive look at sensor network data fault types and a unified basis for describing sensor faults backed up by real world deployment examples. We also identify several considerations one must take into account when developing a fault detection or diagnosis system. We suggest a broad framework of important considerations when developing a data fault detection system and discuss some assumptions that can be made in this context. Based upon experience and previous work we define a series of features to consider when modeling sensor data for either fault detection or fault correction. We define three main headings of types of features and explore the effect that each type has on a sensor. Then we list all common faults that we have observed in actual sensor network deployments. We provide an example of how one may use such a taxonomy of faults. <s> BIB002 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Background on Sensors Failures in Smart homes and AAL <s> Homes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes. <s> BIB003 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Background on Sensors Failures in Smart homes and AAL <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> BIB004 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Background on Sensors Failures in Smart homes and AAL <s> The sensor nodes in wireless sensor networks may be deployed in unattended and possibly hostile environments. The ill-disposed environment affects the monitoring infrastructure that includes the sensor nodes and the network. In addition, node failures and environmental hazards cause frequent topology changes, communication failures, and network partitioning. This in turn adds a new dimension to the fragility of the network topology. Such perturbations are far more common than those found in conventional wireless networks thus, demand efficient techniques for discovering disruptive behavior in such networks. Traditional fault diagnosis techniques devised for multiprocessor systems are not directly applicable to wireless sensor networks due to their specific requirements and limitations. This survey integrates research efforts that have been produced in fault diagnosis specifically for wireless sensor networks. The survey aims at clarifying and uncovering the potential of this technology by providing the technique-based taxonomy. The fault diagnosis techniques are classified based on the nature of the tests, correlation between sensor readings and characteristics of sensor nodes and the network. <s> BIB005 </s> A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living <s> Background on Sensors Failures in Smart homes and AAL <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> BIB006
A fault can be defined as an abnormal event that can cause an element or an item to fail, while a failure is the termination of the ability of an element to perform a function as required  . A fault may or may not lead to failure. For sensor networks in general, two perspectives for fault type classification in sensor networks was proposed by BIB002 : Data-centric viewpoint, which is based on the characteristics of sensor readings, e.g., stuck-at and spike. 2. System-centric viewpoint, which describes faults causing the malfunction of sensor, e.g., low battery and calibration. The authors in BIB005 have presented another three perspectives for classification: 1. Fault-tolerant distributed system viewpoint, that is based on the behaviour of the failed sensor, e.g., crash and omission. 2. Duration viewpoint that classifies faults based on their duration e.g., permanent and intermittent. 3. Components viewpoint, e.g., functional and informational faults. Several fault detection techniques have been developed for sensor networks. However, the techniques were mainly designed for time-driven, continuous-valued and homogeneous sensors, e.g., temperature sensors. Thus, those techniques are not suitable for the event-driven, binary and heterogeneous nature of sensors that are needed for the ambient assisted living, e.g., motion detectors, contact sensors, etc. BIB004 . In an AAL system, a sensor failure is considered to be a fault from the perspective of the whole AAL system. There are two main categories of sensor failures in the AAL terminology: • A fail-stop failure means that the sensor has stopped responding. • A non-fail-stop failure indicates that the sensor is still responding, however, the reported values are no longer representative of the measured variable, nor the occurring events in the surrounding environment that are intended to be detected. Sensor failures can also be classified as single-sensor failures and mutliple-sensor failures. In research works considering single-sensor failures, it is assumed that only one sensor can fail at a time BIB004 . In the field of AAL, Flöck has presented an overview of the binary sensors malfunctions that were observed during practical AAL implementation, e.g., faulty activation of motion detectors by sunlight, bouncing of contact sensors, and switch-off delays of motion sensors  . Also, Rahal et al. have reasoned the false information sent by binary sensors to be either due to an intrinsic error, e.g., the sensor's error rate, or due to an external error, e.g., an air draft or a pet may close the door triggering false events BIB001 . Different types of non-fail-stop failures have been stated in the research papers. Examples of the non-fail-stop failures are: • Moved-location failure, which occurs due to moving furniture that have sensors installed on it to a different area or re-mounting in the wrong location. • Obstructed-view failure that occurs due to covering the sensors or its dislodgement that may result from regular use, cleaning, other non-residents, etc.  BIB006 . A set of guidelines and principles for the deployment of large-scale residential sensing systems was proposed in BIB003 , summarizing the experience gained from installing over 1200 sensors in over 20 homes to monitor human activity. The main failure modes were examined to identify the longest acceptable time interval of inactivity for each sensor. For each periodic sensor, the interval is set to 5 times the sampling period, while for event-driven sensors, it is set to 36 h. The root cause of failure is identified based on the set of simultaneous sensor failures, where the considered causes of failure are wireless link loss, dead battery, disconnected plug, sensor sub-system down, internet-down, power outage, and gateway down. The described failure detection and classification approach was applied on four deployments for seven months. The analysis of the results showed that sensors are 2.3 times more likely to fail due to being unplugged than to dead battery and that wireless link loss is a less cause of failure than the other sources of sensor down time. Failure of an entire sensor sub-system appeared to be the most common cause of failure. This performed failure analysis enabled the authors to present guidelines that could avoid some of the pitfalls and failures observed in the deployments. However, a fault detection and diagnosis system still needs to be implemented to deal efficiently with sensor failures. The following is the most common terminology found in the surveyed literature for the evaluation of various systems;  failure detection latency is the amount of time taken to detect a sensor failure after its occurrence. Figure 1 elaborates the terminology with respect to sensor failure detection systems, where the accuracy, precision and recall values are 85%, 72% and 88%, respectively. The accuracy would still be relatively high if the system does not report as many sensor failures as before (lower TP and higher FN), however, the precision and recall would significantly drop. Thus, only using the accuracy for evaluating the system performance is insufficient. The precision indicates the ratio of the correctly reported sensor failures to all the positively reported sensor failures, while the recall indicates the ratio of correctly reported sensor failures to the positive sensor failures ground truth. 