Detecting abnormal events on binary sensors in smart home environments

Ye, Juan; Stevenson, Graeme; Dobson, Simon

doi:10.1016/j.pmcj.2016.06.012

Cited by 36 publications

(35 citation statements)

References 35 publications

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“…• Intrinsic Sensor Errors: This kind of error is associated with impaired readings or measurements coming from a faulty sensor which is embedded within an IoT device. As the sensors are electronic components, they often fail suddenly and stop working without any indication of degrading performance [26][27][28]. This kind of sensor failure feeds in either no readings or null readings to the data processing algorithm within the IoT system [23].…”

Section: Main Sources For Sensor Outliers That Are Relevant To the Iomentioning

confidence: 99%

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

et al. 2020

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The Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world in this Industry 4.0 era. The IoTs are being used in many diverse applications that are part of our life and is growing to become the global digital nervous systems. It is quite evident that in the near future, hundreds of millions of individuals and businesses with billions will have smart-sensors and advanced communication technology, and these things will expand the boundaries of current systems. This will result in a potential change in the way we work, learn, innovate, live and entertain. The heterogeneous smart sensors within the Internet of Things are indispensable parts, which capture the raw data from the physical world by being the first port of contact. Often the sensors within the IoT are deployed or installed in harsh environments. This inevitably means that the sensors are prone to failure, malfunction, rapid attrition, malicious attacks, theft and tampering. All of these conditions cause the sensors within the IoT to produce unusual and erroneous readings, often known as outliers. Much of the current research has been done in developing the sensor outlier and fault detection models exclusively for the Wireless Sensor Networks (WSN), and adequate research has not been done so far in the context of the IoT. Wireless sensor network’s operational framework differ greatly when compared to IoT’s operational framework, using some of the existing models developed for WSN cannot be used on IoT’s for detecting outliers and faults. Sensor faults and outlier detection is very crucial in the IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. The data collected by sensors are initially pre-processed to be transformed into information and when Artificially Intelligent (AI), Machine Learning (ML) models are further used by the IoT, the information is further processed into applications and processes. Any faulty, erroneous, corrupted sensor readings corrupt the trained models, which thereby produces abnormal processes or outliers that are significantly distinct from the normal behavioural processes of a system. In this paper, we present a comprehensive review of the detecting sensor faults, anomalies, outliers in the Internet of Things and the challenges. A comprehensive guideline to select an adequate outlier detection model for the sensors in the IoT context for various applications is discussed.

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Section: Main Sources For Sensor Outliers That Are Relevant To the Iomentioning

confidence: 99%

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

et al. 2020

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“…The categorization of the sensors is performed according to daily activities. Ye et al [22] proposed a novel technique known as CLEAN wherein the sensor data is checked for abnormalities using statistical techniques. Skocir et al [23] proposed an approach in which data from two different sensors are used to detect opening and closing.…”

Section: Related Workmentioning

confidence: 99%

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Grewal

Kaur

Park

2019

Wireless Communications and Mobile Computing

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With the revolution in smart infrastructure in the recent past, the smart healthcare system has been paid more considerable attention. The continuous upgradation of electricity meters to smart electricity devices has probed into a new market of intelligent data analysis services, providing aid to the health care systems. This paper presents a unified framework for extracting user behaviour patterns from home-based smart electricity meter data. The structure allows exploration and integration of frequent pattern growth algorithm for pattern mining and application of a variety of machine learning algorithms for categorizing the activities into manually labelled classes along with the implementation of Local Outlier Factor method for detection of an abnormal pattern of the inhabitant of smart homes. To evaluate the proposed framework, the work is implemented on the smart electricity dataset from the United Kingdom by separating the data into four distinct data files meant for the morning, afternoon, evening, and night energy utilization records. The results show a remarkable performance of Support Vector Machine (SVM) and Multilayer Perceptron (MLP) classifiers with kappa statics greater than 0.95 for all time slots data. The resultant frequent device utilization patterns with anomaly score more than the threshold value, reflecting abnormal activity patterns, are found more in evening time data in comparison to other time slots, requiring the immediate attention of concerned healthcare authorities.

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“…CASAS (Centre for Advanced Studies in Adaptive Systems) is a project run by the University Of Washington State which collects data of people from sensors placed in an apartment that stores the data in files, uses algorithms and techniques to extract patterns and predictions of trends in a smart home [10,11]. This dataset has been widely used in the literature of the human activity recognition field [12].…”

Section: B Related Workmentioning

confidence: 99%

Sensors in Smart Homes for Independent Living of the Elderly

Pirzada

White

Wilde

2018

2018 5th International Multi-Topic ICT Conference (IMTIC)

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a rapidly ageing population requires support systems which would enable them to preserve dwellers' independence without compromising on their safety or their quality of life. Smart homes for the elderly have the potential to offer unobtrusive health and wellness monitoring. The aim is to provide a safe, independent living environment which can identify and predict problems by monitoring the activities of daily living (ADLs) of the inhabitants. For this, a system able to handle continuous streams of data is required. Such a system can extract the information by using appropriate classification and learning algorithms and thus allow the remote monitoring of health and wellbeing at a high level. The implementation requires: the use of appropriate sensing technologies, identification of ADLs, data pre-processing techniques and machine learning algorithms. This is challenging due to individual differences: such a system must be able to personalize individual needs. Our contribution was the design and implementation of a platform to smartly monitor health condition of elderly using sensor data from a smart home, through an interactive user interface which is user-friendly and multiplatform. This proof-of-concept used off-line data, with the view to extend to real-time data collection in the future, which could then be used to inform support providers remotely.

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Detecting abnormal events on binary sensors in smart home environments

Cited by 36 publications

References 35 publications

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

Detecting Sensor Faults, Anomalies and Outliers in the Internet of Things: A Survey on the Challenges and Solutions

A Unified Framework for Behaviour Monitoring and Abnormality Detection for Smart Home

Sensors in Smart Homes for Independent Living of the Elderly

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