Detecting and Identifying Faulty IoT Devices in Smart Home with Context Extraction

Choi, Jiwon; Jeoung, Hayoung; Kim, Jihun; Ko, Youngjoo; Jung, Wonup; Kim, Hanjun; Kim, Jong

doi:10.1109/dsn.2018.00068

Cited by 65 publications

(41 citation statements)

References 21 publications

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“…Among several devices installed at smart homes frequently, we may face some failures due to electronics malfunctions or software bugs. The authors in [26] discussed the possibility of self-estimation in intelligent appliances. The more devices would be able to diagnose themselves the better efficiency of the smart home would be.…”

Section: B General Ideas For the Developmentmentioning

confidence: 99%

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

et al. 2021

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The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry. INDEX TERMS Internet of Things (IoT), smart home, healthcare, energy management, security. I. INTRODUCTION O VER time, the type of home was evolving. The shape of the house was evolving during ages, cultural revolutions, and climate changes. We have divided our homes into rooms devoted to sleeping, cooking, work, children, sanitary needs, warehousing, etc. People started to settle in various locations and continents.

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Section: B General Ideas For the Developmentmentioning

confidence: 99%

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

et al. 2021

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“…In order to make this technique, it needs to be devised into two stages which are training and experimenting [38]. In the training phase, the technique aims at learning a classifier using the available labelled training data followed by an experimenting phase that classifies a test instance as regular or an outlier or a sensor fault [39,40].…”

Section: Classification-based Techniquesmentioning

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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“…These faults in IoT surroundings cannot be accepted because it can cause many terrible problems. Thus, keeping the integrity of the information is a vital necessity in IoT applications [3]. Because of this, many existing faulty detection mechanisms have been proposed to detect faulty sensors for IoT using several techniques and methods, and one of them is using correlation values between actuators or sensors.…”

Section: Previous Workmentioning

confidence: 99%

Implementation of Faulty Sensor Detection Mechanism using Data Correlation of Multivariate Sensor Readings in Smart Agriculture

Malik¹,

Jamil²,

Omar³

et al. 2021

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Through sensor networks, agriculture can be connected to the IoT, which allows us to create connections among agronomists, farmers, and crops regardless of their geographical differences. Faulty sensor detection is critical in IoT. When a sensor becomes faulty, missing data and/or bad data is provided to the control and management systems, which may lead to potential malfunction or even system failures. Because of this, a sensor fault detection mechanism must be implemented in an IoT system to eliminate this potential fault. This paper focuses on the implementation of a faulty sensor detection mechanism using data correlation among multivariate sensor readings, which is called Multivariate Faulty Sensor Detection Mechanism (Multi-FSDM) in a smart agriculture system. The smart agriculture system is attached with multi-variate sensors, which are moisture, temperature, and water sensor. These sensors are connected to Arduino UNO, which is equipped with an ESP8266 Wi-Fi module for internet connectivity. ThingsBoard is selected as the IoT cloud platform. The sensor readings are collected periodically and send to the cloud via the internet. Multi-FSDM calculates the correlation between each sensor reading to determine the health condition of each sensor. When all sensors are in good condition, all sensor readings are correlated with each other. However, when any sensor becomes faulty, sensor readings become uncorrelated. Once uncorrelated sensor readings occur, this means a faulty sensor is detected. Based on the findings, it is proven that Multi-FSDM can detect each sensor state on the smart agriculture system either in a good or faulty condition. When a sensor becomes faulty, Multi-FSDM detects and determines the faulty sensor successfully.

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Detecting and Identifying Faulty IoT Devices in Smart Home with Context Extraction

Cited by 65 publications

References 21 publications

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

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

Implementation of Faulty Sensor Detection Mechanism using Data Correlation of Multivariate Sensor Readings in Smart Agriculture

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