Anomaly Detection Models for Detecting Sensor Faults and Outliers in the IoT - A Survey

Over the past few years, the Internet of Things (IoT) has gained significant recognition to become a novel sensing paradigm to interact with the physical world. The sensors within the Internet of Things are indispensable parts and are the first port to capture the raw data. As the sensors within IoT are usually deployed in environments which are harsh, which inevitably make the sensors venerable to failure and malfunction. Beside sensor faults and malfunctions, the inherent environment where the sensors are usually installed could also make the sensor to fail prematurely. These conditions will make the sensors within the IoT to generate unusual and erroneous data, often known as outliers. Outliers detection is very crucial in IoT to detect the high probability of erroneous reading or data corruption, thereby ensuring the quality of the data collected by sensors. Data anomalies, abnormal data or outliers are considered to be the sensor data streams that are significantly distinct from the normal behavioural data. In this paper, we present a comprehensive survey that can be used as a guideline to select which outlier model is adequate for the application in the IoT context.

[1]  Mohamed Abid,et al.  An overview of outlier detection technique developed for wireless sensor networks , 2013, 10th International Multi-Conferences on Systems, Signals & Devices 2013 (SSD13).

[2]  Kire Trivodaliev,et al.  A review of Internet of Things for smart home: Challenges and solutions , 2017 .

[3]  Biming Tian,et al.  Anomaly detection in wireless sensor networks: A survey , 2011, J. Netw. Comput. Appl..

[4]  Miao Xie,et al.  Anomaly Detection in Wireless Sensor Networks , 2013 .

[5]  Wanyi Gu,et al.  Design of K-Node (Edge) Content Connected Optical Data Center Networks , 2016, IEEE Communications Letters.

[6]  Matt Welsh,et al.  LiveNet: Using Passive Monitoring to Reconstruct Sensor Network Dynamics , 2008, DCOSS.

[7]  Wu He,et al.  Internet of Things in Industries: A Survey , 2014, IEEE Transactions on Industrial Informatics.

[8]  Ahmad Lotfi,et al.  Behavioural Pattern Identification in a Smart Home Using Binary Similarity and Dissimilarity Measures , 2011, 2011 Seventh International Conference on Intelligent Environments.

[9]  Sirajum Munir,et al.  FailureSense: Detecting Sensor Failure Using Electrical Appliances in the Home , 2014, 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.

[10]  M. Mehdi Afsar,et al.  Clustering in sensor networks: A literature survey , 2014, J. Netw. Comput. Appl..

[11]  Mohamed Abid,et al.  Improved KPCA for outlier detection in Wireless Sensor Networks , 2014, 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[12]  Xiuzhen Cheng,et al.  Localized fault-tolerant event boundary detection in sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[13]  Jiwon Choi,et al.  Detecting and Identifying Faulty IoT Devices in Smart Home with Context Extraction , 2018, 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).

[14]  David Kim,et al.  A Bayesian network-based approach for fault analysis , 2017, Expert Syst. Appl..

[15]  Haider Abbas,et al.  Internet of things (IoT) design considerations for developers and manufacturers , 2017, 2017 IEEE International Conference on Communications Workshops (ICC Workshops).

[16]  Andrea Castillo O'Sullivan,et al.  Projecting the Growth and Economic Impact of the Internet of Things , 2015 .

[17]  Sandeep Sharma,et al.  Anomaly Detection in Medical Wireless Sensor Networks using Machine Learning Algorithms , 2015 .

[18]  Mudhakar Srivatsa,et al.  Idea: A System for Efficient Failure Management in Smart IoT Environments , 2016, MobiSys.

[19]  V M van Zoest,et al.  Outlier Detection in Urban Air Quality Sensor Networks , 2018, Water, Air, & Soil Pollution.

[20]  Yixiong Feng,et al.  A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things , 2018, Sensors.

[21]  Min Chen,et al.  Outlier detection and countermeasure for hierarchical wireless sensor networks , 2010, IET Inf. Secur..

[22]  Jacques Bughin,et al.  An executive ’ s guide to the Internet of Things , 2022 .

[23]  Mumbai,et al.  Internet of Things (IoT): A Literature Review , 2015 .

[24]  Yozo Hida,et al.  Aggregation Query Under Uncertainty in Sensor Networks CS 252 Project , 2003 .

[25]  E. Balaban,et al.  Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications , 2009, IEEE Sensors Journal.

[26]  Simon A. Dobson,et al.  Unifying Sensor Fault Detection with Energy Conservation , 2013, IWSOS.

[27]  Simon A. Dobson,et al.  Detecting abnormal events on binary sensors in smart home environments , 2016, Pervasive Mob. Comput..

[28]  Chen Wang,et al.  An IoT Application for Fault Diagnosis and Prediction , 2015, 2015 IEEE International Conference on Data Science and Data Intensive Systems.

[29]  Jyrki Kullaa,et al.  Detection, identification, and quantification of sensor fault in a sensor network , 2013 .

[30]  Mohiuddin Ahmed,et al.  A survey of network anomaly detection techniques , 2016, J. Netw. Comput. Appl..

[31]  Wael Guibène,et al.  An evaluation of low power wide area network technologies for the Internet of Things , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[32]  Ravindra Navanath Duche,et al.  Sensor Node Failure Detection Based on Round Trip Delay and Paths in WSNs , 2014, IEEE Sensors Journal.

[33]  Soma Bandyopadhyay,et al.  IoT Healthcare Analytics: The Importance of Anomaly Detection , 2016, 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA).

[34]  Mohamed Abid,et al.  Outlier detection approaches for wireless sensor networks: A survey , 2017, Comput. Networks.

[35]  K. K. Pattanaik,et al.  Contextual outlier detection for wireless sensor networks , 2020, J. Ambient Intell. Humaniz. Comput..

[36]  Gregory J. Pottie,et al.  Sensor network data fault types , 2007, TOSN.

[37]  In Lee,et al.  The Internet of Things (IoT): Applications, investments, and challenges for enterprises , 2015 .

[38]  Kamin Whitehouse,et al.  The hitchhiker's guide to successful residential sensing deployments , 2011, SenSys.

[39]  Ran Wolff,et al.  Noname manuscript No. (will be inserted by the editor) In-Network Outlier Detection in Wireless Sensor Networks , 2022 .

[40]  Min Chen,et al.  A Survey on Internet of Things From Industrial Market Perspective , 2015, IEEE Access.

[41]  Sang Hyuk Son,et al.  Being SMART about failures: assessing repairs in SMART homes , 2012, UbiComp.