research-article

Fall-curve: A novel primitive for IoT Fault Detection and Isolation

Authors:

Tusher Chakraborty,

Akshay Uttama Nambi,

Ranveer Chandra,

Manohar Swaminathan,

Zerina Kapetanovic,

Jonathan AppavooAuthors Info & Claims

SenSys '18: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

Pages 95 - 107

https://doi.org/10.1145/3274783.3274853

Published: 04 November 2018 Publication History

Abstract

The proliferation of Internet of Things (IoT) devices has led to the deployment of various types of sensors in the homes, offices, buildings, lawns, cities, and even in agricultural farms. Since IoT applications rely on the fidelity of data reported by the sensors, it is important to detect a faulty sensor and isolate the cause of the fault. Existing fault detection techniques demand sensor domain knowledge along with the contextual information and historical data from similar near-by sensors. However, detecting a sensor fault by analyzing just the sensor data is non-trivial since a faulty sensor reading could mimic non-faulty sensor data. This paper presents a novel primitive, which we call the Fall-curve - a sensor's voltage response when the power is turned off - that can be used to characterize sensor faults. The Fall-curve constitutes a unique signature independent of the phenomenon being monitored which can be used to identify the sensor and determine whether the sensor is correctly operating.

We have empirically evaluated the Fall-curve technique on a wide variety of analog and digital sensors. We have also been running this system live in a few agricultural farms, with over 20 IoT devices. We were able to detect and isolate faults with an accuracy over 99%, which would have otherwise been hard to detect only by observing measured sensor data.

References

[1]

Lady Ada. 2018. PIR Motion Sensor. (2018). Retrieved Sep 16, 2018 from https://goo.gl/gvF93e

[2]

Alphasense. 2018. Alphasense sensors. (2018). Retrieved May 27, 2018 from https://goo.gl/ivXVes

[3]

Arduino. 2018. Arduino Uno. (2018). Retrieved April 4, 2018 from https://goo.gl/pDxdRv

[4]

Laura Balzano and Robert Nowak. 2007. Blind calibration of sensor networks. In Proceedings of the 6th international conference on Information processing in sensor networks. ACM, 79--88.

Digital Library

[5]

Vladimir Bychkovskiy, Seapahn Megerian, Deborah Estrin, and Miodrag Potkonjak. 2003. A collaborative approach to in-place sensor calibration. In Information Processing in Sensor Networks. Springer, 301--316.

Digital Library

[6]

Vassilis Chatzigiannakis and Symeon Papavassiliou. 2007. Diagnosing anomalies and identifying faulty nodes in sensor networks. IEEE Sensors Journal 7, 5 (2007), 637--645.

[7]

Li Chen, Srivaths Ravi, Anand Raghunathan, and Sujit Dey. 2003. A scalable software-based self-test methodology for programmable processors. In Proceedings of the 40th annual Design Automation Conference. ACM, 548--553.

Digital Library

[8]

Amphenol Corporation. 2014. Telaire T6713 Series CO2 Module. (2014). Retrieved Sep 16, 2018 from https://goo.gl/2CGciZ

[9]

Daniel-Ioan Curiac and Constantin Volosencu. 2012. Ensemble based sensing anomaly detection in wireless sensor networks. Expert Systems with Applications 39, 10 (2012), 9087--9096.

Digital Library

[10]

Amol Deshpande, Carlos Guestrin, Samuel R Madden, Joseph M Hellerstein, and Wei Hong. 2004. Model-driven data acquisition in sensor networks. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. VLDB Endowment, 588--599.

Digital Library

[11]

DFRobot. 2012. Gravity: Analog Capacitive Soil Moisture Sensor- Corrosion Resistant. (2012). Retrieved April 4, 2018 from https://goo.gl/p5bhFK

[12]

Bill Earl. 2018. ADXL345 Digital Accelerometer. (2018). Retrieved Sep 16, 2018 from https://goo.gl/96X6fs

[13]

Eiman Elnahrawy and Badri Nath. 2003. Cleaning and querying noisy sensors. In Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications. ACM, 78--87.

Digital Library

[14]

Chirag Gupta, Arun Sai Suggala, Ankit Goyal, Harsha Vardhan Simhadri, Bhargavi Paranjape, Ashish Kumar, Saurabh Goyal, Raghavendra Udupa, Manik Varma, and Prateek Jain. 2017. ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Vol. 70. PMLR, International Convention Centre, Sydney, Australia, 1331--1340.

[15]

Texas Instruments. 2013. Tiva TM4C123G Launchpad. (April 2013). Retrieved April 4, 2018 from https://goo.gl/GX1itw

[16]

Shawn R Jeffery, Gustavo Alonso, Michael J Franklin, Wei Hong, and Jennifer Widom. 2006. Declarative support for sensor data cleaning. In International Conference on Pervasive Computing. Springer, 83--100.

Digital Library

[17]

Nodira Khoussainova, Magdalena Balazinska, and Dan Suciu. 2006. Towards correcting input data errors probabilistically using integrity constraints. In Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access. ACM, 43--50.

Digital Library

[18]

Farinaz Koushanfar, Miodrag Potkonjak, and Alberto Sangiovanni-Vincentelli. 2003. On-line fault detection of sensor measurements. In Sensors, 2003. Proceedings of IEEE, Vol. 2. IEEE, 974--979.

[19]

Bhaskar Krishnamachari and Sitharama Iyengar. 2004. Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks. IEEE Trans. Comput. 53, 3 (2004), 241--250.

Digital Library

[20]

Mohammad Ahamdi Livani and Mahdi Abadi. 2010. Distributed PCA-based anomaly detection in wireless sensor networks. In Internet Technology and Secured Transactions (ICITST), 2010 International Conference for. IEEE, 1--8.

[21]

LTspice 2018. LTspice. (2018). Retrieved May 27, 2018 from http://www.analog.com/en/design-center/design-tools-and-calculators/ltspice-simulator.html

[22]

MQ-135 2018. MQ-135 Carbon Monoxide Sensor Technical datasheet. (2018). https://goo.gl/PKkn1G.

[23]

MQ-7 Gas Sensor 2018. MQ-7 Gas Sensor Technical datasheet. (2018). https://goo.gl/MfhBLJ.

[24]

Shoubhik Mukhopadhyay, Debashis Panigrahi, and Sujit Dey. 2004. Model based error correction for wireless sensor networks. In Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004. 2004 First Annual IEEE Communications Society Conference on. IEEE, 575--584.

[25]

Dejan Nedelkovski. 2016. DHT11 & DHT22 Sensors Temperature and Humidity Tutorial using Arduino. (2016). Retrieved April 4, 2018 from https://goo.gl/YvroSj

[26]

Kevin Ni and Greg Pottie. 2007. Bayesian selection of non-faulty sensors. In Information Theory, 2007. ISIT 2007. IEEE International Symposium on. IEEE, 616--620.

[27]

Kevin Ni, Nithya Ramanathan, Mohamed Nabil Hajj Chehade, Laura Balzano, Sheela Nair, Sadaf Zahedi, Eddie Kohler, Greg Pottie, Mark Hansen, and Mani Srivastava. 2009. Sensor network data fault types. ACM Transactions on Sensor Networks (TOSN) 5, 3 (2009), 25.

Digital Library

[28]

Colin O'Reilly, Alexander Gluhak, Muhammad Ali Imran, and Sutharshan Rajasegarar. 2014. Anomaly detection in wireless sensor networks in a non-stationary environment. IEEE Communications Surveys & Tutorials 16, 3 (2014), 1413--1432.

[29]

Bob Orwiler. 1969. Vertical Amplifier Circuits. Tektronix, Inc., Beaverton, Oregon.

[30]

Precision and recall 2018. Precision and recall. (2018). https://en.wikipedia.org/wiki/Precision_and_recall.

[31]

Sutharshan Rajasegarar, Christopher Leckie, James C Bezdek, and Marimuthu Palaniswami. 2010. Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Transactions on Information Forensics and Security 5, 3 (2010), 518--533.

Digital Library

[32]

Nithya Ramanathan, Laura Balzano, Marci Burt, Deborah Estrin, Tom Harmon, Charlie Harvey, Jenny Jay, Eddie Kohler, Sarah Rothenberg, and Mani Srivastava. 2006. Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks. (2006).

[33]

Nithya Ramanathan, Tom Schoellhammer, Deborah Estrin, Mark Hansen, Tom Harmon, Eddie Kohler, and Mani Srivastava. 2006. The final frontier: Embedding networked sensors in the soil. (2006).

[34]

Abhishek B Sharma, Leana Golubchik, and Ramesh Govindan. 2010. Sensor faults: Detection methods and prevalence in real-world datasets. ACM Transactions on Sensor Networks (TOSN) 6, 3 (2010), 23.

Digital Library

[35]

Bo Sheng, Qun Li, Weizhen Mao, and Wen Jin. 2007. Outlier detection in sensor networks. In Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing. ACM, 219--228.

Digital Library

[36]

SparkFun 2018. SparkFun Soil Moisture Sensor. (2018). Retrieved May 27, 2018 from https://www.sparkfun.com/products/13322

[37]

Sharmila Subramaniam, Themis Palpanas, Dimitris Papadopoulos, Vana Kalogeraki, and Dimitrios Gunopulos. 2006. Online outlier detection in sensor data using non-parametric models. In Proceedings of the 32nd international conference on Very large data bases. VLDB Endowment, 187--198.

Digital Library

[38]

Gilman Tolle, Joseph Polastre, Robert Szewczyk, David Culler, Neil Turner, Kevin Tu, Stephen Burgess, Todd Dawson, Phil Buonadonna, David Gay, et al. 2005. A macroscope in the redwoods. In Proceedings of the 3rd international conference on Embedded networked sensor systems. ACM, 51--63.

Digital Library

[39]

Daniela Tulone and Samuel Madden. 2006. PAQ: Time series forecasting for approximate query answering in sensor networks. In European Workshop on Wireless Sensor Networks. Springer, 21--37.

Digital Library

[40]

Inc. Vegetronix. 2014. Soil Temperature Sensor Probes. (2014). Retrieved April 4, 2018 from https://goo.gl/iot1YW

[41]

Inc. Vegetronix. 2014. VH400 Soil Moisture Sensor Probes. (2014). Retrieved April 4, 2018 from https://goo.gl/tDTh95

[42]

Miao Xie, Jiankun Hu, Song Han, and Hsiao-Hwa Chen. 2013. Scalable hyper-grid k-NN-based online anomaly detection in wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems 24, 8 (2013), 1661--1670.

Digital Library

[43]

Zhou Yong. 2016. Digital universal particle concentration sensor. (2016). Retrieved Sep 16, 2018 from https://goo.gl/LvJJKz

[44]

Yang Zhang, Nirvana Meratnia, and Paul JM Havinga. 2013. Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine. Ad hoc networks 11, 3 (2013), 1062--1074.

Digital Library

Cited By

Bandarupalli ABhat AChaterji SReiter MKate ABagchi S(2024)SensorBFT: Fault-Tolerant Target Localization Using Voronoi Diagrams and Approximate Agreement2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00026(186-197)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00026
Gupta THanda SNambi A(2023)Verified Telemetry: A General, Easy to use, Scalable and Robust Fault Detection SDK for IoT SensorsProceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation10.1145/3576842.3582386(381-395)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576842.3582386
Yoon JMoon ASon S(2023)Outlier Elimination and Reliability Assessment for Peak and Declining Time Series Datasets2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00083(593-600)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00083
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Index Terms

Fall-curve: A novel primitive for IoT Fault Detection and Isolation
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Hardware
  1. Robustness
    1. Fault tolerance

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cover image ACM Conferences

SenSys '18: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

November 2018

449 pages

ISBN:9781450359528

DOI:10.1145/3274783

Editors:
Gowri Sankar Ramachandran
University of Southern California, Los Angeles
,
Bhaskar Krishnamachari
University of Southern California, Los Angeles

Copyright © 2018 ACM.

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Published: 04 November 2018

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SenSys '18: The 16th ACM Conference on Embedded Networked Sensor Systems

November 4 - 7, 2018

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Cited By

Bandarupalli ABhat AChaterji SReiter MKate ABagchi S(2024)SensorBFT: Fault-Tolerant Target Localization Using Voronoi Diagrams and Approximate Agreement2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00026(186-197)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00026
Gupta THanda SNambi A(2023)Verified Telemetry: A General, Easy to use, Scalable and Robust Fault Detection SDK for IoT SensorsProceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation10.1145/3576842.3582386(381-395)Online publication date: 9-May-2023
https://dl.acm.org/doi/10.1145/3576842.3582386
Yoon JMoon ASon S(2023)Outlier Elimination and Reliability Assessment for Peak and Declining Time Series Datasets2023 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW60847.2023.00083(593-600)Online publication date: 4-Dec-2023
https://doi.org/10.1109/ICDMW60847.2023.00083
Attarha SBand SFörster A(2023)Automated Fault Detection Framework for Reliable Provision of IoT Applications in Agriculture2023 19th International Conference on the Design of Reliable Communication Networks (DRCN)10.1109/DRCN57075.2023.10108238(1-8)Online publication date: 17-Apr-2023
https://doi.org/10.1109/DRCN57075.2023.10108238
Guittoum AAïssaoui FBolle SBoyer FDe Palma N(2023)Solving the IoT Cascading Failure Dilemma Using a Semantic Multi-agent SystemThe Semantic Web – ISWC 202310.1007/978-3-031-47243-5_18(325-344)Online publication date: 27-Oct-2023
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Tsotsolas NKomisopoulos FPapadopoulos PKoutsouraki E(2022)An Integrated LoRa-Based IoT Platform Serving Smart Farming and Agro-LogisticsEmerging Ecosystem-Centric Business Models for Sustainable Value Creation10.4018/978-1-7998-4843-1.ch006(132-158)Online publication date: 2022
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Vaidya GPrabhakar T(2022)Hardware based identification for Intelligent Electronic Devices2022 IEEE/ACM Seventh International Conference on Internet-of-Things Design and Implementation (IoTDI)10.1109/IoTDI54339.2022.00018(82-94)Online publication date: May-2022
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Gou LZhang JLi N(2022)Knowledge representation and decoupling analysis on failure mechanisms of remotely controlled intelligent machineryInformation Processing in Agriculture10.1016/j.inpa.2020.11.0069:1(80-89)Online publication date: Mar-2022
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Silva DHeideker AZyrianoff IKleinschmidt JRoffia LSoininen JKamienski C(2022)A Management Architecture for IoT Smart Solutions: Design and ImplementationJournal of Network and Systems Management10.1007/s10922-022-09648-630:2Online publication date: 30-Jan-2022
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