Tusher Chakraborty
Akshay Uttama Nambi
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.
- Lady Ada. 2018. PIR Motion Sensor. (2018). Retrieved Sep 16, 2018 from https://goo.gl/gvF93eGoogle Scholar
- Alphasense. 2018. Alphasense sensors. (2018). Retrieved May 27, 2018 from https://goo.gl/ivXVesGoogle Scholar
- Arduino. 2018. Arduino Uno. (2018). Retrieved April 4, 2018 from https://goo.gl/pDxdRvGoogle Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Vassilis Chatzigiannakis and Symeon Papavassiliou. 2007. Diagnosing anomalies and identifying faulty nodes in sensor networks. IEEE Sensors Journal 7, 5 (2007), 637--645.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- Amphenol Corporation. 2014. Telaire T6713 Series CO2 Module. (2014). Retrieved Sep 16, 2018 from https://goo.gl/2CGciZGoogle Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- DFRobot. 2012. Gravity: Analog Capacitive Soil Moisture Sensor- Corrosion Resistant. (2012). Retrieved April 4, 2018 from https://goo.gl/p5bhFKGoogle Scholar
- Bill Earl. 2018. ADXL345 Digital Accelerometer. (2018). Retrieved Sep 16, 2018 from https://goo.gl/96X6fsGoogle Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- Texas Instruments. 2013. Tiva TM4C123G Launchpad. (April 2013). Retrieved April 4, 2018 from https://goo.gl/GX1itwGoogle Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google Scholar
- LTspice 2018. LTspice. (2018). Retrieved May 27, 2018 from http://www.analog.com/en/design-center/design-tools-and-calculators/ltspice-simulator.htmlGoogle Scholar
- MQ-135 2018. MQ-135 Carbon Monoxide Sensor Technical datasheet. (2018). https://goo.gl/PKkn1G.Google Scholar
- MQ-7 Gas Sensor 2018. MQ-7 Gas Sensor Technical datasheet. (2018). https://goo.gl/MfhBLJ.Google Scholar
- 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.Google ScholarCross Ref
- Dejan Nedelkovski. 2016. DHT11 & DHT22 Sensors Temperature and Humidity Tutorial using Arduino. (2016). Retrieved April 4, 2018 from https://goo.gl/YvroSjGoogle Scholar
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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.Google Scholar
- Bob Orwiler. 1969. Vertical Amplifier Circuits. Tektronix, Inc., Beaverton, Oregon.Google Scholar
- Precision and recall 2018. Precision and recall. (2018). https://en.wikipedia.org/wiki/Precision_and_recall.Google Scholar
- 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. Google ScholarDigital Library
- 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).Google Scholar
- 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).Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- SparkFun 2018. SparkFun Soil Moisture Sensor. (2018). Retrieved May 27, 2018 from https://www.sparkfun.com/products/13322Google Scholar
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Inc. Vegetronix. 2014. Soil Temperature Sensor Probes. (2014). Retrieved April 4, 2018 from https://goo.gl/iot1YWGoogle Scholar
- Inc. Vegetronix. 2014. VH400 Soil Moisture Sensor Probes. (2014). Retrieved April 4, 2018 from https://goo.gl/tDTh95Google Scholar
- 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. Google ScholarDigital Library
- Zhou Yong. 2016. Digital universal particle concentration sensor. (2016). Retrieved Sep 16, 2018 from https://goo.gl/LvJJKzGoogle Scholar
- 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. Google ScholarDigital Library
Index Terms
- Fall-curve: A novel primitive for IoT Fault Detection and Isolation
Recommendations
Sensor Identification and Fault Detection in IoT Systems
SenSys '18: Proceedings of the 16th ACM Conference on Embedded Networked Sensor SystemsThe 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. Due to the diverse nature of IoT deployments and the likelihood ...
Modeling and Analysis of Fault Detection and Fault Tolerance in Wireless Sensor Networks
Technological advancements in communications and embedded systems have led to the proliferation of Wireless Sensor Networks (WSNs) in a wide variety of application domains. These application domains include but are not limited to mission-critical (e.g., ...
Fault tolerant control using a fuzzy predictive approach
This paper proposes the application of fault-tolerant control (FTC) using fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based ...
Comments