Abdus Satter; Nabil Ibtehaz

A Regression based Sensor Data Prediction Technique to Analyze Data Trustworthiness in Cyber-Physical System

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Author(s)

1. Institute of Information Technology, University of Dhaka, Dhaka 1000, Bangladesh

2. Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka 1000, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2018.03.03

Received: 10 Nov. 2017 / Revised: 21 Dec. 2017 / Accepted: 8 Jan. 2018 / Published: 8 May 2018

Index Terms

Cyber Physical System, Sensor Data Trustworthiness

Abstract

A Cyber-Physical System strongly depends on the sensor data to understand the current condition of the environment and act on that. Due to network faults, insufficient power supply, and rough environment, sensor data become noisy and the system may perform unwanted operations causing severe damage. In this paper, a technique has been proposed to analyze the trustworthiness of a sensor reading before performing operation based on the record. The technique employs regression analysis to select nearby sensors and develops a linear model for a target sensor. Using the linear model, target sensor reading is predicted in a particular time stamp with respect to each nearby sensor’s reading. If the difference between the predicted and actual value is within a given limit, the reading is considered as trustworthy for the corresponding nearby sensor. At last, majority consensus is taken to consider the reading as trustworthy. To evaluate the proposed technique, a data set containing temperature reading of 8 sensors for 24 hours was used where first 90% data was used for nearby sensor selection and linear model construction, and rest 10% for testing. The result analysis shows that the proposed technique detects 19, 69, and 73 trustworthy data from 73 records with respect to 3%, 4% and 5% deviation from actual reading.

Cite This Paper

Abdus Satter, Nabil Ibtehaz, "A Regression based Sensor Data Prediction Technique to Analyze Data Trustworthiness in Cyber-Physical System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.3, pp. 15-22, 2018. DOI:10.5815/ijieeb.2018.03.03

Reference

[1]Radhakisan Baheti and Helen Gill, “Cyber-physical systems.” The impact of control technology, 12:161–166, 2011.
[2]Edward A Lee, “Cyber physical systems: Design challenges.” In 11th IEEE International Symposium on Object oriented real-time distributed computing (ISORC), 2008, pages 363–369. IEEE, 2008.
[3]Xu Jin, Wassim M Haddad, and Tansel Yucelen, “An adaptive control architecture for mitigating sensor and actuator attacks in cyber-physical systems.” IEEE Transactions on Automatic Control, 2017.
[4]Xu Jin, Wassim M Haddad, and Tansel Yucelen, “Adaptive control architecture for mitigating sensor and actuator attacks in cyber-physical systems.” IEEE Transactions on Automatic Control, 2017.
[5]Wenjia Li, Pramod Jagtap, Laura Zavala, Anupam Joshi, and Tim Fin, “Care-cps: Context-aware trust evaluation for wireless networks in cyber-physical system using policies.” In IEEE International Symposium on Policies for Distributed Systems and Networks (POLICY), pages 171–172. IEEE, 2011.
[6]Yin Zhang, Meikang Qiu, Chun-Wei Tsai, Mohammad Mehedi Hassan, and Atif Alamri, “Health-cps: Healthcare cyber-physical system assisted by cloud and big data.” IEEE Systems Journal, 11(1):88–95, 2017.
[7]Lu-An Tang, Xiao Yu, Sangkyum Kim, Jiawei Han, Chih-Chieh Hung, and Wen-Chih Peng, “Tru-alarm: Trustworthiness analysis of sensor networks in cyber-physical systems.” In 10th International Conference on Data Mining (ICDM), pages 1079–1084. IEEE, 2010.
[8]Kevin Ni and Greg Pottie, “Bayesian selection of non-faulty sensors.” In International Symposium on Information Theory, pages 616–620. IEEE, 2007.
[9]Hyo-Sang Lim, Gabriel Ghinita, Elisa Bertino, and Murat Kantarcioglu, “A game-theoretic approach for high-assurance of data trustworthiness in sensor networks.” In 28th International Conference on Data Engineering (ICDE), pages 1192–1203. IEEE, 2012
[10]Lu-An Tang, Quanquan Gu, Xiao Yu, Jiawei Han, Thomas La Porta,Alice Leung, Tarek Abdelzaher, and Lance Kaplan, “Intrumine: Mining intruders in untrustworthy data of cyber-physical systems.” In 2012 SIAM International Conference on Data Mining, pages 600–611. SIAM, 2012
[11]Adrian Perrig, Robert Szewczyk, Justin Douglas Tygar, Victor Wen, and David E Culler, “Spins: Security protocols for sensor networks.” Wireless networks, 8(5):521–534, 2002.
[12]Mark Luk, Ghita Mezzour, Adrian Perrig, and Virgil Gligor, “Minisec: a secure sensor network communication architecture.” In 6th International Conference on Information Processing in Sensor Networks, pages 479–488. ACM, 2007.
[13]Liying Zhang, Lun Xie, Weize Li, and Zhiliang Wang, “Security solutions for networked control systems based on des algorithm and improved grey prediction model.” .International Journal of Computer Network and Information Security, 6(1):78, 2013.
[14]G. Shanmugasundaram, and G. Sankarikaarguzhali, “An Investigation on IoT Healthcare Analytics.” International Journal of Information Engineering and Electronic Business 9(2):11, 2017.

International Journal of Information Engineering and Electronic Business (IJIEEB)