Email this article 
Open Access
Subscription or Fee Access
Recognizing the Sensor Data in Cyber-Physical Systems
Abstract
A Cyber-Physical System (CPS) coordinates physical gadgets (i.e., sensors) with digital (i.e., enlightening) parts to structure a setting touchy framework that reacts adroitly to element changes in genuine circumstances. Such a framework has wide applications in the situations of activity control, front line observation, natural observing, et cetera. A center component of CPS is the accumulation and evaluation of data from loud, dynamic, and indeterminate physical situations incorporated with numerous sorts of the internet assets. The capability of this incorporation is unbounded. To attain this potential the crude information gained from the physical world must be changed into useable information continuously. In this way, CPS brings another measurement to learning revelation due to the rising synergism of the physical and the digital. The different properties of the physical world must be tended to in data administration and information revelation. This paper talks about the issues of mining sensor information in CPS: With an extensive number of remote sensors sent in an assigned region, the errand is continuous recognition of interlopers that enter the zone focused around proarious sensor information. The system of Intrumine is acquainted with find interlopers from dishonest sensor information. Intrumine first breaks down the dependability of sensor information, then identifies the interlopers' areas, and confirms the identifications focused around a diagram model of the connections in the middle of sensors and interlopers.
Keywords
References
I. Hwang, H. Balakrishnan, K. Roy, and C. Tomlin, Multiple-target tracking and identity management in clutter, with application to aircraft tracking, in Proceedings of the American Control Conference, 2004.
M. Hewish, Reformatting fighter tactics, http://www.cs.berkeley.edu/ prabal/nest/resources/Hewish2001.pdf, 2001.
L. Tang, X. Yu, Q. Gu, J. Han, A. Leung, and T. La Porta, Mining lines data in cyber-physical system, in KDD, 2013.
C. Lo, W. Peng, C. Chen, T. Lin, and C. Lin, Carweb: A traffic data collection platform, in International Conference on Mobile Data Management, 2008.
Y. Zheng and X. Zhou, Computing with Spatial Trajectories. Springer, 2011.
X. Li, R. Lu, X. Liang, X. Shen, J. Chen, and X. Lin, Smart community: An internet of things application, IEEE Communications Magazine, vol. 49, no. 11, pp. 68-75, 2011.
G. Tolle, J. Polastre, R. Szewczyk, D. E. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, and W. Hong, A macroscope in the redwoods, in the ACM Conference on Embedded Networked Sensor Systems, 2005.
Z. Li, J. Han, M. Ji, L. Tang, Y. Yu, B. Ding,J. Lee, and R. Kays, Movemine: Mining moving object data for discovery of animal movement patterns, ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 4, p. 27, 2011.
R. Szewczyk, J. Polastre, A. Mainwaring, and D. Culler, Lessons from a sensor network expedition, in European Workshop on Wireless Sensor Networks, 2004.
X. Sheng and Y. Hu, Maximum likelihood multiple source localization using acoustic energy measurements with wireless sensor networks, IEEE Transactions on Signal Processing, 2005.
M. A. Hammad, W. G. Aref, and A. K. Elmagarmid, Stream window join: Tracking moving objects in sensor network databases, in International Conference on Scientific and Statistical Database Management, 2003.
J. Aslam, Z. Butler, F. Constantin, V. Crespi, G. Cybenko, and D. Rus, Tracking a moving object with a binary sensor network, in the ACM Conference on Embedded Networked Sensor Systems, 2003.
O. Ozdemir, R. Niu, and P. K. Varshney, Tracking in wireless sensor network using particle filtering: Physical layer considerations, IEEE Transactions on Signal Processing, 2009.
S. J. Pan, J. T. Kwok, Q. Yang, and J. J. Pan, Adaptive localization in a dynamic wifi environment through multiview learning, in AAAI, 2007.
R. Pan, J. Zhao, V. W. Zheng, J. J. Pan, D. Shen, S. J. Pan, and Q. Yang, Domain constrained semisupervised mining of tracking models in sensor networks, in KDD, 2007.
L. Tang, X. Yu, S. Kim, J. Han, C. Hung, and W. Peng, Trualarm: Trustworthiness analysis of sensor networks in cyber-physical systems, in ICDM, 2010.
L. Tang, Q. Gu, X. Yu, J. Han, T. La Porta, A. Leung, T. Abdelzaher, and L. Kaplan, Intrumine: Mining intruders in untrustworthy data of cyber-physical systems, in Proc. Of SIAM International Conference on Data Mining (SDM), 2012.
A. Deshpande, C. Guestrin, S. Madden, J. M. Hellerstein, and W. Hong, Model-driven data acquisition in sensor networks, in VLDB, 2004.
E. Elnahrawy and B. Nath, Cleaning and querying noisy sensors, in WSNA, 2003.
F. Koushanfar, M. Potkonjak, and A. Sangiovanni- Vincentelli, On-line fault detection of sensor measurements, in IEEE Conference on Sensors, 2003.
B. Krishnamachari and S. Iyengar, Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks, IEEE Trans. Comput., vol. 53, no. 3, pp. 241-250, 2004.
S. R. Jeffery, G. Alonso, M. J. Franklin, W. Hong, and J. Widom, Declarative support for sensor data cleaning, in ICPC, 2006.
S. Subramaniam, T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos, Online outlier detection in sensor data using non-parametric models, in VLDB, 2006.
Paras, Sanjay Mathur, Avinash Kumar, and Chandra, Mahesh. A feature based neural network model for weather forecasting.World Academy of Science, Engineering and Technology (2007).
Work, Daniel B., and Bayen, Alexandre M. Impacts of the mobile internet on transportation cyberphysical systems: Trafc monitoring using smartphones.
Y.Gu, A. McCallum, and Towsley, D.Detecting anomalies in network traffic using maximum entropy estimation, Oct. 2005.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.