The Community for Technology Leaders
RSS Icon
Issue No.09 - Sept. (2012 vol.23)
pp: 1739-1751
Jinzhu Chen , Michigan State University, East Lansing
Rui Tan , Michigan State University, East Lansing
Guoliang Xing , Michigan State University, East Lansing
Xiaorui Wang , Ohio State University, Columbus
Xing Fu , The University of Tennessee, Knoxville
Recent years have seen the growing deployments of Cyber-Physical Systems (CPSs) in many mission-critical applications such as security, civil infrastructure, and transportation. These applications often impose stringent requirements on system sensing fidelity and timeliness. However, existing approaches treat these two concerns in isolation and hence are not suitable for CPSs where system fidelity and timeliness are dependent on each other because of the tight integration of computational and physical resources. In this paper, we propose a holistic approach called Fidelity-Aware Utilization Controller (FAUC) for Wireless Cyber-physical Surveillance (WCS) systems that combine low-end sensors with cameras for large-scale ad hoc surveillance in unplanned environments. By integrating data fusion with feedback control, FAUC can enforce a CPU utilization upper bound to ensure the system's real-time schedulability although CPU workloads vary significantly at runtime because of stochastic detection results. At the same time, FAUC optimizes system fidelity and adjusts the control objective of CPU utilization adaptively in the presence of variations of target/noise characteristics. We have implemented FAUC on a small-scale WCS testbed consisting of TelosB/Iris motes and cameras. Moreover, we conduct extensive simulations based on real acoustic data traces collected in a vehicle surveillance experiment. The testbed experiments and the trace-driven simulations show that FAUC can achieve robust fidelity and real-time guarantees in dynamic environments.
Sensor systems, Cameras, Real time systems, Sensor fusion, Surveillance, Noise, CPU utilization control, Sensor systems, Cameras, Real time systems, Sensor fusion, Surveillance, Noise, cyber-physical systems., Real-time detection, data fusion
Jinzhu Chen, Rui Tan, Guoliang Xing, Xiaorui Wang, Xing Fu, "Fidelity-Aware Utilization Control for Cyber-Physical Surveillance Systems", IEEE Transactions on Parallel & Distributed Systems, vol.23, no. 9, pp. 1739-1751, Sept. 2012, doi:10.1109/TPDS.2012.74
[1] Strix Systems Inc., http:/, 2010.
[2] T. He, S. Krishnamurthy, J.A. Stankovic, T. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, J. Hui, and B. Krogh, "Energy-Efficient Surveillance System using Wireless Sensor Networks," Proc. Second Int'l Conf. Mobile Systems, Applications, and Services (MobiSys), 2004.
[3] Memsic Inc., http:/, 2011.
[4] J.A. Stankovic, T. He, T. Abdelzaher, M. Marley, G. Tao, S. Son, and C. Lu, "Feedback Control Scheduling in Distributed Real-time Systems," Proc. IEEE 22nd Real-Time Systems Symp. (RTSS), 2001.
[5] C. Lu, X. Wang, and X. Koutsoukos, "Feedback Utilization Control in Distributed Real-Time Systems with End-to-End Tasks," IEEE Trans. Parallel Distributed Systems, vol. 16, no. 6, pp. 550-561, June 2005.
[6] J. Yao, X. Liu, M. Yuan, and Z. Gu, "Online Adaptive Utilization Control for Real-Time Embedded Multiprocessor Systems," Proc. Int'l Conf. Hardware/Software Codesign and System Synthesis (CODES+ISSS), 2008.
[7] K. Whitehouse and D. Culler, "Calibration as Parameter Estimation in Sensor Networks," Proc. First ACM Int'l Workshop Wireless Sensor Networks and Applications (WSNA), 2002.
[8] P.K. Varshney, Distributed Detection and Data Fusion. Springer, 1996.
[9] G. Xing, R. Tan, B. Liu, J. Wang, X. Jia, and C.-W. Yi, "Data Fusion Improves the Coverage of Wireless Sensor Networks," Proc. MobiCom, 2009.
[10] R. Tan, G. Xing, B. Liu, and J. Wang, "Impact of Data Fusion on Real-Time Detection in Sensor Networks," Proc. IEEE 30th Real-Time Systems Symp. (RTSS), 2009.
[11] J. Feng, S. Megerian, and M. Potkonjak, "Model-Based Calibration for Sensor Networks," Proc. IEEE Sensors, 2003.
[12] R. Tan, G. Xing, X. Liu, J. Yao, and Z. Yuan, "Adaptive Calibration for Fusion-Based Wireless Sensor Networks," Proc. IEEE INFOCOM, 2010.
[13] X. Sheng and Y.-H. Hu, "Maximum Likelihood Multiple-source Localization Using Acoustic Energy Measurements with Wireless Sensor Networks," IEEE Trans. Signal Processing, vol. 53, no. 1, pp. 44-53, Jan. 2005.
[14] M. Hata, "Empirical Formula for Propagation Loss in Land Mobile Radio Services," IEEE Trans. Vehicular Technology, vol. 29, no. 3, pp. 317-325, Aug. 1980.
[15] D. Li and Y.-H. Hu, "Energy Based Collaborative Source Localization Using Acoustic Micro-Sensor Array," EUROSIP J. Applied Signal Processing, vol. 2003, pp. 371-375, 2003.
[16] C. Wren, U. Erdem, and A. Azarbayejani, "Functional Calibration for Pan-Tilt-Zoom Cameras in Hybrid Sensor Networks," Multimedia Systems, vol. 12, no. 3, pp. 255-268, 2006.
[17] P. Dutta, A. Arora, and S. Bibyk, "Towards Radar-enabled Sensor Networks," Proc. ACM/IEEE Fifth Int'l Conf. Information Processing in Sensor Networks (IPSN), 2006.
[18] T. He, P. Vicaire, T. Yan, L. Luo, L. Gu, G. Zhou, R. Stoleru, Q. Cao, J.A. Stankovic, and T. Abdelzaher, "Achieving Real-Time Target Tracking Using Wireless Sensor Networks," Proc. IEEE 12th Real-Time and Embedded Technology and Applications Symp. (RTAS), 2006.
[19] C.L. Liu and J. Layland, "Scheduling Algorithms for Multiprogramming in a Hard Real-Time Environments," J. ACM, vol. 20, no. 1, pp. 46-61, 1973.
[20] C. Lu, X. Wang, and C. Gill, "Feedback Control Real-Time Scheduling in ORB Middleware," Proc. IEEE Ninth Real-Time and Embedded Technology and Applications Symp. (RTAS), 2003.
[21] J. MacQueen, "Some Methods for Classification and Analysis of Multivariate Observations," Proc. Fifth Berkeley Symp. Math. Statistics and Probability, 1967.
[22] K. Ogata, Discrete-Time Control Systems. Prentice-Hall, 1995.
[23] ImageMagick, http:/, 2010.
[24] M. Duarte and Y.-H. Hu, "Vehicle Classification in Distributed Sensor Networks," J. Parallel and Distributed Computing, vol. 64, no. 7, pp. 826-838, 2004.
39 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool