This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Model-Based Techniques for Data Reliability in Wireless Sensor Networks
April 2009 (vol. 8 no. 4)
pp. 528-543
Shoubhik Mukhopadhyay, University of California, San Diego, La Jolla
Curt Schurgers, University of California, San Diego, La Jolla
Debashis Panigrahi, University of California, San Diego, La Jolla
Sujit Dey, University of California, San Diego, La Jolla
Wireless Sensor Networks are a fast-growing class of systems. They offer many new design challenges, due to stringent requirements like tight energy budgets, low-cost components, limited processing resources, and small footprint devices. Such strict design goals call for technologies like nanometer-scale semiconductor design and low-power wireless communication to be used. But using them would also make the sensor data more vulnerable to errors, within both the sensor nodes' hardware and the wireless communication links. Assuring the reliability of the data is going to be one of the major design challenges of future sensor networks. Traditional methods for reliability cannot always be used, because they introduce overheads at different levels, from hardware complexity to amount of data transmitted. This paper presents a new method that makes use of the properties of sensor data to enable reliable data collection. The approach consists of creating predictive models based on the temporal correlation in the data and using them for real-time error correction. This method handles multiple sources of errors together without imposing additional complexity or resource overhead at the sensor nodes. We demonstrate the ability to correct transient errors arising in sensor node hardware and wireless communication channels through simulation results on real sensor data.

[1] S. Mukhopadhyay, D. Panigrahi, and S. Dey, “Data Aware, Low Cost Error Correction for Wireless Sensor Networks,” Proc. IEEE Wireless Comm. and Networking Conf. (WCNC '04), pp. 2492-2497, Mar. 2004.
[2] S. Mukhopadhyay, D. Panigrahi, and S. Dey, “Model Based Error Correction for Wireless Sensor Networks,” Proc. IEEE Sensor and Ad Hoc Comm. and Networks (SECON '04), pp. 575-584, 2004.
[3] M. Hatler and C. Chi, “Wireless Sensor Networks: Growing Markets, Accelerating Demand,” technical report, ON World, Oct. 2005.
[4] Wireless Sensor Networks Market Expected to Skyrocket, http://www.controldesign.com/industrynews/ 2005040.html, 2005.
[5] J. Hill et al., “System Architecture Directions for Networked Sensors,” Proc. Ninth Int'l Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS '00), pp. 93-104, 2000.
[6] N. Ferzli et al., “An Application of Smart Dust for Pavement Condition Monitoring,” Smart Structures and Materials: Proc. SPIE, vol. 6174, pp. 976-987, 2006.
[7] I. Mahgoub and M. Ilyas, Smart Dust: Sensor Network Applications, Architecture, and Design. CRC Press, 2006.
[8] E. Elnahrawy and B. Nath, “Cleaning and Querying Noisy Sensors,” Proc. Second ACM Int'l Workshop Wireless Sensor Networks and Applications (WSNA '03), 2003.
[9] V. Bychkovskiy, S. Megerian, D. Estrin, and M. Potkonjak, “A Collaborative Approach to In-Place Sensor Calibration,” Proc. Int'l Workshop Information Processing in Sensor Networks (IPSN '03), 2003.
[10] T. Karnik and P. Hazucha, “Characterization of Soft Errors Caused by Single Event Upsets in CMOS Processes,” IEEE Trans. Dependable and Secure Computing, vol. 1, no. 2, pp. 128-143, 2004.
[11] P. Shivakumar et al., “Modeling the Effect of Technology Trends on the Soft Error Rate of Combinational Logic,” Proc. Int'l Conf. Dependable Systems and Networks (DSN '02), pp. 389-398, 2002.
[12] G. Schindlbeck, “Trend in DRAM Soft Errors,” Proc. 12th IEEE Int'l On-Line Testing Symp. (IOLTS '06), p. 272, 2006.
[13] R. Baumann, “Technology Scaling Trends and Accelerated Testing for Soft Errors in Commercial Silicon Devices,” Proc. Ninth IEEE Int'l On-Line Testing Symp. (IOLTS '03), p. 4, 2003.
[14] R. Baumann, “Radiation-Induced Soft Errors in Advanced Semiconductor Technologies,” IEEE Trans. Device and Materials Reliability, vol. 5, no. 3, pp. 305-316, 2005.
[15] P. Hazucha et al., “Neutron Soft Error Rate Measurements in a 90-nm CMOS Process and Scaling Trends in SRAM from 0.25-$\mu{\rm m}$ to 90-nm Generation,” IEEE Int'l Electron Devices Meeting. IEDM Technical Digest, pp. 21.5.1-21.5.4, 2003.
[16] C. Zhao, X. Bai, and S. Dey, “A Static Noise Impact Analysis Methodology for Evaluating Transient Error Effects in Digital VLSI Circuits,” Proc. Int'l Test Conf. (ITC '05), p. 40.2, Oct. 2005.
[17] B. Warneke, M. Last, B. Liebowitz, and K. Pister, “Smart Dust: Communicating with a Cubic-Millimeter Computer,” Computer, vol. 34, no. 1, pp. 44-51, 2001.
[18] Y. Zhao and S. Dey, “Separate Dual Transistor Registers: A Circuit Solution for On-Line Testing of Transient Errors in UDSM-IC,” Proc. Ninth IEEE Int'l On-Line Testing Symp. (IOLTS '03), pp. 7-11, 2003.
[19] K. Itoh, M. Horiguchi, and T. Kawahara, “Ultra-Low Voltage Nano-Scale Embedded RAMs,” Proc. IEEE Int'l Conf. Circuits and Systems (ISCAS '06), pp. 25-28, 2006.
[20] S. Mukherjee, J. Emer, and S. Reinhardt, “The Soft Error Problem: An Architectural Perspective,” Proc. 11th Int'l Symp. High-Performance Computer Architecture (HPCA '05), pp. 243-247, 2005.
[21] S.B. Wicker, Error Control Systems for Digital Communication and Storage. Prentice Hall, 1995.
[22] M.C. Vuran, O.B. Akan, and I.F. Akyildiz, “Spatio-Temporal Correlation: Theory and Applications for Wireless Sensor Networks,” Computer Networks, vol. 45, no. 3, pp. 245-259, 2004.
[23] L. Sankaranarayanan, G. Kramer, and N. Mandayam, “Hierarchical Sensor Networks: Capacity Bounds and Cooperative Strategies Using the Multiple-Access Relay Channel Model,” Proc. IEEE Sensor and Ad Hoc Comm. and Networks (SECON '04), pp. 191-199, 2004.
[24] S. Bandyopadhyay and E. Coyle, “An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks,” Proc. IEEE INFOCOM, vol. 3, pp. 1713-1723, 2003.
[25] S. Soro and W. Heinzelman, “Prolonging the Lifetime of Wireless Sensor Networks via Unequal Clustering,” Proc. 19th IEEE Int'l Parallel and Distributed Processing Symp. (IPDPS '05), p. 8, 2005.
[26] M. Zhang and N.R. Shanbhag, “A Soft Error Rate Analysis (Sera) Methodology,” Proc. IEEE/ACM Int'l Conf. Computer-Aided Design (ICCAD '04), pp. 111-118, 2004.
[27] C. Zhao, X. Bai, and S. Dey, “A Scalable Soft Spot Analysis Methodology for Compound Noise Effects in Nano-Meter Circuits,” Proc. 41st Ann. Conf. Design Automation (DAC '04), pp. 894-899, 2004.
[28] S. Mitra, N. Kee, and S. Kim, “Robust System Design with Built-In Soft-Error Resilience,” Computer, vol. 38, no. 2, pp. 43-52, 2005.
[29] R. Szewczyk et al., “Application Driven Systems Research: Habitat Monitoring with Sensor Networks,” Comm. ACM, special issue on sensor networks, pp. 34-40, June 2004.
[30] A. Willig and R. Mitschke, “Results of Bit Error Measurements with Sensor Nodes and Casuistic Consequences for Design of Energy-Efficient Error Control Schemes,” Proc. Third European Workshop Wireless Sensor Networks (EWSN '06), 2006.
[31] L. Nachman et al., “The Intel Mote Platform: A Bluetooth-Based Sensor Network for Industrial Monitoring,” Proc. Fourth Int'l Symp. Information Processing in Sensor Networks (IPSN '05), p. 61, 2005.
[32] H. Akaike, “Fitting Autoregressive Models for Prediction,” Annals of the Inst. Statistical Math., vol. 21, pp. 243-247, 1969.
[33] A. Harvey, Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge Univ. Press, 1989.
[34] H. Kantz, T. Schreiber, and D. Wojcik, “Nonlinear Time Series Analysis,” Pure and Applied Geophysics. Birkhauser, 1998.
[35] M. Thottan and C. Ji, “Fault Prediction at the Network Layer Using Intelligent Agents,” Proc. Sixth IFIP/IEEE Int'l Symp. Integrated Network Management (IM '99), pp. 745-759, 1999.
[36] C. Chatfield, The Analysis of Time Series: An Introduction, sixth ed. CRC Press, 2004.
[37] S. Haykin, Adaptive Filter Theory, second ed. Prentice Hall, 1996.
[38] MATLAB: A High-Level Technical Computing Environment, http://www.mathworks.com/productsmatlab, 2008.
[39] CDEC: California Data Exchange Center, California Dept. of Water Resources, http:/cdec.water.ca.gov, 2008.
[40] K. Jamieson and H. Balakrishnan, “PPR: Partial Packet Recovery for Wireless Networks,” ACM SIGCOMM Computer Comm. Rev., vol. 37, no. 4, pp. 409-420, 2007.
[41] J. Zhao and R. Govindan, “Understanding Packet Delivery Performance in Dense Wireless Sensor Networks,” Proc. First Int'l Conf. Embedded Networked Sensor Systems (SenSys '03), pp. 1-13, 2003.
[42] S. Howard, C. Schlegel, and K. Iniewski, “Error Control Coding in Low-Power Wireless Sensor Networks: When Is ECC Energy-Efficient?” EURASIP J. Wireless Comm. and Networking, vol. 2006, no. 2, p. 29, 2006.
[43] J. Jeong and C. Ee, Forward Error Correction in Sensor Networks. Univ. of California, May 2006.
[44] F. Stann and J. Heidemann, “RMST: Reliable Data Transport in Sensor Networks,” Proc. First Int'l Workshop Sensor Network Protocols and Applications (SNPA '03), 2003.
[45] C.-Y. Wan, A.T. Campbell, and L. Krishnamurthy, Reliable Transport for Sensor Networks: PSFQ—Pump Slowly Fetch Quickly Paradigm. Kluwer Academic Publishers, 2004.
[46] Q. Cao et al., “Cluster-Based Forwarding for Reliable End-to-End Delivery in Wireless Sensor Networks,” Proc. IEEE INFOCOM, pp.1928-1936, 2007.
[47] M. Vuran and I. Akyildiz, “Cross-Layer Analysis of Error Control in Wireless Sensor Networks,” Proc. IEEE Comm. Soc. Conf. Sensor and Ad Hoc Comm. and Networks (SECON '06), pp. 585-594, vol. 2, 2006.
[48] S. Cui et al., “Energy-Efficient Joint Estimation in Sensor Networks: Analog versus Digital,” Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing (ICASSP '05), vol. 4, 2005.
[49] M. Gastpar and M. Vetterli, “Power, Spatio-Temporal Bandwidth, and Distortion in Large Sensor Networks,” IEEE J. Selected Areas in Comm., vol. 23, no. 4, pp. 745-754, 2005.
[50] C. Zhao and S. Dey, “Improving Transient Error Tolerance of Digital VLSI Circuits Using Robustness Compiler (ROCO),” Proc. Seventh Int'l Symp. Quality Electronic Design (ISQED '06), p. 6, 2006.
[51] X. Luo, M. Dong, and Y. Huang, “On Distributed Fault-Tolerant Detection in Wireless Sensor Networks,” IEEE Trans. Computers, vol. 55, no. 1, pp. 58-70, 2006.
[52] B. Krishnamachari and S. Iyengar, “Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks,” IEEE Trans. Computers, vol. 53, no. 3, pp. 241-250, Mar. 2004.
[53] J.M. Hellerstein, W. Hong, S. Madden, and K. Stanek, “Beyond Average: Towards Sophisticated Sensing with Queries,” Proc. Second Int'l Workshop Information Processing in Sensor Networks (IPSN '03), Mar. 2003.
[54] A. Savvides, W. Garber, R. Moses, and M. Srivastava, “An Analysis of Error Inducing Parameters in Multihop Sensor Node Localization,” IEEE Trans. Mobile Computing, vol. 4, no. 6, pp. 567-577, 2005.
[55] A. Manjhi, S. Nath, and P. Gibbons, Tributaries and Deltas: Efficient and Robust Aggregation in Sensor Network Streams, pp.287-298. ACM Press, 2005.
[56] D. Kempe, A. Dobra, and J. Gehrke, “Gossip-Based Computation of Aggregate Information,” Proc. 44th Ann. IEEE Symp. Foundations of Computer Science (FOCS '03), p. 482, 2003.
[57] D. Ganeshan, R. Govindan, S. Shenker, and D. Estrin, “Highly Resilient, Energy Efficient Multipath Routing in Wireless Sensor Networks,” Mobile Computing and Comm. Rev., vol. 1, no. 2, 2002.
[58] S.S. Pradhan and K. Ramachandran, “Distributed Source Coding: Symmetric Rates and Applications to Sensor Networks,” Proc. IEEE Data Compression Conf. (DCC '00), Mar. 2000.
[59] M. Drinic et al., “Model-Based Compression in Wireless Ad Hoc Networks,” Proc. First Int'l Conf. Embedded Networked Sensor Systems (SenSys '03), pp. 231-242, 2003.

Index Terms:
Reliability, data models, wireless sensor networks, error correction.
Citation:
Shoubhik Mukhopadhyay, Curt Schurgers, Debashis Panigrahi, Sujit Dey, "Model-Based Techniques for Data Reliability in Wireless Sensor Networks," IEEE Transactions on Mobile Computing, vol. 8, no. 4, pp. 528-543, April 2009, doi:10.1109/TMC.2008.131
Usage of this product signifies your acceptance of the Terms of Use.