Power watermarking: Facilitating power-based diagnosis of node silence in remote high-end sensing systems

Our prior work suggested the use of power traces of unresponsive sensor nodes to diagnose the cause of anomalous node silence, but suffers from its limitations in scalability. To address these issues, we propose a new concept of power watermarking, a diagnostic service that actively produces unique power watermarks for each system state of interest so as to convey system information over power measurements. Failures of applications, hardware, or the watermark generator result in different watermark combinations or absence thereof. Experiments demonstrate high diagnostic accuracy and energy efficiency, even in the presence of multiple applications of similar natural power consumption patterns.

[1]  F. Sultanem,et al.  Using appliance signatures for monitoring residential loads at meter panel level , 1991 .

[2]  Eamonn J. Keogh,et al.  Mining motifs in massive time series databases , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[3]  David E. Culler,et al.  Design of an application-cooperative management system for wireless sensor networks , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[4]  Jonathan W. Hui,et al.  Marionette: using RPC for interactive development and debugging of wireless embedded networks , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[5]  Matt Welsh,et al.  Monitoring volcanic eruptions with a wireless sensor network , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[6]  Yunhao Liu,et al.  Agnostic diagnosis: Discovering silent failures in wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[7]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[8]  Rakesh Agrawal,et al.  Keyboard acoustic emanations , 2004, IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004.

[9]  Feng Zhao,et al.  Fine-grained energy profiling for power-aware application design , 2008, PERV.

[10]  Fabien Mieyeville,et al.  Towards a taxonomy of simulation tools for wireless sensor networks , 2010, SimuTools.

[11]  Deborah Estrin,et al.  EmStar: A Software Environment for Developing and Deploying Wireless Sensor Networks , 2004, USENIX ATC, General Track.

[12]  Deborah Estrin,et al.  Sympathy for the sensor network debugger , 2005, SenSys '05.

[13]  Ye Wen,et al.  S 2 DB : A Novel Simulation-Based Debugger for Sensor Network Applications , 2006 .

[14]  Koen Langendoen,et al.  MoMi: model-based diagnosis middleware for sensor networks , 2009, MidSens '09.

[15]  Dong Kun Noh,et al.  SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks , 2009, MobiSys '09.

[16]  Hari Balakrishnan,et al.  Memento: A Health Monitoring System for Wireless Sensor Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.

[17]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[18]  Fabio Roli,et al.  Design of effective multiple classifier systems by clustering of classifiers , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[19]  Feng Li,et al.  Dependence-based multi-level tracing and replay for wireless sensor networks debugging , 2011, LCTES '11.

[20]  Michael LeMay,et al.  Acoustic Surveillance of Physically Unmodified PCs , 2006, Security and Management.

[21]  Xin Jin,et al.  Diagnostic powertracing for sensor node failure analysis , 2010, IPSN '10.

[22]  Huadong Ma,et al.  Content Based Pre-diagnosis for Wireless Sensor Networks , 2011, 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks.

[23]  Lufeng Mo,et al.  Passive Diagnosis for WSNs Using Data Traces , 2012, 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems.

[24]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[25]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[26]  Dennis Shasha,et al.  High Performance Discovery In Time Series: Techniques And Case Studies (Monographs in Computer Science) , 2004 .

[27]  John S. Baras,et al.  ATEMU: a fine-grained sensor network simulator , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[28]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[29]  Kamin Whitehouse,et al.  Clairvoyant: a comprehensive source-level debugger for wireless sensor networks , 2007, SenSys '07.

[30]  Lothar Thiele,et al.  Deployment support network a toolkit for the development of WSNs , 2007 .

[31]  Margaret Martonosi,et al.  Hardware design experiences in ZebraNet , 2004, SenSys '04.

[32]  Guoliang Xing,et al.  Nemo: A high-fidelity noninvasive power meter system for wireless sensor networks , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[33]  Richard Maclin,et al.  Ensembles as a Sequence of Classifiers , 1997, IJCAI.

[34]  Alberto O. Mendelzon,et al.  Similarity-based queries for time series data , 1997, SIGMOD '97.

[35]  Cecilia Mascolo,et al.  Evolution and sustainability of a wildlife monitoring sensor network , 2010, SenSys '10.

[36]  François Ingelrest,et al.  SensorScope: Application-specific sensor network for environmental monitoring , 2010, TOSN.

[37]  Koen Langendoen,et al.  A Global-State Perspective on Sensor Network Debugging , 2008 .

[38]  Shutao Zhao,et al.  The Research of Electric Appliance Running Status Detecting Based on DSP , 2005, 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific.

[39]  Kamin Whitehouse,et al.  Declarative tracepoints: a programmable and application independent debugging system for wireless sensor networks , 2008, SenSys '08.

[40]  Pedro José Marrón,et al.  COOJA/MSPSim: interoperability testing for wireless sensor networks , 2009, SimuTools.

[41]  Tei-Wei Kuo,et al.  A multi-granularity energy profiling approach and a quantitative study of a Web browser , 2005, 10th IEEE International Workshop on Object-Oriented Real-Time Dependable Systems.

[42]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[43]  Ray Horak,et al.  Telecommunications and Data Communications Handbook , 2007 .

[44]  Richard Han,et al.  NodeMD: diagnosing node-level faults in remote wireless sensor systems , 2007, MobiSys '07.

[45]  Markus G. Kuhn.  Security Limits for Compromising Emanations , 2005, CHES.

[46]  Yunhao Liu,et al.  Passive diagnosis for wireless sensor networks , 2010, TNET.

[47]  Patrick Th. Eugster,et al.  Lightweight tracing for wireless sensor networks debugging , 2009, MidSens '09.

[48]  Francis Olivier,et al.  Electromagnetic Analysis: Concrete Results , 2001, CHES.