Diagnostic powertracing for sensor node failure analysis

Troubleshooting unresponsive sensor nodes is a significant challenge in remote sensor network deployments. This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure. We developed our own low-cost power meter with low-bandwidth radio to report power measurements and findings, hence allowing remote (i.e., tele-) diagnosis. The tool was deployed and tested in a remote solar-powered sensing network for acoustic and visual environmental monitoring. It was shown to successfully distinguish between several categories of failures that cause unresponsive behavior including energy depletion, antenna damage, radio disconnection, system crashes, and anomalous reboots. It was also able to determine the internal health conditions of an unresponsive node, such as the presence or absence of sensing and data storage activities (for each of multiple sensors). The paper explores the feasibility of building such a remote diagnostic tool from the standpoint of economy, scale and diagnostic accuracy. To the authors' knowledge, this is the first paper that presents a remote diagnostic tool that uses power measurements to diagnose sensor system failures.

[1]  Richard Wolski,et al.  S2DB: a novel simulation-based debugger for sensor network applications , 2006, EMSOFT '06.

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

[3]  Jiawei Han,et al.  Dustminer: troubleshooting interactive complexity bugs in sensor networks , 2008, SenSys '08.

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

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

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

[7]  Tarek F. Abdelzaher,et al.  SNTS: Sensor Network Troubleshooting Suite , 2007, DCOSS.

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

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

[10]  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..

[11]  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.

[12]  Miklós Maróti,et al.  Software composition and verification for sensor networks , 2005, Sci. Comput. Program..

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

[14]  Tarek F. Abdelzaher,et al.  Towards Diagnostic Simulation in Sensor Networks , 2008, DCOSS.

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

[16]  Matt Welsh,et al.  MoteLab: a wireless sensor network testbed , 2005, IPSN '05.

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

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

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

[20]  Yunhao Liu,et al.  Passive Diagnosis for Wireless Sensor Networks , 2010, IEEE/ACM Transactions on Networking.

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

[22]  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..

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

[24]  V. Kulathumani,et al.  Kansei: a testbed for sensing at scale , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[25]  Hridesh Rajan,et al.  Slede: a domain-specific verification framework for sensor network security protocol implementations , 2008, WiSec '08.

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

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

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

[29]  Peter Csaba Ölveczky,et al.  Formal modeling and analysis of wireless sensor network algorithms in Real-Time Maude , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[30]  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.

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

[32]  Paolo Ballarini,et al.  Model Checking Medium Access Control for Sensor Networks , 2006, Second International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (isola 2006).

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

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

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