Reputation-based framework for high integrity sensor networks

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Reputation-based framework for high integrity sensor networks

Full text

Pdf (2.86 MB)

Source

ACM Transactions on Sensor Networks (TOSN) archive
Volume 4 , Issue 3 (May 2008) table of contents

Article No. 15

Year of Publication: 2008

ISSN:1550-4859

Authors

Saurabh Ganeriwal	University of California Los Angeles, Los Angeles, CA
Laura K. Balzano	University of California Los Angeles, Los Angeles, CA
Mani B. Srivastava	University of California Los Angeles, Los Angeles, CA

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 87, Downloads (12 Months): 239, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTex EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1362542.1362546 What is a DOI?

ABSTRACT

Sensor network technology promises a vast increase in automatic data collection capabilities through efficient deployment of tiny sensing devices. The technology will allow users to measure phenomena of interest at unprecedented spatial and temporal densities. However, as with almost every data-driven technology, the many benefits come with a significant challenge in data reliability. If wireless sensor networks are really going to provide data for the scientific community, citizen-driven activism, or organizations which test that companies are upholding environmental laws, then an important question arises: How can a user trust the accuracy of information provided by the sensor network? Data integrity is vulnerable to both node and system failures. In data collection systems, faults are indicators that sensor nodes are not providing useful information. In data fusion systems the consequences are more dire; the final outcome is easily affected by corrupted sensor measurements, and the problems are no longer visibly obvious.

In this article, we investigate a generalized and unified approach for providing information about the data accuracy in sensor networks. Our approach is to allow the sensor nodes to develop a community of trust. We propose a framework where each sensor node maintains reputation metrics which both represent past behavior of other nodes and are used as an inherent aspect in predicting their future behavior. We employ a Bayesian formulation, specifically a beta reputation system, for the algorithm steps of reputation representation, updates, integration and trust evolution. This framework is available as a middleware service on motes and has been ported to two sensor network operating systems, TinyOS and SOS. We evaluate the efficacy of this framework using multiple contexts: (1) a lab-scale test bed of Mica2 motes, (2) Avrora simulations, and (3) real data sets collected from sensor network deployments in James Reserve.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Balzano, L. and Srivastava, M. 2006. Fault in sensor networks. Tech. rep. NESL, UCLA, htttp://nesl.ee.ucla.edu.
	2	Barnett, V. and Lewis, T. 1994. Outliers in Statistical Data. John Wiley.
	3	Blaze, M., Feigenbaum, J., Ioannidis, J., and Keromytics, A. 1999. The keynote trust management system. RFC 2704.
	4	Blaze, M., Feigenbaum, J., and Lacy, J. 1996. Decentralized trust managament. In Proceedings of the IEEE Conference on Security and Privacy.
	5	Breunig, M. M., Kriegel, H. P., Ng, R. T., and Sander, J. 2000. LOF: Identifying density-based local outliers. In Proceedings of the ACM SIGMOD Conference. 93--104.
	6	Buchegger, S. and Boudec, J. L. 2002. Performance analysis of the CONFIDANT protocol. In Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc).
	7	Buchegger, S. and Boudec, J. Y. L. 2003a. Coping with false accusations in misbehavior reputation systems for mobile in ad-hoc networks. Tech. rep. IC/2003/31, EPFL-DI-ICCA.
	8	Buchegger, S. and Boudec, J. Y. L. 2003b. The Effect of Rumor Spreading in Reputation Systems for Mobile Ad-hoc Networks. In Proceedings of WiOpt Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks.
	9	Dellarocas, C. 2000. Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In Proceedings of the 2nd ACM Conference on Electronic Commerce.
	10	Du, W., Deng, J., Han, Y. S., and Varshney, P. K. 2003. A witness-based approach for data fusion assurance in wireless sensor networks. In Proceedings of IEEE GlOBECOMM.
	11	Feng, J., Megerian, S., and Potkonjak, M. 2003. Model-based calibration for Sensor Networks. In Proceedings of the IEEE International Conference on Sensors.
	12	Ferguson, T. S. 1973. A bayesian analysis of some nonparametric problems. Annals Statistics 1, 2, 209--230.
	13	Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. 2003. Bayesian Data Analysis. Chapman and Hall.
	14	Han, S., Kumar, R., Shea, R., Kohler, E., and Srivastava, M. B. 2005. A dynamic operating system for sensor nodes. In Proceedings of the ACM Conference on Mobile Systems, Applications and Services (MobiSys). ACM Press.
	15	Hill, J. and Culler, D. 2001. A wireless-embedded architecture for system level optimization. In Tech. rep., UC Berkeley.
	16	Jew, R., Javelo, A., and Ganeriwal, S. 2004. Fault tolerant temperature monitoring system. Tech. rep., NESL, UCLA, htttp://nesl.ee.ucla.edu.
	17	Josang, A. 2001. A logic for uncertain probabilities. Int. J. Fuziness and Knnui-Bas. Syst: 9, 3, 279--311.
	18	Josang, A. and Ismail, R. 2002. The beta reputation system. In Proceedings of the 15th Bled Electronic Commerce Conference.
	19	Knorr, E. M. and Ng, R. T. 1998. Algorithms for Mining Distance-Based Outliers in Large Datasets. In Proceedings of the 24th International Conference on Very Large Data Bases. 392--403.
	20	Knorr, E. M. and Ng, R. T. 1999. Finding Intensional Knowledge of Distance-based Outliers. In Proceedings of the 25th International Conference on Very Large Data Bases. 211--222.
	21	Koushanfar, F. and Potkonjak, M. 2005. Markov-chain based models for missing and faulty data in mica2 sensor motes. Proceedings of the IEEE International Conference on Sensors.
	22	Koushanfar, F., Potkonjak, M., and Sangiovanni-Vincentelli, A. 2002. Fault tolerance in wireless ad hoc sensor networks. In Proceedings of the IEEE International Conference on Sensors.
	23	Koushanfar, F., Potkonjak, M., and Sangiovanni-Vincentelli, A. 2003. On-line fault detection of sensor measurements. In Proceedings of the IEEE International Conference on Sensors.
	24	Koushanfar, F., Potkonjak, M., and Sangiovanni-Vincentelli, A. 2004. Error models for light sensors by non-parametric statistical snalysis of raw sensor measurements. IEEE International Conference on Sensors.
	25	Marti, S., Giuli, T., Lai, K., and Baker, M. 2000. Mitigating routing misbehavior in mobile ad hoc networks. In Proceedings of the International Conference on Mobile Computing and Networking. 255--265.
	26	Michiardi, P. and Molva, R. 2002. Core: A COllaborative REputation mechanism to enforce node cooperation in Mobile Ad Hoc Networks. In Communication and Multimedia Security, Lecture Notes in Computer Science, vol. 2828.
	27	Michiardi, P. and Molva, R. 2003. A game theoretical approach to evaluate cooperation enforcement mechanisms in mobile ad-hoc networks. In Proceedings of WiOpt Modeling an Optimization in Mobile, Ad Hoc and Wireless Networks.
	28	Papadimitriou, S., Kitawaga, H., Gibbons, P. B., and Faloutsos, C. 2003. LOCI: Fast Outlier Detection Using the Local Correlation Integral. In Proceedings of the 19th International Conference on Data Engineering. 315--326.
	29	Polastre, J., Hill, J., and Culler, D. 2004. Versatile low power media access for wireless sensor networks. In Proceedings of the ACM Conference on Networked Sensor Systems (SenSys).
	30	Przydatek, B., Song, D., and Perrig, A. 2003. SIA: secure information aggregation in sensor networks. In Proceedings of the ACM Conference on Networked Sensor Systems (SenSys).
	31	Ramanathan, N., Balzano, L., Burt, M., Estrin, D., Harmon, T., Harvey, C., Jay, J., Kohler, E., Rothenberg, S., and Srivastava M. 2006. Monitoring a toxin in a rural rice field with a wireless sensor network. CENS Tech. rep. 62. htttp://cens.ucla.edu.
	32	Resnick, P., Kuwabara, K., Zeckhauser, R., and Friedman, E. 2000. Reputation systems. Comm. ACM 43, 45--48.
	33	Resnick, P. and Zeckhauser, R. 2000. Trust among strangers in Internet transactions: Empirical analysis of eBay's Reputation System. In the NBER Workshop on Empirical Studies of Electronic Commerce.
	34	Shafer, G. 1976. A Mathematical Theory of Evidence. Princeton University, Princeton, NJ.
	35	Szewczyk, R., Mainwaring, A., Polastre, J., Anderson, J., and Culler, D. 2004. An analysis of a large scale habitat monitoring application. In Proceedings of the ACM Conference on Networked Sensor Systems (SenSys).
	36	Titzer, B., Lee, D., and Palsberg, J. 2005. Avrora: Scalable sensor network simulation with precise timing. In Proceedings of IPSN Track on Sensor Platform, Tools and Design Methods for Networked Embedded Systems.
	37	Wagner, D. 2004. Resilient aggregation in sensor networks. In Proceedings of the ACM Workshop on Security of Ad Hoc and Sensor Networks (SASN).
	38	Xiong, L. and Liu, L. 2003. A reputation-based trust model for peer-to-peer ecommerce communities. In Proceedings of the IEEE Conference on E-Commerce.
	39	Yang, Y., Wang, X., Zhu, S., and Cao, G. 2006. SDAP: a secure hop-by-hop data aggregation protocol for sensor networks. In Proceedings of International Symposium on Mobile Ad Hoc Networking and Computing.
	40	Zhu, S., Setia, S., Jajodia, S., and Ning, P. 2004. An integrated hop-by-hop authentication scheme for filtering of injected false data in sensor networks. In Proceedings of IEEE Symposium on Security and Privacy.