Abstract
WSN is intended to be deployed in environments where sensors can be exposed to circumstances that might interfere with measurements provided. Such circumstances include strong variations of pressure, temperature, radiation, and electromagnetic noise. Thus, measurements may be imprecise in such scenarios. Data fusion is used to overcome sensor failures, technological limitations, and spatial and temporal coverage problems. Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather this information in order to achieve inferences, which will be more efficient and potentially more accurate than if they were achieved by means of a single source. The term efficient, in this case, can mean more reliable delivery of accurate information, more complete, and more dependable. The data fusion can be implemented in both centralized and distributed systems. In a centralized system, all raw sensor data would be sent to one node, and the data fusion would all occur at the same location. In a distributed system, the different fusion modules would be implemented on distributed components. Data fusion occurs at each node using its own data and data from the neighbors. This chapter briefly discusses the data fusion and a comprehensive survey of the existing data fusion techniques, methods and algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Bibliography
F. Nakamura, A.F. Loureiro, and C. Frery, “Data Fusion for Wireless Sensor Networks: Methods, Models, and Classifications,” ACM Computing Surveys, vol. 39, No. 3, Article 9, 2007, August 2007.
R.R. Brooks and S. Iyengar, Multi-Sensor Fusion: Fundamentals and Applications with Software: Prentice Hall PTR, Upper Saddle River, NJ, 2003.
C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: A scalable and robust communication paradigm for sensor networks,” in Proceeding of the 6th ACM Annual International Conference on Mobile Computing and Networking, Boston, MA, USA, August 2000, pp. 56–67.
L. Krishnamachari, D. Estrin, and S. Wicker, “The impact of data aggregation in wireless sensor networks,” in Proceeding of the 22nd International Conference on Distributed Computing Systems Workshops, Vienna, Austria, July 2002, pp. 575–578.
A. Woo, T. Tong, and D. Culler, “Taming the underlying challenges of reliable multihop routing in sensor networks,” in Proceeding of the 1st International Conference on Embedded Network Sensor Systems, Los Angeles, November 2003, pp. 14–27.
“Data fusion lexicon,” U. S. D. o. Defence, Ed.: Data Fusion Subpanel of the Joint Directors of Laboratories 1991.
J. Llinas and D.L. Hall, “An introduction to multi-sensor data fusion,” in Proceeding of the IEEE International Symposium on Circuits and Systems, Monterey, CA, USA, May 1998, pp. 537–540.
L. Wald, “Some terms of reference in data fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 37, pp. 1190–1193, May 1999.
R.C. Luo and M.G. Kay, “Multisensor integration and fusion in intelligent systems,” IEEE Transactions on Systems, Man and Cybernetics, vol. 19, pp. 901–931, October 1989.
K. Kalpakis, K. Dasgupta, and P. Namjoshi, “Efficient algorithms for maximum lifetime data gathering and aggregation” The International Journal of Computer and Telecommunications Networking, vol. 42, pp. 697–716, August 2003.
A. Boulis, S. Ganeriwal, and M.B. Srivastava, “Aggregation in sensor networks: an energy-accuracy trade-off,” in Proceeding of the 1st IEEE International Workshop on Sensor Network Protocols and Applications, Anchorage, AK, USA, May 2003, pp. 128–138.
A. Hoover and B.D. Olsen, “A real-time occupancy map from multiple video streams,” in Proceeding of the IEEE International Conference on Robotics and Automation, Detroit, MI, USA, May 1999, pp. 2261–2266.
Y.J. Zhao, R. Govindan, and D. Estrin, “Residual energy scan for monitoring sensor networks,” in Proceeding of the IEEE Wireless Communications and Networking Conference, Orlando, Florida, USA, March 2002, pp. 356–362.
X. Luo, M. Dong, and Y. Huang, “On distributed fault-tolerant detection in wireless sensor networks,” IEEE Transactions on Computers, vol. 55, pp. 58–70, January 2006.
J. Polastre, J. Hill, and D. Culler, “Versatile low power media access for wireless sensor networks,” in the 2nd ACM International Conference on Embedded Networked Sensor Systems, Baltimore, USA, November 2004, pp. 95–107.
B. Krishnamachari and S. Iyengar, “Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks,” IEEE Transactions on Computers, vol. 53, pp. 241–250, March 2004.
B.V. Dasarathy, “Sensor fusion potential exploitation-innovative architectures and illustrative applications,” in Proceedings of the IEEE, vol. 85, pp. 24–38, January 1997.
M.L. Sichitiu and V. Ramadurai, “Localization of wireless sensor networks with a mobile beacon,” in Proceeding of the IEEE International Conference on Mobile Ad-hoc and Sensor Systems, Pasadena, CA, USA, May 2004, pp. 174–183.
A.P. Dempster, “A generalization of Bayesian inference,” Journal of the Royal Statistical Society, pp. 205–247, March 1968.
G. Shafer, A Mathematical Theory of Evidence Princeton University Press, 1976.
A.J. Pinto, J.M. Stochero, and J.F. Rezende, “Aggregation-aware routing on wireless sensor networks,” in Proceeding of the 9th International Conference on Personal Wireless Communications, Netherlands, September 2004, pp. 238–247.
B. Yu, J. Giampapa, S. Owens, and K. Sycara, “An evidential model of multisensor decision fusion for force aggregation and classification,” in Proceeding of the 8th International Conference on Information Fusion, Philadelphia, USA. 25–29 July 2005, pp. 8–13.
G. Indranil, D. Riordan, and S. Srinivas, “Cluster-head election using fuzzy logic for wireless sensor networks,” in Proceeding of the 3rd Annual Communication Networks and Services Research Conference, Halifax, Novia Scotia, Canada, May 2005, pp. 255–260.
D.S. Friedlander and S. Phoha, “Semantic data fusion for coordinated signal processing in mobile sensor networks,” International Journal of High Performance Computing Applications, vol. 14, pp. 235–241, April 2002.
L. Xiao, S. Boyd, and S. Lall, “A scheme for robust distributed sensor fusion based on average consensus,” in Proceeding of the 4th International Symposium on Information Processing in Sensor Networks, Los Angeles, California, USA, April 2005, pp. 63–70.
L. Xiao, S. Boyd, and S. Lai, “A space-time diffusion scheme for peer-to-peer least-squares estimation,” in Proceeding of the 5th International Conference on Information Processing in Sensor Networks, Nashville, TN, USA, April 2006, pp. 168–176.
Y. Chin-Lung, S. Bagchi, and W. J. Chappell, “Location tracking with directional antennas in wireless sensor networks,” in Proceeding of the IEEE International Microwave Symposium Digest, Long Beach, CA, USA, June 2005, pp. 4–10.
R.E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 3, pp. 35–45, May 1960.
R. Olfati-Saber, “Distributed Kalman Filter with Embedded Consensus Filters,” in Proceeding of the 44th IEEE Conference on Decision and Control, Seville, Spain, December 2005, pp. 8179–8184.
M. Bedworth and J. O’Brien, “The Omnibus model: a new model of data fusion?,” IEEE Aerospace and Electronic Systems Magazine, pp. 30–36, April 2000.
B. Frankel, “Control, estimation and abstraction in fusion architectures: Lessons from human data processing,” in Proceeding of the 3rd International Conference on Data Fusion, Paris, France, May 2000, pp. 3–10.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Abdelgawad, A., Bayoumi, M. (2012). Data Fusion in WSN. In: Resource-Aware Data Fusion Algorithms for Wireless Sensor Networks. Lecture Notes in Electrical Engineering, vol 118. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1350-9_2
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1350-9_2
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4614-1349-3
Online ISBN: 978-1-4614-1350-9
eBook Packages: EngineeringEngineering (R0)