Computational and Compressed Sensing Optimizations for Information Processing in Sensor Network
##plugins.themes.academic_pro.article.main##
Abstract
This report narrates the development and deployment of a Distributed Sensing Algorithm that had a profound impact on the performance and capabilities of a number of computing systems. It solved a number of complex issues in the deployment of large scale sensor networks. We begin with a brief history of the development of this algorithm.
##plugins.themes.academic_pro.article.details##
How to Cite
Kumar, V. . (2012). Computational and Compressed Sensing Optimizations for Information Processing in Sensor Network. International Journal of Next-Generation Computing, 3(3), 328–332. https://doi.org/10.47164/ijngc.v3i3.41
References
- Brooks, R.R. and Iyengar, S. S., Robust Distributed Computing and Sensing Algorithm" IEEE Computer. vol. 29 No. 6. pp. 53-60. June 1996.
- Brooks, R.R. and Iyengar, S.S., Multi-Sensor Fusion, Fundamentals and Applications with Software, 1998 Prentice Hall PTR.
- Chakrabarty, K. Iyengar, S.S. H. Qi and E.C. Cho, Grid Coverage of Surveillance and Target Location in Distributed Sensor Networks, IEEE Transactions on Computers, Vol 51, No. 12, December 2002.
- Krishnamachari, B. and Iyengar, S.S., Distributed Bayesian Algorithms for Fault-Tolerant Event Region Detection in Wireless Sensor Networks", IEEE Tran Comp, 2004.
- Iyengar et al, "Preventing Future Oil Spills with Software-Based Event Detection" IEEE Comp; 2010.
- Rogina, Pablo J. and Wainer, Gabriel, Extending MINIX with Real-Time Services and Fault Tolerance Capabilities" Infoteca, Departmentto de Computacion - FCEN, Argentina. 2000-2001.