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Sitharama Iyengar

Sitharama Iyengar

FIU School of Computing and Information Sciences

Title: Impact of Brooks-Iyengar Distributed Sensor Network Algorithm for the Next Decade

Biography

S. S. Iyengar is a Distinguished Ryder Professor and Director of the School of Computing and Information Sciences at the Florida International University and is the founding Director of the FIU-Discovery Lab. Iyengar is a pioneer in the field of distributed sensor networks/sensor fusion, computational aspects of robotics and high performance computing. Iyengar has published over 500 research papers and has authored/co-authored/edited 20 books published by MIT Press, John Wiley & Sons, Prentice Hall, CRC Press, Springer Verlag, etc. These publications have been used in major universities all over the world.

His research publications are on the design and analysis of efficient algorithms, parallel computing, sensor networks, and robotics. He is also a member of the European Academy of Sciences, a Fellow of IEEE, a Fellow of ACM, a Fellow of AAAS, Fellow of NAI, and a Fellow of Society of Design and Process Program (SPDS), Fellow of Institution of Engineers (FIE), awarded a Distinguished Alumnus Award of the Indian Institute of Science, Bangalore, and was awarded the IEEE Computer Society Technical Achievement for the contributions to sensor fusion algorithms, and parallel algorithms. He is a Golden Core member of the IEEE-CS and he has received a Lifetime Achievement Award conferred by the International Society of Agile Manufacturing (ISAM) in recognition of his illustrious career in teaching, research, and a lifelong contribution to the fields of Engineering and Computer Science at Indian Institute of Technology (BHU). Iyengar and Nulogix were awarded in the 2012 Innovation 2 Industry (i2i) Florida competition. Iyengar received Distinguished Research Award from Xaimen University, China for his research in Sensor Networks, Computer Vision and Image Processing Iyengar's landmark contributions with his research group is the development of grid coverage for surveillance and target location in distributed sensor networks and Brooks Iyengar fusion algorithm.

He has also been awarded honorary Doctorate of Science and Engineering from an institution. He serves on the advisory board of many corporations and universities in the world. He has served on many National Science Boards such as NIH - National Library of Medicine in Bioinformatics, National Science Foundation review panel, NASA Space Science, Department of Homeland Security, Office of Naval Security, and many others. His contribution was a centerpiece of this pioneering effort to develop image analysis for our science and technology and to the Goals of the US Naval Research Laboratory. The impact of his research contributions can be seen in companies/National Labs like Raytheon, Telecordia, Motorola, the United States Navy, DARPA agencies, etc. His contribution in DARPAS's program demonstration with BBN, Cambridge, Massachusetts, MURI, researchers from PSU/ARL, Duke, University of Wisconsin, UCLA, Cornell university and LSU.

Abstract

Brooks–Iyengar algorithm is a seminal work and a major milestone in distributed sensing, and could be used as a fault tolerant solution for many redundancy scenarios. Also, it is easy to implement and embed in any networking systems. In 1996, the algorithm was used in MINIX to provide more accuracy and precision, which leads to the development of the first version of RT-Linux. In 2000, the algorithm was also central to the DARPA SensIT program’s distributed tracking program. Acoustic, seismic and motion detection readings from multiple sensors are combined and fed into a distributed tracking system. Besides, it was used to combine heterogeneous sensor feeds in the application fielded by BBN Technologies, BAE systems, Penn State Applied Research Lab(ARL), and USC/ISI.

Besides, the Thales Group, an UK Defense Manufacturer, used this work in its Global Operational Analysis Laboratory. It is applied to Raytheon’s programs where many systems need extract reliable data from unreliable sensor network, this exempts the increasing investment in improving sensor reliability. Also, the research in developing this algorithm results in the tools used by the US Navy in its maritime domain awareness software.

In education, Brooks–Iyengar algorithm has been widely used in teaching classes such as University of Wisconsin, Purdue, Georgia Tech, Clemson University, University of Maryland, etc.

In addition to the area of sensor network, other fields such as time-triggered architecture, safety of cyber-physical systems, data fusion, robot convergence, high-performance computing, software/hardware reliability, ensemble learning in artificial intelligence systems could also benefit from Brooks–Iyengar algorithm.