The Brooks–Iyengar hybrid algorithm for distributed control in the presence of noisy data combines Byzantine agreement with sensor fusion. It bridges the gap between sensor fusion and Byzantine fault tolerance. This seminal algorithm unified these disparate fields for the first time. Essentially, it combines Dolev’s algorithm for approximate agreement with Mahaney and Schneider’s fast convergence algorithm (FCA). The algorithm assumes N processing elements (PEs), t of which are faulty and can behave maliciously. It takes as input either real values with inherent inaccuracy or noise (which can be unknown), or a real value with apriori defined uncertainty, or an interval. The output of the algorithm is a real value with an explicitly specified accuracy. The algorithm runs in O(NlogN) where N is the number of PEs: see Big O notation. It is possible to modify this algorithm to correspond to Crusader’s Convergence Algorithm (CCA), showever, the bandwidth requirement will also increase. The algorithm has applications in distributed control, software reliability and High-performance computing. Wikipedia

Application in DARPAS's program demonstration with BBN, Cambridge Massachussetts, MURI, researchers from PSU/ARL, Duke, University of Wisconsin, UCLA, Cornell and LSU.

In 2000, the DARPA program manager used two major demostrations to showcase SensIT's advances and document the ability of sensor networks to provide new capabilities. One demostration took place at the Twentynine Palms, California Marine Training grounds in August 2000, the other took place at BBN offices in Cambridge, Massachusetts in October 2011. Dr. Gail Mitchell of BBN coordinated this work for BBN, DARPA's SensIT integration contractor. Both demostrations used the Brooks-Iyengar fusion approach to combine sensor readings in real-time. Acoustic, seismic, and motion dectection readings from multiple sensors were combined and fed into a distributed tracking system. The first deployment was effectivem but noisy. The secound demonstration built on the success of the first testing California. An improved outfielder algorithm was used to determine which node was best situated to continue existing tracks. This work was an essential precursor to the Emergent Sensor Plexus MURI from Penn State Applied Reasearch Laboratory (PSU/ARL) with Dr. Sashi Phoha as PI. In that MURI, researchers from PSU/ARL, Duke, University of Wisconsin, UCLA, Cornell, and LSU extended SensIT's advances to create a practical and survivable sensor network applications.

Application of this algorithm in modern day linux operating system for combining data in fault-tolerance.(MINIX)

In 1996, Iyengar's group, in collaboration with Brooks and with funding form Oak Ridge National Laboratory and office of Naval Research, Invented a method of fault tolerance modeling that offers a computationally inspired real-time management solution. This work has emerged in new versions of real time extensions to Linux Operating Systems. Many of these algorithms were used and installed in the RT Linux Operating System. They are now working on formal model verification by incorporating the algorithms into a new embedded kernel for robotic applications.

The profound contribution of the Brooks-Iyengar Distributed Computational Sensing work has enhanced new real-time features by adding fault tolerant capabilities.

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"...The seminal work of professor Iyengar and his colleagues on sensor networks infestructure constitutes a foundation for sensor placement and optimization of information obtained from sensor networks.The ideas and algorithms introduced by Iyengar et al. Played an important role in Telcordia patent 019576 entitled "Method for placement of sensors for Surveillance" and Motorola patent 7688793 entitled, "Wireless sensor node group affiliation method and apparatus," both of which build up upon his pioneering work.

I belive that professor Iyengar work on sensor deployment, coverage, and surveillance has led to major advances in network technologies...."

Marek Rusinkiewicz Vice President for research and General Manager of Telecordia

"...the impact of this work has been significant. The Brooks-Iyengar Algorithm makes it possible to extract reliable data from sensor networks when some sensors are unreliable, thus adding a fault tolerant aspect. Unreliable sensor data are typically the case in systems that Raytheon develops for its customers, so applications of the algorithm made Raytheon and its customers more successful without increased investment in improved sensor reliability. This saved money, always important in today's increasingly budget conscious environment.

...the Brooks-Iyengar algorithm continues to have significant impact where its is applied to Raytheon's programs.

Intelligence, Information and Service Raytheon

Early Glaucoma Detection

Dr. Iyengar and his colleagues at Nulogix have invented a device with an innovative technology that enables a person to see changes in intra ocular pressure (IOP) when he looks at his eye in the mirror. The purpose of this invention is to ensure that glaucoma's silent damage is detected as early as possible, well ahead of any damage. It will be especially useful to people who are at high risk of getting their retina damaged by increased intra ocular pressure, such as those with high blood pressure or a genetic history of glaucoma.

The U.S. Patent Office and the World Intellectual Property Organization have published patent applications filed by Nulogix for this invention. The company was recently named a finalist in the Enterprise Charlotte Economic Council's "Innovation 2 Industry Florida" competition for this technology and is seeking investment and clinical partners.

More precise radiotherapy treatment of lung tumors

S. S. Iyengar, Xin Li , Huanhuan Xu, Supratik Mukhopadyay, N. Balakrishana, Amit Sawant and Puneeth Iyengar worked towards providing a better more precise radiotherapy treatment of lung tumor computer in January 2012. A computational framework for modeling the respiratory motion of lung tumors provides a 4D parametic representation that tracks, analyzes, and models movement to provide more accurate guidance in the planning and delivery of lung tumor radiotherapy.

Oceanic Research

His research provided the foundation for a much larger effort involving U.S. Navy laboratories, industry partners and leading universities to construct next-generation U.S. Navy surveillance systems. His work was the centerpiece for the Navy's pioneering efforts in developing computerized image analysis system leading to the first fully automated U.S. Navy system for interpretation of satellite images of the ocean. his work on image analysis is the fundamental framework for today's operational U.S. Navy systems. Expanding the fronties of image analysis science, and are captured in his book, Advances in Distributed Sensor Integration: Applications in Theory, published in 1995 and his new, soon to be published work, The Design and Analysis of Algorithms for Processing Digital Satellite IR images.

Contributions to U.S. Navy

Dr. Iyengar's research on image processing systems was used in 1988-93 by the U.S. Navy as a centerpiece of architecture. Dr. Iyengar addressed the problem of linking of low level features of oceanographic objects. without useful result in this key aream the work of other laboratories involved with this project could not have been integrated into a working prototype system. In three years Dr. Iyengar's research served well as the centerpiece of his pioneering efforts in computerized image analysis systems.

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"...Prof. Iyengar's use of non-linear Probabilistic relaxation to perform feature labeling was innovative and well executed. His work was a key factor leading to the frist fully automated interpretion of a satellite image of the ocean in 1989..."

Ronald Holyer Head, Computer Sciences Section

Florida Public Hurricane Loss Model

The "Florida Public Hurricane Loss Model" (FPHLM) project is an initiative that is funded by the Florida Office of Insurance Regulation (OIR) to develop a computer model in homeowner insurance rate-making policy. Its importance and impact on all Floridians need not be over-emphasized since it is the first public model that can assess hurricane risk, and predict annual expected insured residential losses in Florida for an insurance company, zip code, county, or for the entire state. This model has been approved by the Florida Commission on Hurricane Loss Projection Methodology in 2007, 2008, and 2009.

The FPHLM is a large-scale project consisting of five major components: meteorology, engineering, actuarial science, statistics, and computer science; and therefore, the person in charge of managing the software engineering aspect of the model needs to be able to understand all components and to technically interface with experts (researches and professors from Hurricane Research Division/NOAA, University of Florida, Florida Institute of Technology, University of Miami, and Florida State University) from each of the components. Since the FPHLM is an open and public model that has been developed primarily by university experts without the influence from either the insurance industry or the state regulators, it makes the rate evaluation process more objective and consequently has a direct impact on the whole state, as it enables the regulators to justify, by rejecting or accepting, insurance rate increases based on an independent and transparent model. The FPHLM is open to public scrutiny and provides a benchmark against which to compare and validate the estimates of insurance losses and risks produced by the less-transparent models developed by the private industry. The importance of homeowner rate-making policy in Florida makes FPHLM a project that has a critical and measurable impact on Floridians.

Among other factors, OIR utilizes the FPHLM's output to generate estimated insurance losses for the policy portfolio of all insurance companies in Florida that request a change in their homeowner insurance rates. Since 2006, the FPHLM has provided this output to OIR over 480 times. This is an incredible amount of work with tremendous impact on the citizens of the state. Besides services provided to OIR, in 2009 we began to provide services directly to insurance industry using our model. Frequently, insurance companies request modeled results from the FPHLM according to their own specifications and requirements. Providing such results requires a thorough understanding of actuarial concepts and capabilities of the individual models. The FPHLM output has been provided to the insurance industry over 70 times, and the number of companies requesting services is increasing.

Co-invented the Cognitive Information Processing Shell

the Cognitive Information Processing Shell is a complex event processing architecture and engine which recognize and reponds to complex patterns in mission critical, real time applications.

S. S. Iyengar, Supratik Mukhopadhyay, Christopher Steinmuller, and Xin Li "Preventing Future Oil Spills with Software-Based event Detection", IEEE Computers, pp: 76-78, August 2010.

His Research work was shown on the Discovery Channel, History Channel, Local ABC news affiliates (WBRZ News), Fox TV channel (Fox 44 WGMB), Youtube and Major news outlets (LSUReville, The Advocate) around the world, over 60 million homes distributed worldwide (Russia, Korea, Europe, China, India etc.)

One of Dr. Iyengar's Students Brian Obe's love of digital artistry emerged in 1990 with a visit to LSU robotics research laboratory where he was introduced to many techniques and that ended him getting an animation award at the 78th Academy Awards.

One of Dr. Iyengar’s project along with Dr. Phoha (LA Tech) and Dr. Kannan (LSU) entitled “Fast Page Allocation on a Sever Using Self-Organization Properties of Neural Networks” was selected to be in the world’s Best Technology Showcase in the year 2007. This is a highly competitive summit and other companies were Alos Alamos National Labs, John Hopkins University, EPS, NASA and many others.

• "Professor receives large grants from Navy, Army" [Photo]

• Elected Fellow of ACM [Photo]

• "For the Record" [Photo]

Director of the Discoverlab