Sundararaj Iyengar

Conventional Networking systems inherently assume the structure of information content exchanged in a network. Distributed Routing methods and policies will not be taken into account in these scenarios. For applications in extreme environments using sensor networks, we must develop strategies and protocols which exploit the variability and dynamics of the information structure between different types of sources, users, and applications. This talk presents an overview of the author’s contribution for the last two decades on the challenges involved in the design and deployment of sensors and for modeling of information topological structure. As an example, we discuss the challenges involved in the MobiCon system developed by Korean Advanced Institute of Science and Technology (KAIST). Also this seminar provides an overview of a famous Brooks-Iyengar distributed sensing algorithm, which is being used by many agencies such as DARPA, ONR, Raytheon/BBN, LINUX OS, and many other organizations.

Scientist vision and voice must extend to the training of next generation workforce. Investment in Science and Innovation now will result for decades to come. The convergence of Science & Technology is the focus of this talk. The speaker will present a future of Science and innovation for the 21st century. This talk covers a broad spectrum of the acceleration and commercialization of technology for the next decade.

Wireless sensor networks are envisioned to consist of thousands of devices, each capable of some limited computation, communication, and sensing, operating in an unattended mode. According to a recent National Research Council report, the use of such networks of embedded systems "could well dwarf previous revolutions in the information revolution." To our knowledge, we are the first to propose a solution to the fault-event disambiguation problem in sensor networks. Our proposed solution, in the form of Bayesian fault recognition algorithms, exploits the notion that measurement error due to faulty equipment are likely to be uncorrelated, while environmental conditions are spatially correlated. We show through theoretical and simulation results that the optimal threshold decision algorithm we present can reduce sensor measurement faults by as much as 85-95 percent for fault rates up to 10 percent.