Demand for and competition over water drive conflicts across the globe and, nationally, in the United States. In the United States, responsibility for water policymaking is spread across levels of governance, regions, populations, and natural resources. Water use policy in the U.S. is fragmented, and at times, contradictory with potentially dire ecological consequences and significant risk to Americans’ way of life. Yet, despite the looming crisis, solving water issues is easier said than done. Political, economic, and cultural factors all contribute to the mish mash of current decisions regarding water use in this country and around the world. This research team aims to propose a way to improve water policy decisions and potentially water use with the aid of Big Data, both hydrological and social.
The research team has built further upon the ideas in their project to pilot a large NSF proposal on handling floods for $1.3M. They have finished a majority of the hydrological modeling that will be integrated with the humanities data through the decision theoretic component. Additionally, the team had to have the decision theoretic component readied first before integrating all the diverse disciplines in the work, so they have 1) mapped the problem of dynamic state/parameter estimation to well-understood problems in least squares theory and random matrix theory, 2) formed a basic decision theory to avoid the contradictions produced by axioms with the real world, and 3) incorporated cognitive constraints through use of the time horizon of optimization. Based on this progress, they have the means to simulate the feedback between policy and society. The team has uncovered several insights, and even answers to some of the questions about how this research could extend to such things as education, healthcare, and government itself. They have also developed a rather powerful usable database with the hydrology and demographic census data integrated. Finally, the team was able to purchase Twitter data in order to work with and understand the feedback loops between policy and social data.
Kartik B. Ariyur leads the Autonomous Systems Laboratory in the School of Mechanical Engineering with collaborations across campus. His group works on several aspects of autonomous operation from sensor design and characterization to multi-vehicle mission planning and health monitoring. His projects range from the use of magnetic maps for cell phone geolocation, and microscopic traffic measurements, to designing equitable water policy with big data. His health monitoring algorithms developed while at working at the Honeywell Labs in Minneapolis currently run on Honeywell APUs in service in commercial aircraft (around 70% of all commercial aircraft); his front end filtering algorithms developed at Qualcomm reside in all CDMA chips in cell-phones. The thresholds and threshold calculation methods he developed for the LAAS (Local Area Augmentation System) will be used for health monitoring of LAAS receivers in all small airports where the system is installed. His PhD work on extremum seeking is used in dozens of industries.
Prior to joining Purdue (from 2002—2008), he worked at the Honeywell Labs in Minneapolis on a variety of problems in navigation, control, communication and surveillance. He worked on the tracking of ground vehicles by aerial vehicles and multi-vehicle path planning on the DARPA SEC program. He developed the Laplacian path planner used on the DARPA OAV-II program. He contributed to the winning DARPA HURT planning and control component for Honeywell. He has authored 15 peer reviewed journal papers, 50 conference papers, and 17 US patents. He co-authored the text, Real-time Optimization by Extremum Seeking Control (John Wiley & Sons, 2003), is Associate editor of the International Journal of Adaptive Control and Signal Processing (Wiley), and a member of the IEEE-CSS Conference Editorial Board. He has been and is on the program committees of several important conferences, including, the ACC, IEEE MSC, IEEE ICCVE, HSCC (hybrid systems control and computation).
At Purdue (from August 2008), he has developed autonomous navigation using natural fields for indoor, pedestrian, and UAV navigation. A sun sensor for compassing and a more sophisticated sun sensor for geolocation have been developed in his lab. His group has developed scalable methods for multi-UAV autonomous operations for specific surveillance tasks. He leads the Purdue portion of the AFRL’s Adaptive G&C ISHM Integration Requirements study.