Research Specialist
Saban Research Institute
Childrens Hospital Los Angeles, MS 139
4650 W. Sunset Blvd.
Los Angeles, CA 90027
Email: anandvp AT usc DOT edu
Phone: 323-361-2413
Fax: 323-361-3512
Current Research
Resource optimization for sensor networks
I am working with Dr. Ashit Talukder on developing a sensor network-based remote health monitoring system. The project is funded by the NIAAA/NIH. The goal is to develop a system that would enable a person's health condition to be continuously monitored over long distance communication networks. The system consists of minimally invasive medical sensors attached to a person (also called a body sensor network. The sensor measurements are collected by a sensor node or "mote" - a miniature computer with its own battery and wireless communication ability. We have developed our own "mote" that can communicate over multipe radio frequencies and over the cellular phone network. Sensor measurements are transmitted by the motes over an available wireless network to a remote computer for monitoring and storage in a database.
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| Overview of the remote health monitoring system | Custom designed mote (click for a larger view with an attached ISF sensor) |
My focus in this project is on developing algorithms for optimizing the limited resources (such as battery power) on the sensor nodes. These algorithms adapt the operating parameters of the sensor nodes (such as the sampling rates of the sensors) in order to extend the system lifetime while still responding adequately to critical events. As these algorithms have to be executed on the low power processors on the sensor nodes, there is a need to make such algorithms computationally simple. Some of the techniques I have worked on are described below.
Distributed embedded fault-tolerant control of resource constrained sensor networks
I am using Markov decision processes (MDPs) and Kalman filters to derive a distributed policy that can be executed on each sensor node. The MDP framework is used to derive a control policy under the assumption that the internal state of all sensors is always available. This global policy is calculated before deployment. After deployment, each sensor node maintains an estimate of the global state in order to execute the control policy. The nodes communicate to exchange state information only when the variance of the estimate becomes large. The actual controller is implemented as a simple lookup-table on the sensor nodes. The Kalman filter is used to fuse measurements from multiple sensors into one estimate of the criticality of the sensed event.
Model Predictive Control for sensor networks
Model Predictive Control (MPC) is an established control method and is often used for controlling chemical processes in large plants. This technique assumes that a mathematical model of the system to be controlled and its physical limits are available. The optimal control inputs are calculated by solving a constrained optimization problem that uses this system model in its objective function and the physical limits as constraints. We have adapted this technique for controlling the sensors in a sensor network. We first formulate a model that characterizes the effect of sensor operation (such as sampling rates) on the system state (for instance, energy reserves). We then formulate a multi-objective function that describes the conflicting demands - reduce energy consumption, but sense the environment with high accuracy. The solution to this optimization problem is then used as the control for the sensors in the sensor network.
Adaptive sampling for a coastal environment monitoring network
We applied the technique of controlling the resources in a sensor network using a Model Predictive Controller for use in a coastal monitoring network in the New York harbor region (NYHOPS). The NYHOPS system produces forecasts of ocean conditions using a hydrodynamic model. We showed how this forecast could be improved by incorporating real-time sensor measurements from fixed and mobile sensors. Our MPC controller determined the optimal operational parameters of all the controllable components in the network. These included the sampling rates of the sensors, paths of Unmanned Underwater Vehicles (UUVs), and communication paths for sensor data.
Transmitting wavelet coefficients over a lossy radio link
Sensor measurements are often compressed before transmission over a wireless link to reduce the energy cost of transmission. Applying the wavelet transform is one method for compression. However, wireless transmission suffers from packet drops. The loss of wavelet coefficients due to a packet drop affects the fidelity of the reconstructed signal at the receiver. We have developed an algorithm to distribute wavelet coefficients among multiple transmission packets to minimize the error introduced in the reconstruction step due to packet drops. This algorithm utilizes a statistical model of the correlation between coefficients. We also developed an interpolation algorithm to estimate dropped coefficients from available correlated coefficients.
Previous work
I was a post-doctoral researcher at the Robotics Research Lab of Prof. Maja Mataric and Prof. Gaurav Sukhatme. While there, I worked on modeling the movement patterns of humans in their everyday environments. More information on this work is on this page.
I worked with Prof. Adnan Darwiche at the UCLA Computer Science Department. I embedded reasoning algorithms based on efficient representations of propositional logic into a Sony Aibo robot. Details of this work are on this page.
My Ph.D. dissertation work was at the UCLA Computer Science Department under Prof. Michael G. Dyer. My research demonstrated how agents built with a connectionist architecture could construct arbitrary physical structures in a simulated environment. We showed how an agent could represent arbitrary 2-dimensional patterns in a connectionist map, learn to exploit spatial and temporal correlations in the environment, and compute an efficient sequence of construction tasks by spreading activation over a connectionist network. Details of this work are on this page.