CENS Technical Seminar Series

Adaptive Sampling for Field Estimation using an Actuated Sensor Network

Invited Speaker: Mohammad Rahimi
Date: April 22, 2005
Time: 1:00 PM - 2:00 PM
Venue: 4760 Boelter Hall, UCLA

Abstract

Monitoring environmental phenomena by distributed sensor sampling confronts the challenge of unpredictable variability in the spatial distribution of phenomena often coupled with demands for a high spatial sampling rate. The introduction of actuation-enabled robotics sensors permits a system to optimize the sampling distribution through runtime adaptation. However, such systems must efficiently dispense sampling points or otherwise suffer from poor temporal and spatial response.

In this talk I propose and characterize an active modeling system. In this approach, as the robotic sensor acquires measurement samples of the environment, it builds a model of the phenomenon. Our algorithm is based on an incremental optimization process where the robot supports a continuous, iterative process of collecting samples with maximal coverage in the design space, building the environmental model and predicting sampling point locations that contribute the greatest certainty regarding the phenomenon. It then samples the environment based on a combined measure of information gain and navigation and sampling cost. This can provide significant reductions in the magnitude of field estimation error with a modest navigational trajectory time.

This is joint work with Prof. Mark Hansen at statistics department at UCLA.

Biography

Mohammad Rahimi is senior staff member at Center for Embedded
Networked Sensing (CENS) and Ph.D. candidate at the Robotic Embedded Systems Laboratory (RESL). He is broadly interested in systems issues in embedded robotic systems and wireless sensor networks. He has pioneered development of innovative sensor network platforms and research such as RoboMote, wireless low-power data acquisition and vision in sensor networks that are commercially available by leading sensor network companies.