A passive approach to sensor network localization

Sensor networks present the opportunity to accurately localize the phenomena of interest. To be able to do so however, sensor nodes, need themselves to be accurately localized. We present an algorithm to do this based on uncontrolled environmental sounds observed by each of the sensor nodes. A probabilistic generative model is presented and it is shown that the sensor node localization problem is equivalent to maximum likelihood estimation in the model. Experimental results are presented for both simulated sensor nodes and Crossbow MICA2 sensor nodes.

Authors

• Rahul Biswas
• Sebastian Thrun

Keywords

• Acoustic sensors
• Sensor phenomena and characterization
• Computer aided manufacturing
• Computerized monitoring
• Bayesian methods
• Synchronization
• Clocks
• Computer science
• Maximum likelihood estimation
• Computational modeling
• Sensor Networks
• Local Problems
• Probabilistic Generative Model
• High-dimensional
• Bayesian Model
• Gradient Descent
• Conditional Probability
• Unique Solution
• Global Positioning System
• Specific Network
• Solution Space
• Recording Time
• Local Algorithm
• Unknown Variables
• Actual Error
• Global Frame
• Global Reference Frame
• Number Of Sensor Nodes
• Number Of Sounds