Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Robotics-based location sensing using wireless ethernet
Proceedings of the 8th annual international conference on Mobile computing and networking
Calibration of visual sensors and actuators in distributed computing platforms
Proceedings of the third ACM international workshop on Video surveillance & sensor networks
SensEye: a multi-tier camera sensor network
Proceedings of the 13th annual ACM international conference on Multimedia
Distributed acoustic conversation shielding: an application of a smart transducer network
Proceedings of the First ACM workshop on Sensor and actor networks
Distributed localization for anisotropic sensor networks
ACM Transactions on Sensor Networks (TOSN)
Self-localization based on ambient signals
ALGOSENSORS'10 Proceedings of the 6th international conference on Algorithms for sensor systems, wireless adhoc networks, and autonomous mobile entities
Self-Localization based on Ambient Signals
Theoretical Computer Science
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In this paper, we present a novel approach to automatically determine the positions of sensors and actuators in an ad-hoc distributed network of general purpose computing platforms. The formulation and solution accounts for the limited precision in temporal synchronization among multiple platforms. The theoretical performance limit for the sensor positions is derived via the Cramer-Rao bound. We analyze the sensitivity of localization accuracy with respect to the number of sensors and actuators as well as their geometry. Extensive Monte Carlo simulation results are reported together with a discussion of the real-time system. In a test platform consisting of 4 speakers and 4 microphones, the sensors' and actuators' three dimensional locations could be estimated with an average bias of 0.08 cm and average standard deviation of 3.8 cm.