Introduction to Distributed Algorithms
Introduction to Distributed Algorithms
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Poster abstract: cooperative tracking with binary-detection sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
An analysis of a large scale habitat monitoring application
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Resilient Localization for Sensor Networks in Outdoor Environments
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Localization for anchoritic sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Radio interferometric Quasi Doppler bearing estimation
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Antenna diversity using single antenna in robot communication
Digital Signal Processing
Resilient localization for sensor networks in outdoor environments
ACM Transactions on Sensor Networks (TOSN)
Force-directed approaches to sensor localization
ACM Transactions on Sensor Networks (TOSN)
A case against routing-integrated time synchronization
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Wireless sensor node localization by multisequence processing
ACM Transactions on Embedded Computing Systems (TECS)
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We develop a localization algorithm based on global environmental events observed by a sensor network. Examples of such events include the sound of thunder, the shades of moving clouds, or the vibrations in seismic data. Because our localization method does not generate signals for distance measurements, it saves energy. In fact, the algorithm may use existing sensor recordings to determine the locations of nodes at which the recordings were taken. Moreover, the method does not accumulate errors, making it also effective for large and sparse sensor networks. The localization uses time synchronization; we provide an algorithm to compensate for clock synchronization errors. Versions for both two dimensional and three dimensional localization of the algorithm are presented. Simulation results suggest that the algorithm can provide a high degree of accuracy when many events are recorded.