The design and implementation of a self-calibrating distributed acoustic sensing platform
Proceedings of the 4th international conference on Embedded networked sensor systems
An empirical study of collaborative acoustic source localization
Proceedings of the 6th international conference on Information processing in sensor networks
Tiny-sync: Tight time synchronization for wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Emstar: A software environment for developing and deploying heterogeneous sensor-actuator networks
ACM Transactions on Sensor Networks (TOSN)
Design and evaluation of a hybrid sensor network for cane toad monitoring
ACM Transactions on Sensor Networks (TOSN)
An Empirical Study of Collaborative Acoustic Source Localization
Journal of Signal Processing Systems
Distributed algorithm for node localization in wireless ad-hoc networks
ACM Transactions on Sensor Networks (TOSN)
Considerations on quality metrics for self-localization algorithms
IWSOS'11 Proceedings of the 5th international conference on Self-organizing systems
Physical layer sensing using long pseudo noise codes
Proceedings of the Seventh ACM International Conference on Underwater Networks and Systems
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The area of sensor networks promises to support the biological and physical sciences by enabling measurements that were previously impossible. This is accomplished by pushing intelligence into the network and closer to the sensors, enabling sensing to be accomplished at much higher scales and densities with lower cost. Recently, interest in acoustic sensing problems has increased, including the localization and monitoring of birds, wolves, and other species; as well as of localization of electronic devices themselves. This has spurred the development of a rapidly-deployable distributed acoustic sensing platform. A key problem in the development of this platform is the acoustic array calibration problem, which estimates the locations and orientations of a distributed collection of acoustic sensors. We present a system composed of a set of independent acoustic nodes that automatically determines calibration parameters including the relative location and orientation (X, Y, Z, Θ) of each array. These relative coordinates are then fitted to one or more survey points to relate the relative coordinates to a physical map. The application that computes these estimates is itself a distributed sensing application. In this work we present a solution to this position estimation problem, demonstrating a complete vertical application built above a stack of re-usable system components and distributed services, implemented on a deployable embedded hardware platform. We describe: the hardware platform itself; Emstar, a software framework for developing complex embedded system software; a time-synchronized sampling layer; a multihop reliable multicast coordination primitive; a time-of-flight acoustic ranging and direction-of-arrival (DOA) estimation layer; and the top-level application that estimates the position and orientation of each array. We present the results of controlled tests of the ranging and DOA estimation system, as well as the results of deployment experiments in both an urban environment and a forested environment. These results demonstrate that our system outperforms other similar systems, and that it can achieve the sufficient accuracy for anticipated applications, such as bird localization.