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SensorFlock: an airborne wireless sensor network of micro-air vehicles

Allred, Jude, Hasan, Ahmad Bilal, Panichsakul, Saroch, Pisano, William, Gray, Peter, Huang, Jyh, Han, Richard, Lawrence, Dale, Mohseni, Kamran·2007·Proceedings of the 5th international conference on Embedded networked sensor systems

Abstract

An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique three-dimensional vantage for monitoring wildlife and ecological systems. This paper describes a complete implementation of our SensorFlock airborne WSN, spanning the development of our MAV airplane, its avionics, semi-autonomous flight control software, launch system, flock control algorithm, and wireless communication networking between MAVs. We present experimental results from flight tests of flocks of MAVs, and a characterization of wireless RF behavior in air-to-air communication as well as air-to-ground communication.

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Metadata

Authors
Allred, J., Hasan, A. B., Panichsakul, S., Pisano, W., Gray, P., Huang, J., Han, R., Lawrence, D., & Mohseni, K.
Venue
Proceedings of the 5th international conference on Embedded networked sensor systems
Publisher
ACM
Published
2007-11-06
Pages
117-129
OpenAlex
W2133444666
Cited by
177
References
0
FWCI
23.32
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: SensorFlock: an airborne wireless sensor network of micro-air vehicles. https://app.cochat.ai/papers/10.1145/1322263.1322275
The paper
Allred, J., Hasan, A. B., Panichsakul, S., Pisano, W., Gray, P., Huang, J., Han, R., Lawrence, D., & Mohseni, K. (2007). SensorFlock: an airborne wireless sensor network of micro-air vehicles. Proceedings of the 5th international conference on Embedded networked sensor systems. https://doi.org/10.1145/1322263.1322275

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.