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Airplanes aloft as a sensor network for wind forecasting

Authors:

Zachary Horvitz,

Eric HorvitzAuthors Info & Claims

IPSN '14: Proceedings of the 13th international symposium on Information processing in sensor networks

Pages 25 - 34

Published: 15 April 2014 Publication History

Abstract

We explore the feasibility of using commercial aircraft as sensors for observing weather phenomena at a continental scale. We focus specifically on the problem of wind forecasting and explore the use of machine learning and inference methods to harness air and ground speeds reported by aircraft at different locations and altitudes. We validate the learned predictive model with a field study where we release an instrumented high-altitude balloon and compare the predicted trajectory with the sensed winds. The experiments show the promise of using airplane in flight as a large-scale sensor network. Beyond making predictions, we explore the guidance of sensing with value-of-information analyses, where we consider uncertainties and needs of sets of routes and maximize information value in light of the costs of acquiring data from airplanes. The methods can be used to select ideal subsets of planes to serve as sensors and also to evaluate the value of requesting shifts in trajectories of flights for sensing.

References

[1]

http://amdar.noaa.gov/docs/bams/.

[2]

http://amdar.noaa.gov/docs/mamrosh-ams-98/.

[3]

http://amdar.noaa.org.

[4]

Z. Abrams, A. Goel, and S. Plotkin. Set k-cover algorithms for energy efficient monitoring in wireless sensor networks. In IPSN, 2004.

Digital Library

[5]

A. Azevedo-Filho and R. Shachter. Laplace's method approximations for probabilistic inference in belief networks with continuous variables. In Uncertainty in Artificial Intelligence, 1994.

Digital Library

[6]

L. Boccia, P. Pace, G. Amendola, and G. D. Massa. Low multipath antennas for GNSS-based attitude determination systems applied to high-altitude platforms. In GPS Solutions, 2008.

[7]

N. Chen, Z. Qian, X. Meng, and I. T. Nabney. Short-term wind power forecasting using gaussian processes. In IJCAI, 2013.

Digital Library

[8]

N. A. C. Cressie. Statistics for Spatial Data (revised edition). Wiley, 1993.

[9]

T. Evgeniou, M. Pontil, and T. Poggio. Regularization networks and support vector machines. Advances in Computational Mathematics, 13(1), 2000.

[10]

FAA. Aviation Weather Services, Advisory Circular AC 00-45G Change 1. Aviation Supplies and Academics, 2006.

[11]

Y. Freund, H. S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3), 1997.

Digital Library

[12]

R. Guhaniyogi, A. O. Finley, S. Banerjee, and A. E. Gelfand. Adaptive gaussian predictive process models for large spatial datasets. Environmetrics, 22(8), 2011.

[13]

E. Horvitz, J. Breese, and M. Henrion. Decision theory in expert systems and artificial intelligence. International Journal of Approximate Reasoning, 1988.

Digital Library

[14]

R. A. Howard. Information value theory. IEEE Transactions on Systems Science and Cybernetics, 1966.

[15]

Jeppsen. Private Pilot Manual. Sanderson, 2001.

[16]

X. Jiang, B. Dong, L. Xie, and L. Sweeney. Adaptive gaussian process for short-term wind speed forecasting. In Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence, 2010.

Digital Library

[17]

A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Active learning with Gaussian Processes for object categorization. In ICCV, 2007.

[18]

A. Kapoor, E. Horvitz, and S. Basu. Selective supervision: Guiding supervised learning with decision-theoretic active learning. In IJCAI, 2007.

Digital Library

[19]

A. Krause, E. Horvitz, A. Kansal, and F. Zhao. Towards community sensing. In IPSN, 2008.

Digital Library

[20]

A. Krause, J. Leskovec, C. Guestrin, J. VanBriesen, and C. Faloutsos. Efficient sensor placement optimization for securing large water distribution networks. Journal of Water Resources Planning and Management, 136(6), 2008.

[21]

A. Krause, A. Singh, and C. Guestrin. Near-optimal sensor placements in gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research, 9, 2008.

Digital Library

[22]

D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4), 1992.

Digital Library

[23]

C. E. Rasmusen and C. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.

Digital Library

[24]

F. Zhao, J. Shin, and J. Reich. Information-driven dynamic sensor collaboration for tracking applications. IEEE Signal Processing, 19(2), 2002.

Cited By

Trüb RMoser DSchäfer MPinheiro RLenders VMottola LGao JZhang P(2018)Monitoring meteorological parameters with crowdsourced air traffic control dataProceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks10.1109/IPSN.2018.00010(25-36)Online publication date: 11-Apr-2018
https://dl.acm.org/doi/10.1109/IPSN.2018.00010
Ahmad TChandra RKapoor ADaum MHorvitz E(2017)Wi-FlyProceedings of the 16th ACM Workshop on Hot Topics in Networks10.1145/3152434.3152458(43-49)Online publication date: 30-Nov-2017
https://dl.acm.org/doi/10.1145/3152434.3152458
Chakrabarti AMarwah MArlitt M(2016)Robust Anomaly Detection for Large-Scale Sensor DataProceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/2993422.2993583(31-40)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1145/2993422.2993583
Show More Cited By

Index Terms

Airplanes aloft as a sensor network for wind forecasting
1. Applied computing
  1. Physical sciences and engineering
    1. Aerospace
    2. Earth and atmospheric sciences

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Information & Contributors

Information

Published In

cover image ACM Conferences

IPSN '14: Proceedings of the 13th international symposium on Information processing in sensor networks

April 2014

368 pages

ISBN:9781479931460

General Chair:
Adam Wolisz
TU Berlin, Germany
,
Program Chairs:
Jie Liu
Microsoft Research, USA
,
Lin Zhong
Rice University, USA

Sponsors

IEEE
SIGBED: ACM Special Interest Group on Embedded Systems
IEEE-CS\DATC: IEEE Computer Society

Publisher

IEEE Press

Publication History

Published: 15 April 2014

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IPSN '14 Paper Acceptance Rate 23 of 111 submissions, 21%;

Overall Acceptance Rate 143 of 593 submissions, 24%

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Cited By

Trüb RMoser DSchäfer MPinheiro RLenders VMottola LGao JZhang P(2018)Monitoring meteorological parameters with crowdsourced air traffic control dataProceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks10.1109/IPSN.2018.00010(25-36)Online publication date: 11-Apr-2018
https://dl.acm.org/doi/10.1109/IPSN.2018.00010
Ahmad TChandra RKapoor ADaum MHorvitz E(2017)Wi-FlyProceedings of the 16th ACM Workshop on Hot Topics in Networks10.1145/3152434.3152458(43-49)Online publication date: 30-Nov-2017
https://dl.acm.org/doi/10.1145/3152434.3152458
Chakrabarti AMarwah MArlitt M(2016)Robust Anomaly Detection for Large-Scale Sensor DataProceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments10.1145/2993422.2993583(31-40)Online publication date: 16-Nov-2016
https://dl.acm.org/doi/10.1145/2993422.2993583
Grover AKapoor AHorvitz ECao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)A Deep Hybrid Model for Weather ForecastingProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783275(379-386)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2783275

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