Calibration as parameter estimation in sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Nonparametric belief propagation for self-calibration in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
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Foundations of Computational Mathematics
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IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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IEEE Transactions on Image Processing
CaliBree: A Self-calibration System for Mobile Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
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ACM Transactions on Sensor Networks (TOSN)
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Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
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SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
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ACM Transactions on Sensor Networks (TOSN)
ACM Transactions on Sensor Networks (TOSN)
Proceedings of the 9th International Workshop on Adaptive and Reflective Middleware
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DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
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EWSN'12 Proceedings of the 9th European conference on Wireless Sensor Networks
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ACM Computing Surveys (CSUR)
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ACM Transactions on Embedded Computing Systems (TECS)
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IEEE/ACM Transactions on Networking (TON)
Packet-level attestation (PLA): A framework for in-network sensor data reliability
ACM Transactions on Sensor Networks (TOSN)
On the complexity of energy efficient pairwise calibration in embedded sensors
Applied Soft Computing
System-level calibration for data fusion in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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This paper considers the problem of blindly calibrating sensor response using routine sensor network measurements. We show that as long as the sensors slightly oversample the signals of interest, then unknown sensor gains can be perfectly recovered. Remarkably, neither a controlled stimulus nor a dense deployment is required. We also characterize necessary and sufficient conditions for the identification of unknown sensor offsets. Our results exploit incoherence conditions between the basis for the signals and the canonical or natural basis for the sensor measurements. Practical algorithms for gain and offset identification are proposed based on the singular value decomposition and standard least squares techniques. We investigate the robustness of the proposed algorithms to model mismatch and noise on both simulated data and on data from current sensor network deployments.