Calibration as parameter estimation in sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
The Delay-Constrained Minimum Spanning Tree Problem
ISCC '97 Proceedings of the 2nd IEEE Symposium on Computers and Communications (ISCC '97)
Proceedings of the 3rd international conference on Embedded networked sensor systems
Simultaneous localization, calibration, and tracking in an ad hoc sensor network
Proceedings of the 5th international conference on Information processing in sensor networks
The design and implementation of a self-calibrating distributed acoustic sensing platform
Proceedings of the 4th international conference on Embedded networked sensor systems
Blind calibration of sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
CaliBree: A Self-calibration System for Mobile Sensor Networks
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Sensor network data fault types
ACM Transactions on Sensor Networks (TOSN)
Blindly calibrating mobile sensors using piecewise linear functions
SECON'09 Proceedings of the 6th Annual IEEE communications society conference on Sensor, Mesh and Ad Hoc Communications and Networks
Sensor faults: Detection methods and prevalence in real-world datasets
ACM Transactions on Sensor Networks (TOSN)
A collaborative approach to in-place sensor calibration
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Sensor deployment for fault diagnosis using a new discrete optimization algorithm
Applied Soft Computing
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Technological advances in nanotechnology enabled the use of microelectromechanical systems (MEMS) in various application areas. With the integration of various sensor devices into MEMS, autonomously calibrating these sensors become a major research problem. When performing calibration on real-world embedded sensor network deployments, random errors due to internal and external factors alter the calibration parameters and eventually effect the calibration quality in a negative way. Therefore, during autonomous calibration, calibration paths which has low cost and low error values are preferable. To tackle the calibration problem on embedded wireless sensor networks, we present an energy efficient and minimum error calibration model, and also prove that due to random errors the problem turns into an NP-complete problem. To the best of our knowledge this is the first time a formal proof is presented on the complexity of an iterative calibration based problem when random errors are present in the measurements. We also conducted heuristic tests using genetic algorithm to solve the optimization version of the problem, on various graphs. The NP-completeness result also reveals that more research is needed to examine the complexity of calibration in a more general framework in real-world sensor network deployments.