title={Quarter sphere based distributed anomaly detection in wireless sensor networks},
author={Rajasegarar, Sutharshan and Leckie, Christopher and Palaniswami, Marimuthu and Bezdek, James C},
booktitle={2007 IEEE International Conference on Communications},
pages={3864--3869},
year={2007},
organization={IEEE}
}
@inproceedings{moshtaghi2011,
title={Incremental elliptical boundary estimation for anomaly detection in wireless sensor networks},
author={Moshtaghi, Masud and Leckie, Christopher and Karunasekera, Shanika and Bezdek, James C and Rajasegarar, Sutharshan and Palaniswami, Marimuthu},
booktitle={2011 IEEE 11th international conference on data mining},
title="Humidity Sensor Drift Detection and Correction Based on a Kalman Filter with an Artificial Neural Network for Commercial Cultivation of Tropical Orchids",
booktitle="Computational Intelligence in Information Systems",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="140--149",
abstract="Polymer dielectric-based humidity sensors used in the orchid greenhouse monitoring system usually work improperly after continuously being used in a high humid condition for some time (e.g., after eight months). This problem, called sensor drift, has been broadly observed. This paper proposes a simple data-driven technique based on a Kalman filter with an artificial neural network to detect the drift and correct data. The combination of two proposed measures based on the {\$}{\$}L^1{\$}{\$}L1distance and the cosine similarity is used to determine the sensor's status, which is later used to adjust the Kalman gain accordingly. That is, when the sensor malfunctions, the gain is biased toward the prediction. When the sensor is in the normal status, the gain is biased toward the measurement. When the sensor drift is detected, the gain varies in between the prediction and the measurement. The experimental results show that the proposed method could reduce the accumulated mean absolute deviation by approximately 55.66{\%}.",
author={Ni, Kevin and Ramanathan, Nithya and Chehade, Mohamed Nabil Hajj and Balzano, Laura and Nair, Sheela and Zahedi, Sadaf and Kohler, Eddie and Pottie, Greg and Hansen, Mark and Srivastava, Mani},
journal={ACM Transactions on Sensor Networks (TOSN)},
volume={5},
number={3},
pages={1--29},
year={2009},
publisher={ACM New York, NY, USA}
}
@article{wu2019,
title={Drift Calibration Using Constrained Extreme Learning Machine and Kalman Filter in Clustered Wireless Sensor Networks},
author={Wu, Jiawen and Li, Guanghui},
journal={IEEE Access},
volume={8},
pages={13078--13085},
year={2019},
publisher={IEEE}
}
@article{barcelo2019,
title={Self-calibration methods for uncontrolled environments in sensor networks: A reference survey},
author={Barcelo-Ordinas, Jose M and Doudou, Messaoud and Garcia-Vidal, Jorge and Badache, Nadjib},
journal={Ad Hoc Networks},
volume={88},
pages={142--159},
year={2019},
publisher={Elsevier}
}
@article{dehkordi2020,
title={A survey on data aggregation techniques in IoT sensor networks},
author={Dehkordi, Soroush Abbasian and Farajzadeh, Kamran and Rezazadeh, Javad and Farahbakhsh, Reza and Sandrasegaran, Kumbesan and Dehkordi, Masih Abbasian},
journal={Wireless Networks},
volume={26},
number={2},
pages={1243--1263},
year={2020},
publisher={Springer}
}
@article{wang2016,
title={Blind drift calibration of sensor networks using sparse Bayesian learning},
author={Wang, Yuzhi and Yang, Anqi and Li, Zhan and Chen, Xiaoming and Wang, Pengjun and Yang, Huazhong},
journal={IEEE Sensors Journal},
volume={16},
number={16},
pages={6249--6260},
year={2016},
publisher={IEEE}
}
@inproceedings{buonadonna2005,
title={TASK: Sensor network in a box},
author={Buonadonna, Philip and Gay, David and Hellerstein, Joseph M and Hong, Wei and Madden, Samuel},
booktitle={Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005.},
pages={133--144},
year={2005},
organization={IEEE}
}
% noise
@inproceedings{elnahrawy2003,
title={Cleaning and querying noisy sensors},
author={Elnahrawy, Eiman and Nath, Badri},
booktitle={Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications},
pages={78--87},
year={2003}
}
@article{stankovic2018,
title={On consensus-based distributed blind calibration of sensor networks},
author={Stankovi{\'c}, Milo{\v{s}} S and Stankovi{\'c}, Srdjan S and Johansson, Karl Henrik and Beko, Marko and Camarinha-Matos, Luis M},
journal={Sensors},
volume={18},
number={11},
pages={4027},
year={2018},
publisher={Multidisciplinary Digital Publishing Institute}
}
@inproceedings{kumar2013,
title={Automatic sensor drift detection and correction using spatial kriging and kalman filtering},
author={Kumar, Dheeraj and Rajasegarar, Sutharshan and Palaniswami, Marimuthu},
booktitle={2013 IEEE International Conference on Distributed Computing in Sensor Systems},
pages={183--190},
year={2013},
organization={IEEE}
}
@inproceedings{barcelo2018,
title={Calibrating low-cost air quality sensors using multiple arrays of sensors},
author={Barcelo-Ordinas, Jose M and Garcia-Vidal, Jorge and Doudou, Messaoud and Rodrigo-Mu{\~n}oz, Santiago and Cerezo-Llavero, Albert},
booktitle={2018 IEEE Wireless Communications and Networking Conference (WCNC)},
pages={1--6},
year={2018},
organization={IEEE}
}
@article{ramanathan2006,
title={Rapid deployment with confidence: Calibration and fault detection in environmental sensor networks},
author={Ramanathan, Nithya and Balzano, Laura and Burt, Marci and Estrin, Deborah and Harmon, Tom and Harvey, Charlie and Jay, Jenny and Kohler, Eddie and Rothenberg, Sarah and Srivastava, Mani},
year={2006}
}
@inproceedings{hasenfratz2012,
title={On-the-fly calibration of low-cost gas sensors},
author={Hasenfratz, David and Saukh, Olga and Thiele, Lothar},
booktitle={European Conference on Wireless Sensor Networks},
pages={228--244},
year={2012},
organization={Springer}
}
@article{maag2017,
title={SCAN: Multi-hop calibration for mobile sensor arrays},
author={Maag, Balz and Zhou, Zimu and Saukh, Olga and Thiele, Lothar},
journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
