@article{chandola2009,
  title={Anomaly detection: A survey},
  author={Chandola, Varun and Banerjee, Arindam and Kumar, Vipin},
  journal={ACM computing surveys (CSUR)},
  volume={41},
  number={3},
  pages={1--58},
  year={2009},
  doi     = {10.1145/1541880.1541882},
  publisher={ACM New York, NY, USA}
}
@article{bosman2017,
  author  = {Hedde HWJ Bosman and Giovanni Iacca and Arturo Tejada and Heinrich J Wörtche and Antonio Liotta},
  title   = {Spatiala nomaly detection in sensor networks using neighborhood information},
  journal = {Information Fusion},
  pages   = {41-56},
  volume  = {33},
  year    = {2017},
  doi     = {10.1016/j.inffus.2016.04.007}
}
@article{rajasegarar2010,
  author  = {Sutharshan Rajasegarar and Christopher Leckie and James C. Bezdek and Marimuthu Palaniswami},
  journal = {IEEE transactions on information forensics and security},
  number  = {3},
  volume  = {4},
  title   = {Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks},
  year    = {2010},
  pages   = {518-533}
}
@article{rajasegarar2008,
  author          = {Sutharshan Rajasegarar and Christopher Leckie and Marimuthu Palaniswam},
  title           = {Anomaly detection in wireless sensor networks},
  journal         = {IEEE Wireless Communications},
  volume          = {},
  number          = {},
  year            = {2008}
}     
@article{yu2017,
  title={Recursive principal component analysis-based data outlier detection and sensor data aggregation in IoT systems},
  author={Yu, Tianqi and Wang, Xianbin and Shami, Abdallah},
  journal={IEEE Internet of Things Journal},
  volume={4},
  number={6},
  pages={2207--2216},
  year={2017},
  publisher={IEEE}
}

@article{wang2006,
  author  = {Defeng Wang and Daniel S. Yeung and Eric C.C. Tsang},
  journal = {IEEE transactions on systems, man, and cybernetics - Part B: cybernetics},
  title   = {Structured One-Class Classification},
  volume  = {36},
  number  = {6},
  year    = {2006},
  pages   = {1283-1295}
}
@article{laskov2004,
  author          = {P. Laskov and C. Schäfer and I. Kotenko and K.-R. Müller},
  title           = {Intrusion Detection in Unlabeled Data with Quarter-sphere Support Vector Machines},
  journal         = {Proc. Detectionof Intrusions and Malware \& Vulnerability Assessment},
  volume          = {27},
  number          = {4},
  year            = {2006},
  pages           = {228-236}
}
@article{shahid2012a,
  author          = {Nauman Shahid and Ijaz Haider Naqvi and Saad Bin Qaisar},
  title           = {Quarter-Sphere SVM: Attribute andSpatio-Temporal Correlations based Outlier \& Event Detection in Wireless Sensor Networks},
  journal         = {IEEE Wireless Communications and Networking Conference: Mobile and Wireless Networks},
  year            = {2012},
  doi             = {10.1109/WCNC.2012.6214127}
}
@article{shahid2012b,
  author          = {Nauman Shahid and Ijaz Haider Naqvi and Saad Bin Qaisar},
  title           = {Real Time Energy Efficient Approach to Outlier \& Event Detection in Wireless Sensor Networks},
  journal         = {IEEE International Conference on Communication Systems (ICCS)},
  year            = {2012},
  doi             = {10.1109/ICCS.2012.6406130}
}
@article{ali2015,
  author          = {K. Ali and T. Anwaro and I. H. Naqvi and M. H. Jafry},
  title           = {Composite Event Detection and Identification for WSNs using General Hebbian Algorithm},
  journal         = {IEEE ICC 2015 - Ad-hoc and Sensor Networking Symposium},
  year            = {2015}
}
@article{mcdonald2013,
  author          = {Dylan McDonald and Stewart Sanchez and Sanjay Madria and Fikret Ercal},
  title           = {A Survey of Methods for Finding Outliers in Wireless Sensor Networks},
  journal         = {Journal of Network and Systems Management},
  volume          = {23},
  year            = {2015},
  pages           = {153-182}
}
@article{sheng2007,
  author          = {Bo Sheng and Qun Li and Weizhen Mao and Wen Jin},
  title           = {Outlier Detection in Sensor Networks},
  journal         = {MobiHoc '07: Proceedings of the 8th ACM international symposium on Mobile ad hoc networking and computing},
  year            = {2007},
  doi             = {10.1145/1288107.1288137}
}
@article{böhm2008,
  author          = {Christian Böhm and Christos Faloutsos and Claudia Plant},
  title           = {Outlier-robust Clustering using Independent Components},
  journal         = {SIGMOD '08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data},
  year            = {2008},
  doi             = {10.1145/1376616.1376638}
}
@article{papadimitriou2003,
  author          = {Spiros Papadimitriou and Hiroyuki Kitagawa and Phillip B. Gibbons and Christos Faloutsos},
  title           = {LOCI: Fast Outlier DetectionUsing the Local Correlation Integral},
  journal         = {Proceedings 19th International Conference on Data Engineering},
  year            = {March 2003},
  doi             = {10.1109/ICDE.2003.1260802}
}
@article{breuning2000,
  author          = {Markus M. Breunig and Hans-Peter Kriegel and Raymond T. Ng and Jörg Sander},
  title           = {LOF: Identifying Density-Based Local Outliers},
  journal         = {Proc. ACM SIGMOD 2000 Int. Conf. On Management of Data},
  year            = {2000}
}
@article{oreilly2014,
  author          = {Colin O'Reilly and Alexander Gluhak and Muhammad Ali Imran and Sutharshan Rajasegarar},
  title           = {Anomaly Detection in Wireless Sensor Networks in a Non-Stationary Environment},
  journal         = {IEEE Communications Surveys \& Tutorials},
  volume          = {16},
  number          = {3},
  year            = {2014},
  pages           = {1413 - 1432},
  doi             = {10.1109/SURV.2013.112813.00168}
}
@article{oreilly2016,
  author          = {Colin O’Reilly and Alexander Gluhak and Muhammad Ali Imran},
  title           = {Distributed Anomaly Detection using Minimum Volume Elliptical Principal Component Analysis},
  journal         = {IEEE Transactions on Knowledge and Data Engineering},
  volume          = {28},
  number          = {9},
  year            = {2016},
  doi             = {10.1109/TKDE.2016.2555804}
}
@article{shilton2020,
  author          = {Alistair Shilton and Sutharshan Rajasegarar and Marimuthu Palaniswami},
  title           = {Multiclass Anomaly Detector: the CS++ Support VectorMachine},
  journal         = {Journal of Machine Learning Research},
  volume          = {21},
  number          = {213},
  year            = {2020},
  pages           = {1-39}
}
@inproceedings{rajasegarar2007,
  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},
  pages={467--476},
  year={2011},
  organization={IEEE}
}
@article{ramotsoela2018,
  title={A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study},
  author={Ramotsoela, Daniel and Abu-Mahfouz, Adnan and Hancke, Gerhard},
  journal={Sensors},
  volume={18},
  number={8},
  pages={2491},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}
@inproceedings{bosman2013,
  title={Online extreme learning on fixed-point sensor networks},
  author={Bosman, Hedde HWJ and Liotta, Antonio and Iacca, Giovanni and W{\"o}rtche, Heinrich J},
  booktitle={2013 IEEE 13th International Conference on Data Mining Workshops},
  pages={319--326},
  year={2013},
  organization={IEEE}
}
@inproceedings{macua2010,
  title={Consensus-based distributed principal component analysis in wireless sensor networks},
  author={Macua, Sergio Valcarcel and Belanovic, Pavle and Zazo, Santiago},
  booktitle={2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)},
  pages={1--5},
  year={2010},
  organization={IEEE}
}
@article{chan2012,
  title={Robust recursive eigendecomposition and subspace-based algorithms with application to fault detection in wireless sensor networks},
  author={Chan, Shing-Chow and Wu, HC and Tsui, Kai Man},
  journal={IEEE Transactions on Instrumentation and Measurement},
  volume={61},
  number={6},
  pages={1703--1718},
  year={2012},
  publisher={IEEE}
}
@article{li2000,
  title={Recursive PCA for adaptive process monitoring},
  author={Li, Weihua and Yue, H Henry and Valle-Cervantes, Sergio and Qin, S Joe},
  journal={Journal of process control},
  volume={10},
  number={5},
  pages={471--486},
  year={2000},
  publisher={Elsevier}
}
@inproceedings{tien2004,
  title={Comparative study of PCA approaches in process monitoring and fault detection},
  author={Tien, Doan X and Lim, K-W and Jun, Liu},
  booktitle={30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004},
  volume={3},
  pages={2594--2599},
  year={2004},
  organization={IEEE}
}
@book{jolliffee2002,
  author         = {I.T. Jolliffe},
  title          = {Principal Component Analysis},
  publisher      = {Springer},
  year           = {2002}
}
@article{chalapathy2019,
  title={Deep learning for anomaly detection: A survey},
  author={Chalapathy, Raghavendra and Chawla, Sanjay},
  journal={arXiv preprint arXiv:1901.03407},
  year={2019}
}
@article{kiran2018,
  title={An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos},
  author={Kiran, B Ravi and Thomas, Dilip Mathew and Parakkal, Ranjith},
  journal={Journal of Imaging},
  volume={4},
  number={2},
  pages={36},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}
@article{zhang2018,
  title={LSTM-based analysis of industrial IoT equipment},
  author={Zhang, Weishan and Guo, Wuwu and Liu, Xin and Liu, Yan and Zhou, Jiehan and Li, Bo and Lu, Qinghua and Yang, Su},
  journal={IEEE Access},
  volume={6},
  pages={23551--23560},
  year={2018},
  publisher={IEEE}
}
@InProceedings{Sirisanwannakul2021,
author="Sirisanwannakul, Kraithep
and Siripool, Nutchanon
and Kongprawechnon, Waree
and Dangsakul, Prachumpong
and Lewlomphaisarl, Udom
and Sartsatit, Seksun
and Duangtanoo, Thanika
and Keerativittayanun, Suthum
and Suhaili, Wida Susanty Haji
and Owada, Yasunori
and Mya, Khin Than
and Ariffin, Sharifah H. S.
and Karnjana, Jessada",
editor="Suhaili, Wida Susanty Haji
and Siau, Nor Zainah
and Omar, Saiful
and Phon-Amuaisuk, Somnuk",
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{\%}.",
isbn="978-3-030-68133-3"
}
@inproceedings{kakanakova2017,
  title={Outlier detection via deep learning architecture},
  author={Kakanakova, Irina and Stoyanov, Stefan},
  booktitle={Proceedings of the 18th International Conference on Computer Systems and Technologies},
  pages={73--79},
  year={2017}
}

@inproceedings{li2015drift,
  title={Drift detection and calibration of sensor networks},
  author={Li, Zhan and Wang, Yuzhi and Yang, Anqi and Yang, Huazhong},
  booktitle={2015 International Conference on Wireless Communications \& Signal Processing (WCSP)},
  pages={1--6},
  year={2015},
  organization={IEEE}
}


@article{mohanty2020,
  title={Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks},
  author={Mohanty, Sachi Nandan and Lydia, E Laxmi and Elhoseny, Mohamed and Al Otaibi, Majid M Gethami and Shankar, K},
  journal={Physical Communication},
  volume={40},
  pages={101097},
  year={2020},
  publisher={Elsevier}
}


% drift
@article{ni2009,
  title={Sensor network data fault types},
  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},
  volume={1},
  number={2},
  pages={1--21},
  year={2017},
  publisher={ACM New York, NY, USA}
}
@inproceedings{cassens2017automated,
  title={Automated Encounter Detection for Animal-Borne Sensor Nodes.},
  author={Cassens, Bj{\"o}rn and Ripperger, Simon and Hierold, Martin and Mayer, Frieder and Kapitza, R{\"u}diger},
  booktitle={EWSN},
  pages={120--131},
  year={2017}
}
@inproceedings{hefeeda2007wireless,
  title={Wireless sensor networks for early detection of forest fires},
  author={Hefeeda, Mohamed and Bagheri, Majid},
  booktitle={2007 IEEE International Conference on Mobile Adhoc and Sensor Systems},
  pages={1--6},
  year={2007},
  organization={IEEE}
}
@article{werner2006deploying,
  title={Deploying a wireless sensor network on an active volcano},
  author={Werner-Allen, Geoffrey and Lorincz, Konrad and Ruiz, Mario and Marcillo, Omar and Johnson, Jeff and Lees, Jonathan and Welsh, Matt},
  journal={IEEE internet computing},
  volume={10},
  number={2},
  pages={18--25},
  year={2006},
  publisher={IEEE}
}
@article{xie2011anomaly,
  title={Anomaly detection in wireless sensor networks: A survey},
  author={Xie, Miao and Han, Song and Tian, Biming and Parvin, Sazia},
  journal={Journal of Network and computer Applications},
  volume={34},
  number={4},
  pages={1302--1325},
  year={2011},
  publisher={Elsevier}
}
@article{zhang2006detecting,
  title={Detecting outlying subspaces for high-dimensional data: the new task, algorithms, and performance},
  author={Zhang, Ji and Wang, Hai},
  journal={Knowledge and information systems},
  volume={10},
  number={3},
  pages={333--355},
  year={2006},
  publisher={Springer}
}

@article{huang2011extreme,
  title={Extreme learning machine for regression and multiclass classification},
  author={Huang, Guang-Bin and Zhou, Hongming and Ding, Xiaojian and Zhang, Rui},
  journal={IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)},
  volume={42},
  number={2},
  pages={513--529},
  year={2011},
  publisher={IEEE}
}
@article{huang2015extreme,
  title={What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle},
  author={Huang, Guang-Bin},
  journal={Cognitive Computation},
  volume={7},
  number={3},
  pages={263--278},
  year={2015},
  publisher={Springer}
}
@article{balcan2014improved,
  title={Improved distributed principal component analysis},
  author={Balcan, Maria-Florina and Kanchanapally, Vandana and Liang, Yingyu and Woodruff, David},
  journal={arXiv preprint arXiv:1408.5823},
  year={2014}
}