{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T09:01:55Z","timestamp":1777021315513,"version":"3.51.4"},"reference-count":52,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor devices in wireless sensor networks are vulnerable to faults during their operation in unmonitored and hazardous environments. Though various methods have been proposed by researchers to detect sensor faults, only very few research studies have reported on capturing the dynamics of the inherent states in sensor data during fault occurrence. The continuous density hidden Markov model (CDHMM) is proposed in this research to determine the dynamics of the state transitions due to fault occurrence, while neural networks are utilized to classify the faults based on the state transition probability density generated by the CDHMM. Therefore, this paper focuses on the fault detection and classification using the hybridization of CDHMM and various neural networks (NNs), namely the learning vector quantization, probabilistic neural network, adaptive probabilistic neural network, and radial basis function. The hybrid models of each NN are used for the classification of sensor faults, namely bias, drift, random, and spike. The proposed methods are evaluated using four performance metrics which includes detection accuracy, false positive rate, F1-score, and the Matthews correlation coefficient. The simulation results show that the learning vector quantization NN classifier outperforms the detection accuracy rate when compared to the other classifiers. In addition, an ensemble NN framework based on the hybrid CDHMM classifier is built with majority voting scheme for decision making and classification. The results of the hybrid CDHMM ensemble classifiers clearly indicates the efficacy of the proposed scheme in capturing the dynamics of change of statesm which is the vital aspect in determining rapidly-evolving instant faults that occur in wireless sensor networks.<\/jats:p>","DOI":"10.3390\/s20030745","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T10:51:07Z","timestamp":1580295067000},"page":"745","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Hybrid Continuous Density Hmm-Based Ensemble Neural Networks for Sensor Fault Detection and Classification in Wireless Sensor Network"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9794-9786","authenticated-orcid":false,"given":"Malathy","family":"Emperuman","sequence":"first","affiliation":[{"name":"School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1146-4447","authenticated-orcid":false,"given":"Srimathi","family":"Chandrasekaran","sequence":"additional","affiliation":[{"name":"School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu 632014, India"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.comnet.2008.04.002","article-title":"Wireless sensor network survey","volume":"52","author":"Yick","year":"2008","journal-title":"Comput. Netw."},{"key":"ref_2","first-page":"495","article-title":"Energy-efficient and reliable data delivery in wireless sensor networks","volume":"19","author":"Anisi","year":"2013","journal-title":"Comput. Netw."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"489","DOI":"10.24846\/v25i4y201610","article-title":"Context-aware control platform for sensor network integration in IoT and cloud","volume":"25","author":"Merezeanu","year":"2016","journal-title":"Stud. Inform. Control"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10383","DOI":"10.1109\/ACCESS.2019.2890854","article-title":"Software Defined Mission-Critical Wireless Sensor Network: Architecture and Edge Offloading Strategy","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2680538","article-title":"Modeling and analysis of fault detection and fault tolerance in wireless sensor networks","volume":"14","author":"Munir","year":"2015","journal-title":"ACA Trans. Embed. Comput. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"555","DOI":"10.3846\/16484142.2017.1342101","article-title":"Condition monitoring of railway track systems by using acceleration signals on wheelset axle-boxes","volume":"33","author":"Chudzikiewicz","year":"2018","journal-title":"Transport"},{"key":"ref_7","unstructured":"Chudzikiewicz, A., Bogacz, R., and Kostrzewski, M. (2014, January 8\u201311). Using acceleration signals recorded on a railway vehicle wheelsets for rail track condition monitoring. Proceedings of the 7th European Workshop on Structural Health Monitoring, EWSHM 2014\u20142nd European Conference of the Prognostics and Health Management (PHM), Society Nantes, France."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1525856.1525863","article-title":"Sensor network data fault types","volume":"5","author":"Ni","year":"2009","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_9","unstructured":"Nagaraju, S., Gudino, L.J., Tripathi, N., Sreejith, V., and Ramesha, C.K. (2018). Mobility assisted localization for mission critical Wireless Sensor Network applications using hybrid area exploration approach. J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s10922-017-9403-6","article-title":"A Taxonomy of Faults for Wireless Sensor Networks","volume":"25","author":"Raposo","year":"2017","journal-title":"J. Netw. Syst. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mehmood, A., Alrajeh, N., Mukherjee, M., Abdullah, S., and Song, H. (2018). A survey on proactive, active and passive fault diagnosis protocols for WSNs: Network operation perspective. Sensors, 18.","DOI":"10.3390\/s18061787"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1504\/IJSNET.2017.084209","article-title":"A comparative analysis of machine learning algorithms for faults detection in wireless sensor networks","volume":"22","author":"Warriach","year":"2017","journal-title":"Int. J. Sens. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Noshad, Z., Javaid, N., Saba, T., Wadud, Z., Saleem, M.Q., Alzahrani, M.E., and Sheta, O.E. (2019). Fault detection in wireless sensor networks through the random forest classifier. Sensors, 7.","DOI":"10.3390\/s19071568"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S1389-1286(01)00302-4","article-title":"Wireless sensor networks: A survey","volume":"38","author":"Akyildiz","year":"2002","journal-title":"Comput. Netw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1109\/JBHI.2014.2312214","article-title":"Online Anomaly Detection in Wireless Body Area Networks for Reliable Healthcare Monitoring","volume":"18","author":"Salem","year":"2014","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_16","first-page":"1511","article-title":"Fault detection for time-delayed networked control systems with sensor saturation and randomly occurring faults","volume":"14","author":"Gao","year":"2018","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.jsv.2017.11.007","article-title":"Condition monitoring and fault diagnosis of motor bearings using undersampled vibration signals from a wireless sensor network","volume":"414","author":"GLu","year":"2018","journal-title":"J. Sound Vib."},{"key":"ref_18","first-page":"267","article-title":"An analysis of fault detection strategies in wireless sensor networks","volume":"78","author":"Muhammed","year":"2017","journal-title":"Wirel. Networks"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1016\/j.eswa.2013.11.034","article-title":"Probabilistic fault detector forWireless Sensor Network","volume":"41","author":"Lau","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_20","first-page":"14","article-title":"SGF: A state-free gradient-based forwarding protocol for wireless sensor networks","volume":"5","author":"Huang","year":"2009","journal-title":"ACM T. Sens. Netw."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2599","DOI":"10.1109\/TCYB.2014.2377123","article-title":"A distributed support vector machine learning over wireless sensor networks","volume":"11","author":"Kim","year":"2015","journal-title":"IEEE Trans. Cybernetics"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zidi, S., Moulahi, T., and Alaya, B. (2018). Fault detection in wireless sensor networks through SVM classifier. IEEE Sens. J., 340\u2013347.","DOI":"10.1109\/JSEN.2017.2771226"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8764","DOI":"10.3390\/s150408764","article-title":"Sensor anomaly detection in wireless sensor networks for healthcare","volume":"17","author":"Haque","year":"2015","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Javaid, A., Javaid, N., Wadud, Z., Saba, T., Sheta, O.E., Saleem, M.Q., and Alzahrani, M.E. (2019). Machine learning algorithms and fault detection for improved belief function based decision fusion in wireless sensor networks. Sensors, 19.","DOI":"10.3390\/s19061334"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"165732","DOI":"10.1155\/2013\/165732","article-title":"and Lo Re, G. and Milazzo, F. and Ortolani, M. QoS-aware fault detection in wireless sensor networks","volume":"9","year":"2013","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_26","unstructured":"Swain, R.R., Khilar, P.M., and Dash, T. (2018). Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network. Digit. Commun. Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4019","DOI":"10.1007\/s00521-018-3342-3","article-title":"Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation","volume":"31","author":"Zhao","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.compeleceng.2015.06.024","article-title":"Distributed Byzantine fault detection technique in wireless sensor networks based on hypothesis testing","volume":"48","author":"Panda","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s11276-014-0820-0","article-title":"Kuiper test and autoregressive model-based approach for wireless sensor network fault diagnosis","volume":"21","author":"Jin","year":"2015","journal-title":"Wirel. Netw."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1109\/TPDS.2014.2308173","article-title":"Directional Diagnosis for Wireless Sensor Networks","volume":"26","author":"Gong","year":"2015","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1002\/wcm.2661","article-title":"A faulty node detection scheme for wireless sensor networks that use data aggregation for transport","volume":"95","author":"Artail","year":"2016","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1109\/TII.2016.2537758","article-title":"Wireless Sensor-Networks Conditions Monitoring and Fault Diagnosis Using Neighborhood Hidden Conditional Random Field","volume":"12","author":"Tang","year":"2016","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3754","DOI":"10.1016\/j.proeng.2011.08.703","article-title":"The fuzzy nonlinear enhancement algorithm of infrared image based on curvelet transform","volume":"15","author":"Zhao","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2530526","article-title":"Failure detection in wireless sensor networks: A sequence-based dynamic approach","volume":"10","author":"Kamal","year":"2014","journal-title":"ACM Trans. Sens. Netw."},{"key":"ref_35","first-page":"1931","article-title":"Online distributed fault diagnosis in wireless sensor networks","volume":"71","author":"Mahapatro","year":"2013","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1007\/s11276-016-1207-1","article-title":"rDFD: Reactive distributed fault detection in wireless sensor networks","volume":"23","author":"Sharma","year":"2017","journal-title":"Wirel. Netw."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1109\/TPDS.2013.2295810","article-title":"A time efficient approach for detecting errors in big sensor data on cloud","volume":"26","author":"Yang","year":"2015","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_38","first-page":"281","article-title":"Distributed fault detection and recovery algorithms in two-tier wireless sensor networks","volume":"16","author":"Nitesh","year":"2016","journal-title":"Int. J. Commun. Netw. Distrib. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1109\/TIM.2007.913803","article-title":"Wireless sensor network modeling using modified recurrent neural networks: Application to fault detection","volume":"57","author":"Moustapha","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"241","DOI":"10.3390\/s100100241","article-title":"A multi-fault diagnosis method for sensor systems based on principle component analysis","volume":"10","author":"Zhu","year":"2010","journal-title":"Sensors"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.neucom.2012.04.002","article-title":"Application of fuzzy inference systems to detection of faults in wireless sensor networks","volume":"94","author":"Khan","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jss.2016.05.041","article-title":"Mobile sink based fault diagnosis scheme for wireless sensor network","volume":"119","author":"Chanak","year":"2016","journal-title":"J. Syst. Softw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2826","DOI":"10.1016\/j.comcom.2007.05.024","article-title":"A survey on clustering algorithms for wireless sensor networks","volume":"30","author":"Abbasi","year":"2007","journal-title":"Comput. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1177\/0142331217691334","article-title":"Hidden Gaussian Markov model for distributed fault detection in wireless sensor networks","volume":"40","author":"Saihi","year":"2018","journal-title":"Trans. Inst. Meas. Control"},{"key":"ref_45","unstructured":"Fausett, L. (2004). Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Pearson Education. [1st ed.]."},{"key":"ref_46","unstructured":"Timothy, M. (1993). Practical Neural Network Recipies in C++, Morgan Kaufmann."},{"key":"ref_47","unstructured":"Bose, N.K., and Liang, P. (1995). Neural Network Fundamentals with Graphs, Algorithms and Applications, McGraw-Hill Inc."},{"key":"ref_48","unstructured":"(2019, June 13). Intel Lab Data. Available online: http:\/\/db.csail.mit.edu\/labdata\/labdata.html."},{"key":"ref_49","unstructured":"De Bruijn, B., Nguyen, T.A., Bucur, D., and Tei, K. (November, January 30). Benchmark datasets for fault detection and classification in sensor data. Proceedings of the 5th International Conference on Sensor Networks, Orlando, FL, USA."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"96319","DOI":"10.1109\/ACCESS.2019.2929581","article-title":"An isolation-based distributed outlier detection framework using nearest neighbor ensembles for wireless sensor networks","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.comnet.2019.06.014","article-title":"DODS: A Distributed Outlier Detection Scheme for Wireless Sensor Networks","volume":"161","author":"Titouna","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Paolanti, M., Romeo, L., Liciotti, D., Pietrin, R., Cenci, A., Frontoni, E., and Zingaretti, P. (2018). Person re-identification with RGB-D camera in top-view configuration through multiple nearest neighbor classifiers and neighborhood component features selection. Sensors, 18.","DOI":"10.3390\/s18103471"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/745\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:53:01Z","timestamp":1760172781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/745"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,29]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030745"],"URL":"https:\/\/doi.org\/10.3390\/s20030745","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,29]]}}}