Abstract
Wireless Sensor Networks (WSNs) are composed of distributed small sensors that are deployed in various environmental conditions to collect data such as temperature, moisture, light speed, etc. These sensors are subjected to various damages, noises and low battery life as a result of which sensors account for abnormal data readings. These anomalies must be detected for the reliable and efficient working of a WSN. This study presents a comparative study for fault detection using Twin Support Vector Machines (TSVM) by utilizing different kernel functions. TSVM uses two non-parallel hyperplanes to classify data and is insensitive to class size imbalance. The choice of kernels significantly influences the performance of TSVM. The proposed work is estimated using different standard WSN datasets by inserting various types of fault. The performance of the work is measured using standard performance indexes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gavel S, Raghuvanshi AS, Tiwari S (2020) Comparative study of anomaly detection in wireless sensor networks using different kernel functions. In: Advances in VLSI, communication, and signal processing, pp 81–89. Springer, Singapore
Biswas P, Charitha R, Gavel S, Raghuvanshi AS (2019) Fault detection using hybrid of KF-ELM for wireless sensor networks. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI), pp 746–750. IEEE, April 2019
Liu Q, Cui X, Abbod MF, Huang SJ, Han YY, Shieh JS (2011) Brain death prediction based on ensembled artificial neural networks in neurosurgical intensive care unit. J Taiwan Inst Chem Eng 42(1):97–107
Yang J, Singh H, Hines EL et al (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif Intell Med 55(2):117–126
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999
Vapnik V (2000) The nature of statistical learning theory. Springer, Heidelberg
Mangasarian OL, Musicant DR (2001) Lagrangian support vector machines. J Mach Learn Res 1:161–177
Lee Y-J, Mangasarian OL (2001) SSVM: a smooth support vector machine for classification. Comput Optim Appl 20(1):5–22
Suykens JA, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300
Mangasarian OL, Wild EW (2001) Proximal support vector machine classifiers. In: Proceedings KDD 2001: knowledge discovery and data mining. Citeseer
Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74
Jayadeva, Khemchandani R, Chandra S (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910
Khemchandani R (2008) Mathematical programming applications in machine learning. PhD thesis, Indian Institute of Technology Delhi New Delhi-110016, India
Ding M, Yang D, Li X (2020) Fault diagnosis for wireless sensor by twin support vector machine. Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:1–43
Ding S, Zhang N, Zhang X, Wu F (2017) Twin support vector machine: theory, algorithm and applications. Neural Comput Appl 28(11):3119–3130
Khemchandani R, Goyal K, Chandra S (2016) TWSVR: regression via twin support vector machine. Neural Netw 74:14–21
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, J., Raghuvanshi, A.S., Shukla, N., Gavel, S. (2021). A Comparative Study on Fault Detection Using Twin Support Vector Machines for Wireless Sensor Networks. In: Nath, V., Mandal, J.K. (eds) Proceeding of Fifth International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 748. Springer, Singapore. https://doi.org/10.1007/978-981-16-0275-7_22
Download citation
DOI: https://doi.org/10.1007/978-981-16-0275-7_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0274-0
Online ISBN: 978-981-16-0275-7
eBook Packages: EngineeringEngineering (R0)