Anomaly detection in sensor systems using lightweight machine learning

H.H.W.J. Bosman, A. Liotta, G. Iacca, H.J. Wörtche

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

29 Citations (Scopus)
3 Downloads (Pure)


The maturing field of Wireless Sensor Networks (WSN) results in long-lived deployments that produce large amounts of sensor data. Lightweight online on-mote processing may improve the usage of their limited resources, such as energy, by transmitting only unexpected sensor data (anomalies). We detect anomalies by analyzing sensor reading predictions from a linear model. We use Recursive Least Squares (RLS) to estimate the model parameters, because for large datasets the standard Linear Least Squares Estimation (LLSE) is not resource friendly. We evaluate the use of fixed-point RLS with adaptive thresholding, and its application to anomaly detection in embedded systems. We present an extensive experimental campaign on generated and real-world datasets, with floating-point RLS, LLSE, and a rule-based method as benchmarks. The methods are evaluated on prediction accuracy of the models, and on detection of anomalies, which are injected in the generated dataset. The experimental results show that the proposed algorithm is comparable, in terms of prediction accuracy and detection performance, to the other LS methods. However, fixed-point RLS is efficiently implementable in embedded devices. The presented method enables online on-mote anomaly detection with results comparable to offline LS methods.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Systems, Man and Cybernetics (SMC), 13-16 October 2013, Manchester
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4799-0652-9
Publication statusPublished - 2013
Event2013 IEEE Internationak Conference on Systems, Man, and Cybernetics (SMC 2013) - Manchester, United Kingdom
Duration: 13 Oct 201316 Oct 2013


Conference2013 IEEE Internationak Conference on Systems, Man, and Cybernetics (SMC 2013)
Abbreviated titleSMC 2013
Country/TerritoryUnited Kingdom


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