Bayesian Estimation of Recurrent Changepoints for Signal Segmentation and Anomaly Detection

Reich, Christian; Nicolaou, Christina; Mansour, Ahmad; Laerhoven, Kristof Van

doi:10.23919/eusipco.2019.8902774

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…In this work, well-known batch and online learning algorithms (both supervised and semisupervised) were employed to cope with the anomaly detection problem. Differently from our paper, works in [11,12] do not exploit deep learning and do not take account of the distribution of the detection algorithm over several Fog nodes.…”

Section: Related Workmentioning

confidence: 91%

“…Overall, unlike our paper, works in [11][12][13][14] and references therein do not take account of the accuracy-vs.-energy performance over a distributed and virtualized networked Fog platform, deeply investigated in this paper.…”

Section: Related Workmentioning

confidence: 97%

“…Anomaly detection algorithms exploiting DNNs-A very challenging issue when dealing with SM data is represented by the early detection of anomalies. In this regard, the literature offers several contributions, like [11][12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…Specifically, the work in [11] is focused on a Bayesian approach for the segmentation of data sequences and the identification of anomalies based on an empirical estimate of the recurrent behavior of observed changepoints. Paper [12] provides a comparative analysis of some traditional "shallow" machine learning algorithms (like k-NN, MLP and SVM), which were used to classify measurements as regular or anomaly.…”

Section: Related Workmentioning

confidence: 99%

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SmartFog: Training the Fog for the Energy-Saving Analytics of Smart-Meter Data

et al. 2019

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In this paper, we characterize the main building blocks and numerically verify the classification accuracy and energy performance of SmartFog, a distributed and virtualized networked Fog technological platform for the support for Stacked Denoising Auto-Encoder (SDAE)-based anomaly detection in data flows generated by Smart-Meters (SMs). In SmartFog, the various layers of an SDAE are pretrained at different Fog nodes, in order to distribute the overall computational efforts and, then, save energy. For this purpose, a new Adaptive Elitist Genetic Algorithm (AEGA) is “ad hoc” designed to find the optimized allocation of the SDAE layers to the Fog nodes. Interestingly, the proposed AEGA implements a (novel) mechanism that adaptively tunes the exploration and exploitation capabilities of the AEGA, in order to quickly escape the attraction basins of local minima of the underlying energy objective function and, then, speed up the convergence towards global minima. As a matter of fact, the main distinguishing feature of the resulting SmartFog paradigm is that it accomplishes the joint integration on a distributed Fog computing platform of the anomaly detection functionality and the minimization of the resulting energy consumption. The reported numerical tests support the effectiveness of the designed technological platform and point out that the attained performance improvements over some state-of-the-art competing solutions are around 5%, 68% and 30% in terms of detection accuracy, execution time and energy consumption, respectively.

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Section: Related Workmentioning

confidence: 91%