5,539 research outputs found
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Anomaly Detection using Autoencoders in High Performance Computing Systems
Anomaly detection in supercomputers is a very difficult problem due to the
big scale of the systems and the high number of components. The current state
of the art for automated anomaly detection employs Machine Learning methods or
statistical regression models in a supervised fashion, meaning that the
detection tool is trained to distinguish among a fixed set of behaviour classes
(healthy and unhealthy states).
We propose a novel approach for anomaly detection in High Performance
Computing systems based on a Machine (Deep) Learning technique, namely a type
of neural network called autoencoder. The key idea is to train a set of
autoencoders to learn the normal (healthy) behaviour of the supercomputer nodes
and, after training, use them to identify abnormal conditions. This is
different from previous approaches which where based on learning the abnormal
condition, for which there are much smaller datasets (since it is very hard to
identify them to begin with).
We test our approach on a real supercomputer equipped with a fine-grained,
scalable monitoring infrastructure that can provide large amount of data to
characterize the system behaviour. The results are extremely promising: after
the training phase to learn the normal system behaviour, our method is capable
of detecting anomalies that have never been seen before with a very good
accuracy (values ranging between 88% and 96%).Comment: 9 pages, 3 figure
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Outlier detection techniques for wireless sensor networks: A survey
In the field of wireless sensor networks, those measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are not directly applicable to wireless sensor networks due to the nature of sensor data and specific requirements and limitations of the wireless sensor networks. This survey provides a comprehensive overview of existing outlier detection techniques specifically developed for the wireless sensor networks. Additionally, it presents a technique-based taxonomy and a comparative table to be used as a guideline to select a technique suitable for the application at hand based on characteristics such as data type, outlier type, outlier identity, and outlier degree
Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning
This paper presents a spatiotemporal unsupervised feature learning method for
cause identification of electromagnetic transient events (EMTE) in power grids.
The proposed method is formulated based on the availability of
time-synchronized high-frequency measurement, and using the convolutional
neural network (CNN) as the spatiotemporal feature representation along with
softmax function. Despite the existing threshold-based, or energy-based events
analysis methods, such as support vector machine (SVM), autoencoder, and
tapered multi-layer perception (t-MLP) neural network, the proposed feature
learning is carried out with respect to both time and space. The effectiveness
of the proposed feature learning and the subsequent cause identification is
validated through the EMTP simulation of different events such as line
energization, capacitor bank energization, lightning, fault, and high-impedance
fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the
WSCC 9-bus system.Comment: 9 pages, 7 figure
Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques
Peer reviewedPostprin
A survey of outlier detection methodologies
Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review
- …