An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications

Rassam, Murad A.; Zainal, Anazida; Maarof, Mohd Aizaini

doi:10.1016/j.asoc.2012.11.041

Cited by 29 publications

(25 citation statements)

References 14 publications

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“…These devices are commonly used in sensor nodes (widely used in wireless sensor network [27]) usually under the environmental changing conditions, because they are an economic, small, and flexible solutions to interpret signals from the various sensors and take a decision according to the inputs received [19], [28]- [32]. An advantage of microcontrollers is that they can be easily programmed using standard programming languages, such as C, C++, and Java, while their main limitations are memory size and computing speed.…”

Section: Introductionmentioning

confidence: 99%

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers

Ortega-Zamorano

Jerez

Munoz

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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The well-known backpropagation learning algorithm is implemented in a field-programmable gate array (FPGA) board and a microcontroller, focusing in obtaining efficient implementations in terms of a resource usage and computational speed. The algorithm was implemented in both cases using a training/validation/testing scheme in order to avoid overfitting problems. For the case of the FPGA implementation, a new neuron representation that reduces drastically the resource usage was introduced by combining the input and first hidden layer units in a single module. Further, a time-division multiplexing scheme was implemented for carrying out product computations taking advantage of the built-in digital signal processor cores. In both implementations, the floating-point data type representation normally used in a personal computer (PC) has been changed to a more efficient one based on a fixed-point scheme, reducing system memory variable usage and leading to an increase in computation speed. The results show that the modifications proposed produced a clear increase in computation speed in comparison with the standard PC-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in neurocomputational tasks and the suitability of both implementations of the algorithm for its application to the real world problems.

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Section: Introductionmentioning

confidence: 99%

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers

Ortega-Zamorano

Jerez

Munoz

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…Moreover, the use of wavelets helped in reducing the dimension of data and hence reduces the communication cost in the network. The dimension reduction concept by transforming the data to another space was also conducted using an adaptive PCA approach [65]. Meanwhile, the long segmented anomalies were addressed by the use of DTW distance-based method given that environmental data are spatially correlated.…”

Section: Detection Method-based Classification Of Anomaly Detection Mmentioning

confidence: 99%

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

Rassam

Zainal

Maarof

2013

Sensors

Self Cite

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Wireless Sensor Networks (WSNs) are important and necessary platforms for the future as the concept “Internet of Things” has emerged lately. They are used for monitoring, tracking, or controlling of many applications in industry, health care, habitat, and military. However, the quality of data collected by sensor nodes is affected by anomalies that occur due to various reasons, such as node failures, reading errors, unusual events, and malicious attacks. Therefore, anomaly detection is a necessary process to ensure the quality of sensor data before it is utilized for making decisions. In this review, we present the challenges of anomaly detection in WSNs and state the requirements to design efficient and effective anomaly detection models. We then review the latest advancements of data anomaly detection research in WSNs and classify current detection approaches in five main classes based on the detection methods used to design these approaches. Varieties of the state-of-the-art models for each class are covered and their limitations are highlighted to provide ideas for potential future works. Furthermore, the reviewed approaches are compared and evaluated based on how well they meet the stated requirements. Finally, the general limitations of current approaches are mentioned and further research opportunities are suggested and discussed.

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“…It transforming them into a set of uncorrelated variables called Principal Components (PCs) [23,28]. In [24], authors introduced how detecting outliers and identifying faulty nodes using PCA.…”

Section: Related Workmentioning

confidence: 99%

A Novel Outlier Detection Model Based on One Class Principal Component Classifier in Wireless Sensor Networks

Ghorbel

Abid

Snoussi

2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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Wireless sensor networks (WSNs) are important platforms for collecting environmental data and monitoring phenomena. So, outlier detection process is a necessary step in building sensor network systems to assure data quality for perfect decision making. Over the last few years Kernel Principal Component Analysis (KPCA) is considered as a natural nonlinear generalization of PCA, which extracts nonlinear structure from the data. Wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. Then, the key challenge is to extract high level knowledge from such raw data. So, the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. However, KPCA based reconstruction error (RE) has found several applications in outlier detection but is not perfect to detect outlier. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new outlier detection method using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from three real datasets are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the original RE based variant and the One-Class SVM detection approach.

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An adaptive and efficient dimension reduction model for multivariate wireless sensor networks applications

Cited by 29 publications

References 14 publications

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers

Advancements of Data Anomaly Detection Research in Wireless Sensor Networks: A Survey and Open Issues

A Novel Outlier Detection Model Based on One Class Principal Component Classifier in Wireless Sensor Networks

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