Signal change detection in sensor networks with artificial neural network structure

Reznik, Leon; Pless, G. Von; Karim, T. Al

doi:10.1109/cihsps.2005.1500609

Cited by 14 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patterns of missing data are analysed and a matrix based structure is used to represent them. Other models involve Multi-layer Perceptrons (MLPs) [26], Self-Organizing Maps (SOMs) [6], and Adaptive Resonance Theory (ART) [5]. The advantages of using neural networks for this problem are that they can capture many kinds of relationships and they allow quick and easy modeling of the environment [20].…”

Section: Prior Workmentioning

confidence: 99%

A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

Kolomvatsos

Papadopoulou

Anagnostopoulos

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Current applications developed for the Internet of Things (IoT) usually involve the processing of collected data for delivering analytics and support efficient decision making. The basis for any processing mechanism is data analysis, usually having as an outcome responses in various analytics queries defined by end users or applications. However, as already noted in the respective literature, data analysis cannot be efficient when missing values are present. The research community has already proposed various missing data imputation methods paying more attention of the statistical aspect of the problem. In this paper, we study the problem and propose a method that combines machine learning and a consensus scheme. We focus on the clustering of the IoT devices assuming they observe the same phenomenon and report the collected data to the edge infrastructure. Through a sliding window approach, we try to detect IoT nodes that report similar contextual values to edge nodes and base on them to deliver the replacement value for missing data. We provide the description of our model together with results retrieved by an extensive set of simulations on top of real data. Our aim is to reveal the potentials of the proposed scheme and place it in the respective literature.

show abstract

Section: Prior Workmentioning

confidence: 99%

A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

Kolomvatsos

Papadopoulou

Anagnostopoulos

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Examples of these algorithms are Multi-layer Perceptrons (MLPs) [12], Self-Organizing Maps (SOMs) [4], and Adaptive Resonance Theory (ART) [3]. The advantages of using neural networks for this problem are that they can capture many kinds of relationships, and they allow quick and easy modeling of the environment.…”

Section: Related Workmentioning

confidence: 99%

A spatial-temporal imputation technique for classification with missing data in a wireless sensor network

Parker

2008

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

We have developed a novel method to estimate missing observations in wireless sensor networks. We use a hierarchical unsupervised fuzzy ART neural network to represent the data cluster prototypes. We then estimate missing inputs by using a new spatial-temporal imputation technique. We have evaluated this approach through experiments on both real sensor data and artificially generated data. Our experimental results show that our proposed approach performs better than nine other estimation algorithms including moving average and Expectation-Maximization (EM) imputation.

show abstract

“…Examples of these algorithms are Multi-layer Perceptrons (MLPs) [11], SelfOrganizing Maps (SOMs) [4], and Adaptive Resonance Theory (ART) [8]. We have chosen the fuzzy ART model proposed by Kulakov and Davcev in [8] for our WSN implementation, because of its unique abilities to learn in a short period of time and to continually learn from new events [2], [3], [8], [12].…”

Section: Related Workmentioning

confidence: 99%

Classification with missing data in a wireless sensor network

Parker

2008

IEEE SoutheastCon 2008

View full text Add to dashboard Cite

Signal change detection in sensor networks with artificial neural network structure

Cited by 14 publications

References 9 publications

A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

A Spatio-Temporal Data Imputation Model for Supporting Analytics at the Edge

A spatial-temporal imputation technique for classification with missing data in a wireless sensor network

Classification with missing data in a wireless sensor network

Contact Info

Product

Resources

About