1,119 research outputs found
Rate-Distortion Classification for Self-Tuning IoT Networks
Many future wireless sensor networks and the Internet of Things are expected
to follow a software defined paradigm, where protocol parameters and behaviors
will be dynamically tuned as a function of the signal statistics. New protocols
will be then injected as a software as certain events occur. For instance, new
data compressors could be (re)programmed on-the-fly as the monitored signal
type or its statistical properties change. We consider a lossy compression
scenario, where the application tolerates some distortion of the gathered
signal in return for improved energy efficiency. To reap the full benefits of
this paradigm, we discuss an automatic sensor profiling approach where the
signal class, and in particular the corresponding rate-distortion curve, is
automatically assessed using machine learning tools (namely, support vector
machines and neural networks). We show that this curve can be reliably
estimated on-the-fly through the computation of a small number (from ten to
twenty) of statistical features on time windows of a few hundreds samples
Toward a Robust Sparse Data Representation for Wireless Sensor Networks
Compressive sensing has been successfully used for optimized operations in
wireless sensor networks. However, raw data collected by sensors may be neither
originally sparse nor easily transformed into a sparse data representation.
This paper addresses the problem of transforming source data collected by
sensor nodes into a sparse representation with a few nonzero elements. Our
contributions that address three major issues include: 1) an effective method
that extracts population sparsity of the data, 2) a sparsity ratio guarantee
scheme, and 3) a customized learning algorithm of the sparsifying dictionary.
We introduce an unsupervised neural network to extract an intrinsic sparse
coding of the data. The sparse codes are generated at the activation of the
hidden layer using a sparsity nomination constraint and a shrinking mechanism.
Our analysis using real data samples shows that the proposed method outperforms
conventional sparsity-inducing methods.Comment: 8 page
Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network
The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and
compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work
is to design a unified compression and classification framework for delivery of EEG
data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is
practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest
Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data.
Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed
framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems
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Optimized Node Selection for Compressive Sleeping Wireless Sensor Networks
In this paper, we propose an active node selection framework for compressive sleeping wireless sensor networks (WSNs) in order to improve signal acquisition performance, network lifetime and the use of spectrum resources. While conventional compressive sleeping WSNs only exploit the spatial correlation of SNs, the proposed approach further exploits the temporal correlation by selecting active nodes using the support of the data reconstructed in the previous time instant. The node selection problem is framed as the design of a specialized sensing matrix, where the sensing matrix consists of selected rows of an identity matrix. By capitalizing on a genie-aided reconstruction procedure, we formulate the active node selection problem into an optimization problem, which is then approximated by a constrained convex relaxation plus a rounding scheme. Simulation results show that our proposed active node selection approach leads to an improved reconstruction performance, network lifetime and spectrum usage in comparison to various node selection schemes for compressive sleeping WSNs.This is the accepted manuscript. The final published version is available from IEEE at http://dx.doi.org/10.1109/TVT.2015.2400635
Multimodal sensor fusion in the latent representation space
A new method for multimodal sensor fusion is introduced. The technique relies
on a two-stage process. In the first stage, a multimodal generative model is
constructed from unlabelled training data. In the second stage, the generative
model serves as a reconstruction prior and the search manifold for the sensor
fusion tasks. The method also handles cases where observations are accessed
only via subsampling i.e. compressed sensing. We demonstrate the effectiveness
and excellent performance on a range of multimodal fusion experiments such as
multisensory classification, denoising, and recovery from subsampled
observations.Comment: Under review for Nature Scientific Report
From Real to Complex: Enhancing Radio-based Activity Recognition Using Complex-Valued CSI
Activity recognition is an important component of many pervasive computing
applications. Radio-based activity recognition has the advantage that it does
not have the privacy concern and the subjects do not have to carry a device on
them. Recently, it has been shown channel state information (CSI) can be used
for activity recognition in a device-free setting. With the proliferation of
wireless devices, it is important to understand how radio frequency
interference (RFI) can impact on pervasive computing applications. In this
paper, we investigate the impact of RFI on device-free CSI-based
location-oriented activity recognition. We present data to show that RFI can
have a significant impact on the CSI vectors. In the absence of RFI, different
activities give rise to different CSI vectors that can be differentiated
visually. However, in the presence of RFI, the CSI vectors become much noisier
and activity recognition also becomes harder. Our extensive experiments show
that the performance of state-of-the-art classification methods may degrade
significantly with RFI. We then propose a number of counter measures to
mitigate the impact of RFI and improve the location-oriented activity
recognition performance. We are also the first to use complex-valued CSI to
improve the performance in the environment with RFI
Anomaly detection in unknown environments using wireless sensor networks
This dissertation addresses the problem of distributed anomaly detection in Wireless Sensor Networks (WSN). A challenge of designing such systems is that the sensor nodes are battery powered, often have different capabilities and generally operate in dynamic environments. Programming such sensor nodes at a large scale can be a tedious job if the system is not carefully designed. Data modeling in distributed systems is important for determining the normal operation mode of the system. Being able to model the expected sensor signatures for typical operations greatly simplifies the human designer’s job by enabling the system to autonomously characterize the expected sensor data streams. This, in turn, allows the system to perform autonomous anomaly detection to recognize when unexpected sensor signals are detected. This type of distributed sensor modeling can be used in a wide variety of sensor networks, such as detecting the presence of intruders, detecting sensor failures, and so forth. The advantage of this approach is that the human designer does not have to characterize the anomalous signatures in advance.
The contributions of this approach include: (1) providing a way for a WSN to autonomously model sensor data with no prior knowledge of the environment; (2) enabling a distributed system to detect anomalies in both sensor signals and temporal events online; (3) providing a way to automatically extract semantic labels from temporal sequences; (4) providing a way for WSNs to save communication power by transmitting compressed temporal sequences; (5) enabling the system to detect time-related anomalies without prior knowledge of abnormal events; and, (6) providing a novel missing data estimation method that utilizes temporal and spatial information to replace missing values. The algorithms have been designed, developed, evaluated, and validated experimentally in synthesized data, and in real-world sensor network applications
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