Standard of things, first step: Understanding and normalizing sensor signals

Monte, Gustavo; Huang, Victor; Liscovsky, Pablo; Marasco, Damian; Agnello, Ariel

doi:10.1109/iecon.2013.6699121

Cited by 14 publications

(7 citation statements)

References 7 publications

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“…The overall obtained results reveal that CS improves the energy saving by a factor of 79.4% compared to 62.43% and 34% obtained by TC and AS, respectively. In addition, Abate et al [73] conducted a comparative study in terms of reconstruction quality and computation time between CS and the segmentation and labelling technique [74] which has been considered for the definition of the new standard of the IEEE 1451 [75]. The obtained results show that, even though CS is more complex, it outperforms remarkably the segmentation and labelling technique in terms of data reconstruction and robustness to noise.…”

Section: Sensing Layermentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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The Internet of Things (IoT) holds great promises to provide an edge cutting technology that enables numerous innovative services related to healthcare, manufacturing, smart cities and various human daily activities. In a typical IoT scenario, a large number of self-powered smart devices collect real-world data and communicate with each other and with the cloud through a wireless link in order to exchange information and to provide specific services. However, the high energy consumption associated with the wireless transmission limits the performance of these IoT self-powered devices in terms of computation abilities and battery lifetime. Thus, to optimize data transmission, different approaches have to be explored such as cooperative transmission, multi-hop network architectures and sophisticated compression techniques. For the latter, compressive sensing (CS) is a very attractive paradigm to be incorporated in the design of IoT platforms. CS is a novel signal acquisition and compression theory that exploits the sparsity behavior of most natural signals and IoT architectures to achieve power-efficient, real-time platforms that can grant efficient IoT applications. This paper assesses the extant literature that has aimed to incorporate CS in IoT applications. Moreover, the paper highlights emerging trends and identifies several avenues for future CS-based IoT research.

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Section: Sensing Layermentioning

confidence: 99%

Compressive Sensing-Based IoT Applications: A Review

Djelouat

Amira

Bensaali

2018

JSAN

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“…They are divided into two layers. An overview of these algorithms are in [8][9][10]. It is necessary to synchronize these vectors with those from other transducers.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

Real time transducer signal features extraction: A standard approach

Monte

Huang²,

Abate

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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This paper presents a real time algorithm, based on a segmentation and labelling technique, for analog transducer signals, which extracts information from the signal. The process of sampling is extremely important because it is the only one that connects the real world with the digital world. In addition, the proposed technique can be embedded into sensors, allowing applications in sensor networks and sensors data fusion architectures like Internet of Things. A simplified version of these algorithms has been proposed as a standard for transducer signal feature extraction. These algorithms are analysed and experimental results are tackled for representative signals, in different field

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“…Among the algorithms with low computational load, there is one that provides a segmentation and labeling of the raw data samples [15]. This latter technique has been considered for the definition of the new standard of the IEEE 1451 [16], [17]. This paper reports the comparison of these two algorithms.…”

Section: Introductionmentioning

confidence: 97%

A Comparison Between Sensor Signal Preprocessing Techniques

Abate

Huang

Monte³

et al. 2015

IEEE Sensors J.

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The need for the use of sensors networks in ever more efficient manner drives research methods for better information management. It would be useful to decrease the amount of managed data. Often we are interested in few noteworthy information of a signal (for example period, amplitude, time constant, steady state value, etc…) not in the whole waveform. The idea is to take less data, but acquire the same information. In a highly oversampled signal, each single sample does not carry a lot of information. From this point, two different algorithms are compared, in which only few samples are stored or transferred. This paper describes these two algorithms: the first one is the segmentation and labeling algorithm, also proposed for the definition of the new standard of the IEEE 1451, and the second one is based on compressive sensing theory. These two algorithms are compared, the simulations results are shown, and it is discussed which case could be more suitable for

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Standard of things, first step: Understanding and normalizing sensor signals

Cited by 14 publications

References 7 publications

Compressive Sensing-Based IoT Applications: A Review

Compressive Sensing-Based IoT Applications: A Review

Real time transducer signal features extraction: A standard approach

A Comparison Between Sensor Signal Preprocessing Techniques

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