Sensor signal preprocessing techniques for analysis and prediction

We live in an interconnected world. The IoT, Internet of Things, is a reality. There are more things connected to internet than people on the planet. The communication channels has been established and standardized, things can be easily identified. We know how to interchange data but we do not know exactly what to interchange. There is a need to reduce the traffic over the networks when the number of nodes increases. Things could be sensors or devices with sensors. Things generate signals and since that the world is inferred through these signals, there is a need to interchange knowledge at low bit rates in a standardized way. The technology explosion that invades our lives has an influence not only on what is plausible to be standardized but opens possibilities not even thought. In the sensor world, intelligence has been concentrated in the point of acquisition, inside the transducer. Considering CPU capabilities, cost, low consumption and reduction of size, the sensor signal will be processed entirely into the smart sensor, even for cheap and simple sensors. The network will be used to interchange more meaningful information or knowledge. This paper focuses on algorithms suitable for any sensor signal that start from oversampled data and end with knowledge about the sensor signal behavior in a normalized scheme. The proposed standard is an important step towards an interconnected and manageable world.

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“…The two cases from the top correspond to signals with a long-term value. This value can be predicted with precision using MCT vectors [8].…”

Section: A Exponential Detection or Super Segmentsmentioning

confidence: 99%

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

Monte

Huang²,

Liscovsky

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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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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“…As above described, the output of the Segmentation and Labeling algorithm returns three arrays, named also M for amplitude, C for classes and T for time [16], [ 17]. The algorithm calculates the periods of the input waveform acquired elaborating this set of three vectors.…”

Section: B Segmentation and Labeling Algorithmmentioning

confidence: 99%

A comparison between FFT and MCT for period measurement with an ARM microcontroller

Guia

Espírito-Santo

Paciellco

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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A comparison between two different algorithms to measure the period of a signal is performed on this work. One of the algorithms is based on the Fast Fourier Transformation (FFT) measurement procedure, whereas the other is based on the Segmentation and Labeling algorithm (MeT). Both algorithms have been prepared for implementation in a micro controller with ARM architecture. The measurement operation of each one of the algorithms is described in detail. Experimental results are reported, with the purpose of demonstrating both performance and output format for signal analysis.

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Sensor signal preprocessing techniques for analysis and prediction

Cited by 24 publications

References 3 publications

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

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

Compressive Sensing-Based IoT Applications: A Review

A comparison between FFT and MCT for period measurement with an ARM microcontroller

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