Rate-Adaptive Compressive Sensing for IoT Applications

Charalampidis, Pavlos; Fragkiadakis, Alexandros; Τράγος, Ηλίας

doi:10.1109/vtcspring.2015.7146042

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…A common paradigm is to adjust the compression factor at the transmission stage based on an estimate of signal sparsity, and considering a bound on the signal reconstruction error. For example, Charalampidis et al [10] introduced a framework for compressing an already acquired signal prior to transmission. The approach was based on a change point detection to adapt the compression ratio according to the time-varying sparsity level.…”

Section: Related Workmentioning

confidence: 99%

Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

Schiboni

Vicario

Suarez

et al. 2022

IEEE Trans. on Mobile Comput.

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This paper investigates a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices. Our approach can be applied to compressive sensing frameworks to minimise sampling and transmission costs. We consider a context estimate to represent the local signal structure and a feed-forward response model to continuously tune signal acquisition of an online sampling and transmission system. To evaluate our approach, we analysed the performance in different pattern recognition scenarios. We report three case studies here: (1) eating monitoring based on electromyography measurements in smart eyeglasses, (2) human activity recognition based on waist-worn inertial sensor data, and (3) heartbeat detection and arrhythmia classification based on single-lead electrocardiogram readings. Compared to conventional sub-Nyquist sampling, our context-adaptive approach saves between 13% to 22% of energy, while achieving similar pattern recognition performance and reconstruction error.

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Section: Related Workmentioning

confidence: 99%

Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

Schiboni

Vicario

Suarez

et al. 2022

IEEE Trans. on Mobile Comput.

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“…Other specific applications of the CS principle are also listed: see [26][27][28] for its application to radar with CRs of 10.4%, 25%, and 75%, respectively; see [29][30][31] for its application to electroencephalogram measurement with CRs of 10%, 15%, and 75%, respectively; see [32][33][34] for the application of video compression with CRs of 1%, 20%, and 59.8%, respectively, and [18,35] for other applications with CRs of 50% and 60%, respectively. Despite the unreasonableness of directly comparing the values of CR for different principles and application methods, these reports also reflect, to some extent, the successful application of our methods.…”

Section: Influence Of Rd-aic Hardware Structure Parameters On Svm Pre...mentioning

confidence: 99%

Research into a novel surface acoustic wave sensor signal-processing system based on compressive sensing and an observed-signal augmentation method based on secondary information prediction

Han¹,

Bu²,

Song³

et al. 2021

Meas. Sci. Technol.

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Due to their unique advantages, surface acoustic wave (SAW) sensors have attracted various disciplines to carry out thorough research. However, in their wireless applications, their high carrier frequency, low signal-to-noise ratio, and short effective signal length have led to poor signal measurement accuracy and complex detection systems. Using an SAW torque sensor as its objective, this paper describes the proposal and completion of a novel processing system based on compressive sensing (CS). The system was implemented using an analog-to-information conversion module. The module hardware’s structural parameters and signal recovery parameters were designed and optimized by simulation. Furthermore, an intervening interpolation method based on support vector machine (SVM) signal secondary information prediction was developed to achieve an effective extension of the observed signal. The method’s parameters and the SVM super parameters were optimized by a cuckoo search algorithm. The success rate of the predicted signal secondary information reached more than 94%. The compression ratio of the system reached 5.77‰ while ensuring successful signal frequency recovery. Finally, a torque static calibration experiment was carried out using the system prototype; the results show that the system sensitivity is 1.951 kHz Nm−1, which proves that the scheme and methods are effective. The SAW signal-processing system based on CS combines the advantages of traditional demodulation system solutions with low hardware costs, low data volume, and high interference immunity. This system will prompt some new thoughts about SAW sensor signal detection.

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“…In addition, the CN creates a profile that assigns to each range of sparsity the best CF that renders the minimum reconstruction error. Furthermore, Charalampidis et al [77] proposed a new approach to detect the changes in the signal sparsity using change point methods (CPM) [78] to update the CF value each time the signal sparsity level changes.…”

Section: Adaptive Measurementsmentioning

confidence: 99%

“…Sensing Layer Adaptive measurements [76,77] Optimize CF value • Sparsity change detection based on QoS [76] • Sparsity change detection using CPM [77] CS-based sparse Energy efficiency…”

Section: Iot Layer Approach Features Main Attributementioning

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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Rate-Adaptive Compressive Sensing for IoT Applications

Cited by 13 publications

References 16 publications

Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

Research into a novel surface acoustic wave sensor signal-processing system based on compressive sensing and an observed-signal augmentation method based on secondary information prediction

Compressive Sensing-Based IoT Applications: A Review

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