A method for co-existing heterogeneous IoT environments based on compressive sensing

Lee, Hyungkeuk; Jo, Seng-Kyoun; Lee, NamKyung; Lee, Hyun–Woo

doi:10.1109/icact.2016.7423330

Cited by 5 publications

(5 citation statements)

References 9 publications

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“…We might also manage expenses, minimize product value loss, and maximizing the total value of items. Other advantages ate to contribute to decision-making related to distribution and storage planning, finding possible quality failures, and identifying risks for real-time action to mitigate such risks [3,6,24].…”

Section: Resultsmentioning

confidence: 99%

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Prediction of Cold Chain Transport Conditions Using Data Mining

Mangini

Lima

Nääs

2020

IFIP Advances in Information and Communication Technology

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Ensuring the delivery of temperature-controlled products in transportation is an increasing challenge, especially in countries with continental extension and tropical climate such as Brazil. Products with this type of specificity generally have a higher added value and involve specialized equipment and labor. Thus, route mapping is necessary for the logistics of the cold chain. The study aimed to predict the transport conditions in the cold chain. The data set analyzed includes the temperature of the loads and the route information (Southeast to Northeast and South of Brazil). The classification of temperature excursions considered data below 15ºC or above 30ºC. The Naïve Bayes and Multilayer Perceptron algorithms are used to predict the optimal temperature excursion model. The Multilayer Perceptron algorithm proved to be the most suitable for a thermal route mapping model. With this identified standard, logistics decision making can be improved to reducer o waste and ensure product integrity with less recourse.

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Section: Resultsmentioning

confidence: 99%

“…The cold chain is part of the supply chain, where temperature monitoring plays a vital role in the system. At the same time, automatic data collection is an essential aspect of cold chain management [3][4][5][6][7]2].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cold Chain Transport Conditions Using Data Mining

Mangini

Lima

Nääs

2020

IFIP Advances in Information and Communication Technology

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“…This idea was first introduced in the context of CS for image reconstruction using wavelet decomposition where different CFs are assigned to different wavelet sub-bands [79]. Subsequently, borrowing the same concept to IoT applications, Lee et al [80] introduced weighted CoSaMP (wCoSaMP) reconstruction algorithm to enable a new framework for data acquisition in heterogeneous IoT platforms where different type of sensors have different impact on the overall system, for instance, for IoT-based connected health, the ECG data are more crucial than humidity and temperature data. Applying this concept, fewer measurements are needed to achieve the same performance as conventional CS.…”

Section: Weighted Measurementsmentioning

confidence: 99%

“…Weighted measurements [79,80] Optimize • (MECDA) approach: select the best route that consumes the minimum power [83].…”

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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“…when we have limited bandwidth, limited storage capacity, measurement are more costly or sluggish to capture such as in radiology and medical imaging modules like MRI, CT, PET, etc. In this type of circumstances, CS offers a favorable solution [6]- [8]. In this CS, there are many algorithms there to reconstruct the original signal, and these are mainly subdivided into the convex optimizations as well as greedy pursuit's schemes [9].…”

Section: Introductionmentioning

confidence: 99%

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

Lakshminarayana¹,

Sarvagya²

2018

IJECE

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The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.

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A method for co-existing heterogeneous IoT environments based on compressive sensing

Cited by 5 publications

References 9 publications

Prediction of Cold Chain Transport Conditions Using Data Mining

Prediction of Cold Chain Transport Conditions Using Data Mining

Compressive Sensing-Based IoT Applications: A Review

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

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