Internet of things applications using Raspberry-Pi: a survey

Hosny, Khalid M.; Magdi, Amal; Salah, Ahmad; El-Komy, Osama; Lashin, Nabil A.

doi:10.11591/ijece.v13i1.pp902-910

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…There is only one limitation in our method: we observed that shadows have resulted in a building structure as part of the equipment. This needs further refinement to improve the accuracy of the segmentation and in the world of advanced technology, the Internet of Things is often used to resolve those issues [30], [31]. In Table 12, we present a comparative analysis of various image segmentation methods, including the proposal approach (referred to as this work) and several methods from recent articles.…”

Section: Discussionmentioning

confidence: 99%

An improved mining image segmentation with K-Means and morphology using drone dataset

Haqiq,

Zaim,

Sbihi

et al. 2024

IJECE

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The mining industry faces the challenge of incorporating advanced technology to explore new ways of increasing productivity and reducing costs. Our focus is on integrating drone technology to revolutionize mining tasks like inspection, mapping, and surveying. Drones offer a precision advantage over traditional satellite methods. To this end, we have created a dataset consisting of 373 aerial images captured by a DJI Phantom 4 drone, which depict a mining site in the Benslimane region of Western Morocco. These images, with a ground resolution of 2.5 cm per pixel, are the basis of our research. Our study aims to address the challenges posed by traditional mining techniques and to leverage technological innovations to improve segmentation and classification. The proposed approach includes new methodologies, particularly the combination of K-Means clustering and mathematical morphology, to overcome limitations and deliver better segmentation results. Our findings represent a significant step forward in advancing mining operations through the effective use of modern technologies.

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Section: Discussionmentioning

confidence: 99%

An improved mining image segmentation with K-Means and morphology using drone dataset

Haqiq,

Zaim,

Sbihi

et al. 2024

IJECE

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“…In general, the Internet of Things in combination with Raspberry Pi is used in various fields such as transportation, medicine, agriculture, but the construction and maintenance of the current state of communication networks and roads are not very many solutions proposed based on Raspberry Pi [1]. Using the Internet of Things to process data on the state of corrosion and concrete to send to cloud storage [2,3], but the main detection method is external sensors.…”

Section: Overviewmentioning

confidence: 99%

Iot-Based Solution for Detection Defects in Infrastructure Objects Using Raspberry Pi

Mysiuk¹,

Yuzevych²

2023

ELIT

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“…It was developed by the Raspberry Pi Foundation. The Raspberry Pi is a quad-core computer with parallel computing skills, which can be utilized to accelerate computations and processes [32]. Raspberry Pi can be useful in image processing areas because of its portability, parallelism, cheap cost, and minimal power usage [33]…”

Section: Advanced Encryption Standard Algorithmmentioning

confidence: 99%

“…Many smart city applications utilize embeddable systems like the Raspberry Pi since PCs are not portable. • Due to its portability and ability to be managed through the Internet, the Raspberry Pi platform has been used for research in domains like image processing [35]. • Real-time applications employ the Raspberry Pi to minimize system complexity.…”

Section: Implementation Of Zero Watermarking On Raspberry Pimentioning

confidence: 99%

Securing Transmitted Color Images Using Zero Watermarking and Advanced Encryption Standard on Raspberry Pi

Khafaga¹,

Alhammad²,

Magdi³

et al. 2023

Computer Systems Science and Engineering

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Image authentication techniques have recently received a lot of attention for protecting images against unauthorized access. Due to the wide use of the Internet nowadays, the need to ensure data integrity and authentication increases. Many techniques, such as watermarking and encryption, are used for securing images transmitted via the Internet. The majority of watermarking systems are PC-based, but they are not very portable. Hardwarebased watermarking methods need to be developed to accommodate real-time applications and provide portability. This paper presents hybrid data security techniques using a zero watermarking method to provide copyright protection for the transmitted color images using multi-channel orthogonal Legendre Fourier moments of fractional orders (MFrLFMs) and the advanced encryption standard (AES) algorithm on a low-cost Raspberry Pi. In order to increase embedding robustness, the watermark picture is scrambled using the Arnold method. Zero watermarking is implemented on the Raspberry Pi to produce a real-time ownership verification key. Before sending the ownership verification key and the original image to the monitoring station, we can encrypt the transmitted data with AES for additional security and hide any viewable information. The receiver next verifies the received image's integrity to confirm its authenticity and that it has not been tampered with. We assessed the suggested algorithm's resistance to many attacks. The suggested algorithm provides a reasonable degree of robustness while still being perceptible. The proposed method provides improved bit error rate (BER) and normalized correlation (NC) values compared to previous zero watermarking approaches. AES performance analysis is performed to demonstrate its effectiveness. Using a 256 × 256 image size, it takes only 2 s to apply the zero-watermark algorithm on the Raspberry Pi.

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Internet of things applications using Raspberry-Pi: a survey

Cited by 11 publications

References 32 publications

An improved mining image segmentation with K-Means and morphology using drone dataset

An improved mining image segmentation with K-Means and morphology using drone dataset

Iot-Based Solution for Detection Defects in Infrastructure Objects Using Raspberry Pi

Securing Transmitted Color Images Using Zero Watermarking and Advanced Encryption Standard on Raspberry Pi

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