Image subset communication for resource-constrained applications in wireless sensor networks

Nazir, Sajid; Alzubi, Omar A.; Kaleem, Mohammad; Hamdoun, Hassan

doi:10.3906/elk-2002-169

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…ML algorithms fall under the topic of artificial intelligence (AI), which is commonly employed for healthcare applications for feature extraction and image examination purposes. A classification model is developed for computing the dissimilarity amongst a collection of Regions of Interest (ROIs) [7,8]. Moreover, the features are classified by a normal vector-oriented classifier technique.…”

Section: Related Workmentioning

confidence: 99%

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

et al. 2022

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Recently, the COVID-19 epidemic has had a major impact on day-to-day life of people all over the globe, and it demands various kinds of screening tests to detect the coronavirus. Conversely, the development of deep learning (DL) models combined with radiological images is useful for accurate detection and classification. DL models are full of hyperparameters, and identifying the optimal parameter configuration in such a high dimensional space is not a trivial challenge. Since the procedure of setting the hyperparameters requires expertise and extensive trial and error, metaheuristic algorithms can be employed. With this motivation, this paper presents an automated glowworm swarm optimization (GSO) with an inception-based deep convolutional neural network (IDCNN) for COVID-19 diagnosis and classification, called the GSO-IDCNN model. The presented model involves a Gaussian smoothening filter (GSF) to eradicate the noise that exists from the radiological images. Additionally, the IDCNN-based feature extractor is utilized, which makes use of the Inception v4 model. To further enhance the performance of the IDCNN technique, the hyperparameters are optimally tuned using the GSO algorithm. Lastly, an adaptive neuro-fuzzy classifier (ANFC) is used for classifying the existence of COVID-19. The design of the GSO algorithm with the ANFC model for COVID-19 diagnosis shows the novelty of the work. For experimental validation, a series of simulations were performed on benchmark radiological imaging databases to highlight the superior outcome of the GSO-IDCNN technique. The experimental values pointed out that the GSO-IDCNN methodology has demonstrated a proficient outcome by offering a maximal sensy of 0.9422, specy of 0.9466, precn of 0.9494, accy of 0.9429, and F1score of 0.9394.

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Section: Related Workmentioning

confidence: 99%

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

et al. 2022

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“…An energy-efficient data transmission mechanism is proposed to improve emergency data transmission by increasing accuracy and decreasing packet delay ( Sheikh et al, 2012 ; Singanamalla et al, 2019 ; Nazir et al, 2020 ). It was claimed that the scheme was reliable, but a mechanism for ensuring the security of data transmission was not presented.…”

Section: Related Workmentioning

confidence: 99%

Quantum readout and gradient deep learning model for secure and sustainable data access in IWSN

Alzubi

2022

PeerJ Computer Science

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The industrial wireless sensor network (IWSN) is a surface-type of wireless sensor network (WSN) that suffers from high levels of security breaches and energy consumption. In modern complex industrial plants, it is essential to maintain the security, energy efficiency, and green sustainability of the network. In an IWSN, sensors are connected to the Internet in a non-monitored environment. Hence, non-authorized sensors can retrieve information from the IWSN. Therefore, to ensure that data access between sensors remains sustainable and secure, energy-efficient authentication and authorization are required. In this article, a novel Quantum Readout Gradient Secured Deep Learning (QR-GSDL) model is proposed to ensure that only trustworthy sensors can access IWSN data. The major objective of this QR-GSDL model is to create secure, energy-efficient IWSN to attain green sustainability and reduce the industrial impact on the environment. First, using the quantum readout and hash function, a registration method is designed to efficiently perform the registration process. Next, a gradient secured deep learning method is adopted to implement the authentication and authorization process in order to ensure energy-saving and secure data access. Simulations are conducted to evaluate the QR-GSDL model and compare its performance with that of three well-known models: online threshold anomaly detection, machine learning-based anomaly detection, and dynamic CNN. The simulation outcomes show that the proposed model is secure and energy-efficient for use in the IWSN. Moreover, the experimental results prove that the QR-SGDL model outperforms the existing models in terms of energy consumption, authentication rate, authentication time, and false acceptance rate.

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“…JPEG standard [12] compresses the raw pixel data in 8X8 blocks, and the compressed representation contains, the DC coefficient, followed by AC coefficient. DC coefficient for each 8x8 block is a single number but can be used to recreate a coarse representation of the image whereas the AC coefficients are required for generating finer image details [13].…”

Section: A Image Data and Headermentioning

confidence: 99%

Embedded Hash Codes for Image Similarity Detection and Tamper Proofing

Nazir

Kaleem

2022

2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)

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The image and video traffic on the Internet has increased due to the ease and abundance of the image capture and sharing services. However, this can sometimes result in violations of copyright and image tampering for various purposes. Finding differences between images has importance for image retrieval, aiding forensics, disease diagnosis, and environmental changes etc. Many similarity measures have been proposed that use different features extractable from the detected image. In some cases it is important to determine if the set of images differ and if so then to what extent. Digital Rights Management (DRM) has gained prominence and importance due to widespread availability of tools to modify images. Sometimes the need arises to claim the rightful ownership of an image. The blockchain through the use of hash codes can be used to determine tampering similar to its success in many industries where privacy and security of the documents is important.In this paper, we embed the image hash code in the JPEG image header. The proposed system simplifies the hashing and encryption process by processing only the image data without which a reconstruction is not possible. The results show that the image data can thus be shared without the risk of any undetected tampering or misuse.

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Image subset communication for resource-constrained applications in wireless sensor networks

Cited by 7 publications

References 30 publications

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

An Automated Glowworm Swarm Optimization with an Inception-Based Deep Convolutional Neural Network for COVID-19 Diagnosis and Classification

Quantum readout and gradient deep learning model for secure and sustainable data access in IWSN

Embedded Hash Codes for Image Similarity Detection and Tamper Proofing

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