Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Ravikumar, G.; Venkatachalam, K.; AlZain, Mohammed A.; Masud, Mehedi; Abouhawwash, Mohamed

doi:10.32604/csse.2023.024605

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…Also, image retrieval [36,37] can play a vital role in generating the new natural images used as Stego images for coverless steganography. Different data types, such as text audio, and videos [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55], may be used for generating many robust coverless steganography systems.…”

Section: Discussionmentioning

confidence: 99%

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Seddik¹,

Salah²,

Behery³

et al. 2023

Computers, Materials &Amp; Continua

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The ability of any steganography system to correctly retrieve the secret message is the primary criterion for measuring its efficiency. Recently, researchers have tried to generate a new natural image driven from only the secret message bits rather than using a cover to embed the secret message within it; this is called the stego image. This paper proposes a new secured coverless steganography system using a generative mathematical model based on semi Quick Response (QR) code and maze game image generation. This system consists of two components. The first component contains two processes, encryption process, and hiding process. The encryption process encrypts secret message bits in the form of a semi-QR code image whereas the hiding process conceals the pregenerated semi-QR code in the generated maze game image. On the other hand, the second component contains two processes, extraction and decryption, which are responsible for extracting the semi-QR code from the maze game image and then retrieving the original secret message from the extracted semi-QR code image, respectively. The results were obtained using the bit error rate (BER) metric. These results confirmed that the system achieved high hiding capacity, good performance, and a high level of robustness against attackers compared with other coverless steganography methods.

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Section: Discussionmentioning

confidence: 99%

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Seddik¹,

Salah²,

Behery³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Fig. 2 shows the sequence diagram of three smart contracts with the supply chain [50][51][52][53][54]. Medical equipment details and their requirements for treatment and medical waste details are stored in the distributed storage system on the edge computing ledger.…”

Section: Smart Contractmentioning

confidence: 99%

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

Palanikkumar¹,

Kumar²,

Masud³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The major environmental hazard in this pandemic is the unhygienic disposal of medical waste. Medical wastage is not properly managed it will become a hazard to the environment and humans. Managing medical wastage is a major issue in the city, municipalities in the aspects of the environment, and logistics. An efficient supply chain with edge computing technology is used in managing medical waste. The supply chain operations include processing of waste collection, transportation, and disposal of waste. Many research works have been applied to improve the management of wastage. The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security, trustworthiness, and transparency. To overcome these issues, in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm (NBQ-BBOA). This proposed work is used to track, detect, and manage medical waste. To minimize the transferring cost of medical wastage from various nodes, the Q-Learning algorithm is used. The accuracy obtained for the Naïve Bayes algorithm is 88%, the Q-Learning algorithm is 82% and NBQ-BBOA is 98%. The error rate of Root Mean Square Error (RMSE) and Mean Error (MAE) for the proposed work NBQ-BBOA are 0.012 and 0.045.

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“…At the start, every node has the same amount of energy. The BS would have no energy constraints and has very high computing energy [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43].…”

Section: System Modelmentioning

confidence: 99%

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Alkanhel¹,

Chinnathambi²,

Thilagavathi³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in largescale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network's lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network's longevity and optimizes the routing. As a result of the study's findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.

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Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Cited by 13 publications

References 32 publications

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

A New Generative Mathematical Model for Coverless Steganography System Based on Image Generation

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

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