Cost Efficient Scheduling Using Smart Contract Cognizant Ethereum for IoMT

Ravikumar, G.; Venkatachalam, K.; Masud, Mehedi; Abouhawwash, Mohamed

doi:10.32604/iasc.2022.024278

Cited by 13 publications

(6 citation statements)

References 18 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

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“… Base on ‘task and cost scheduling method and smart contract enabled blockchain network for IoMT’. 38 …”

Section: Iomt With Ethereum Using Blockchain In Fog-cloudmentioning

confidence: 99%

Case studies about smart contracts in healthcare

Vargas,

Mira da Silva

2023

DIGITAL HEALTH

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The Internet of Things (IoT) such as devices and sensors are a fast growth reality which our bureaucratical and archaic institutional system is not yet ready to embrace its functionalities. In the health system, many developments are made, and smart devices are the key to preventing, studying, investigating, and solving a lot of diseases and improving our health system. But along with this, innovation is necessary for the hospitals, for example, to have a proper system that provides storage of health data information and respects the General Data Protection Regulation (GDPR) with the use of smart contracts that secure the integrity and disclosure of the patient's data, since the majority of hospitals still use paper, physical records to store data. In this study, we will briefly analyse and explain three different suggested methods to deal with the challenges that Internet of Medical Things (IoMT) encounters. We will not choose which one is the best because of the different features and the countries they are proposed but will emphasize the benefits and challenges which one has.

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“…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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Cost Efficient Scheduling Using Smart Contract Cognizant Ethereum for IoMT

Cited by 13 publications

References 18 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

Case studies about smart contracts in healthcare

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

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