Blockchain-Based Distributed Information Hiding Framework for Data Privacy Preserving in Medical Supply Chain Systems.

Medical supply chain communication networks engender critical information and data. Notably in the COVID era, inner personal and private information is being shared between healthcare providers regarding the medical supply chain. In recent years, multiple cyber-attacks have targeted medical supply chain communication networks due to their lack of security measures. In the era where cyber-attacks are cheaper and easier due to the computational power and various algorithms available for malicious uses, security, and data privacy requires intensive and higher measures. On the other hand, Information Hiding Techniques (IHT) compromise various advanced methods to hide sensitive information from being disclosed to malicious nodes. Moreover, with the support of Blockchain, IHT can bring higher security and the required privacy levels. In this paper, we propose the implementation of Blockchain and smart contract with the information hiding technique to enhance the security and privacy of data communication in critical systems, such as smart healthcare supply chain communication networks. Results show the feasibility of the framework using Hyperledger smart contract along with the desired security level.

CoVAC: A P2P smart contract-based intelligent smart city architecture for vaccine manufacturing.

With the Corona Virus Disease 2019 (COVID-19) outbreak, vaccination is an urgent need worldwide. Internet of Things (IoT) has a vital role in the smart city for vaccine manufacturing with wearable sensors. According to the advanced services in intelligent manufacturing, the fourth resolution is also changing in Industry 5.0 and utilizes high-definition connectivity sensors. Traditional manufacturing companies rely on trusted third parties, which may act as a single point of failure. Access control, big data, and scalability are also challenging issues in existing systems because of the demand response data (DRD) in advanced manufacturing. To mitigate these challenges, CoVAC: A P2P Smart Contract-based Intelligent Smart City Architecture for Vaccine Manufacturing is proposed with three layers, including connection, conversion, and intelligent cloud layer. Smart contract-based blockchain is utilized at the conversion layer for resolving access control, security, and privacy issues. Deep learning is adopted in the intelligent cloud layer for big data analysis and increasing production for vaccine manufacturing in smart city environments. A case study is carried out wherein access data are collected from the various smart plants for vaccines using smart manufacturing to validate the effectiveness of the proposed architecture. Simulation of the proposed architecture is performed on the collected advanced sensor IoT plants data to address the challenges above, offering scalable production in the vaccine manufacturing for the smart city.

SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Healthy City.

Nowadays, the world is experiencing a pandemic crisis due to the spread of COVID-19, a novel coronavirus disease. The contamination rate and death cases are expeditiously increasing. Simultaneously, people are no longer relying on traditional news channels to enlighten themselves about the epidemic situation. Alternately, smart cities citizens are relying more on Social Network Service (SNS) to follow the latest news and information regarding the outbreak, share their opinions, and express their feelings and symptoms. In this paper, we propose an SNS Big Data Analysis Framework for COVID-19 Outbreak Prediction in Smart Sustainable Healthy City, where Twitter platform is adopted. Over 10000 Tweets were collected during two months, 38% of users aged between 18 and 29, while 26% are between 30 and 49 years old. 56% of them are males and 44% are females. The geospatial location is USA, and the used language is English. Natural Language Processing (NLP) is deployed to filter the tweets. Results demonstrated an outbreak cluster predicted seven days earlier than the confirmed cases with an indicator of 0.989. Analyzing data from SNS platforms enabled predicting future outbreaks several days earlier, and scientifically reduce the infection rate in a smart sustainable healthy city environment.