This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios.
Blockchain and Machine Learning (ML) are state-of-the-art technologies in the digital era. Developing countries have witnessed great digital transformation, and one of the key challenges during this development phase has been to create a platform that integrates the health and vitals of citizens confidentially. The healthcare insurance industry is one sector that has experienced a significant number of instances of fraudulent claims and mismanagement of patient data. These issues often arise due to inadequate technological integration and an over-reliance on manual processes and human intervention. The insurance industry relies on multiple processes between end users to initiate, maintain, and close diverse policies. The proposed model initially recommends a suitable insurance policy for newly admitted patients, but in the case of existing patients, the objectives are to speed up transaction processing and payment settlement securely using a private blockchain. Collectively, these two technologies possess the potential to revolutionize the future. This study aims to incorporate blockchain and ML techniques like Support Vector Machine (SVM) and Random Forest Regression, which can differentiate between fraudulent and legal medical records to recommend personalized policies, streamline claim processing, and ensure the security of sensitive patient information and vital insurance records. The key aim is to create a more patient-centric environment with data transparency. This integrated framework results in a secure, adaptable, and efficient ecosystem that outperforms traditional methods, paving the way for the future of healthcare and insurance services.
Blockchain technology has been an emerging solution to various problems in the healthcare sector. Its applications in the healthcare sector range from securing patient data to increasing transparency in the pharmaceutical supply chain. Here, consumer electronic devices are used to collect and process healthcare data before uploading them to a blockchain network. Many schemes have been already developed using blockchain technology, Mobile Edge Computing (MEC), and consumer electronic devices to exchange Electronic Medical Records (EMR) efficiently. However, they face many critical concerns like data security, automation, and scalability. A novel blockchain-based EMR sharing scheme is proposed in this work to solve these problems. It protects the system during the entire Health Information Exchange (HIE) process between the patient and doctor. Here, consumer electronic devices and MEC are used to generate and upload EMRs and diagnosis reports. The proposed scheme utilizes Advanced Encryption Standard (AES), Rivest Shamir and Adleman (RSA), Edwards-curve Digital Signature Algorithm (EdDSA), Elliptic Curve Digital Signature Algorithm (ECDSA) techniques, and Inter-Planetary File System (IPFS) to securely store EMRs, so that they cannot be tampered with and are always available to authorized users. Experimental results of the proposed scheme show its efficiency compared to other existing well-known schemes.
Jameel Almalki, Saeed M. Alshahrani, Nayyar Ahmed Khan
Recently, the use of the Internet of Medical Things (IoMT) has gained popularity across various sections of the health sector. The historical security risks of IoMT devices themselves and the data flowing from them are major concerns. Deploying many devices, sensors, services, and networks that connect the IoMT systems is gaining popularity. This study focuses on identifying the use of blockchain in innovative healthcare units empowered by federated learning. A collective use of blockchain with intrusion detection management (IDM) is beneficial to detect and prevent malicious activity across the storage nodes. Data accumulated at a centralized storage node is analyzed with the help of machine learning algorithms to diagnose disease and allow appropriate medication to be prescribed by a medical healthcare professional. The model proposed in this study focuses on the effective use of such models for healthcare monitoring. The amalgamation of federated learning and the proposed model makes it possible to reach 93.89 percent accuracy for disease analysis and addiction. Further, intrusion detection ensures a success rate of 97.13 percent in this study.
From the last few years, in product manufacturing industries, counterfeit products have played an important role that would affect the companv's fame. sales. and profit. To detect fake products and identify real products, a secure system can be developed using blockchain technology, Blockchain technology is a distributed decentralized, digital ledger that is stored across multiple databases, storing all transactions as blocks connected by a chain. Due to having a digital ledger, a block cannot be changed or be hacked. By employing blockchain technology, consumers or users may certify the safety of a product without depending on other users. In this study, Quick Response (QR) codes and bar codes have been used to provide robust techniques to detect counterfeit products. where QR code of the product is associated as a block to a blockchain. The proposed system stores product details and generate a unique QR code associated with that product as a block in the database to detect the counterfeit product. The unique QR codes have been created throughout the procedure and it is matched with the entries stored in the blockchain database. If the generated unique QR code is matched against entries in the blockchain database, it will give a notification to the user otherwise fake product notification will be given to the user. The proposed system outperforms the existing systems in terms of security as well as computational time.
Electronic Health Records (EHRs) have become an increasingly significant source of information for healthcare professionals and researchers. Two technical challenges are addressed: motivating federated learning members to contribute their time and effort, and ensuring accurate aggregation of the global model by the centralized federated learning server. To overcome these issues and establish a decentralized solution, the integration of blockchain and federated learning proves effective, offering enhanced security and privacy for smart healthcare. The proposed approach includes a gamified element to incentivize and recognize contributions from federated learning members. This research work offers a solution involving resource management within the Internet of Medical Things (IoMT) using a newly proposed trust decentralized loop federated learning consensus blockchain. The obtained raw data is pre-processed by using handling missing values and adaptive min-max normalization. The appropriate features are selected with the aid of hybrid weighted-leader exponential distribution optimization algorithm. Because, data with multiple features exhibits varying levels of variation across each feature. The selected features are then forwarded to the training phase through the proposed pyramid squeeze attention generative adversarial networks to classify the EHR as positive and negative. The proposed classification model demonstrates high flexibility and scalability, making it applicable to a wide range of network architectures for various computer vision tasks. The introduced model provides better outcomes in terms of 98.5% in the training accuracy and 99% in the validation accuracy over Medical Information Mart for Intensive Care III (MIMIC-III) dataset, which is more efficient than the other traditional methods.
Nabil Tazi Chibi, Omar Ait Oualhaj, Wassim Fassi Fihri, Hassan El Ghazi
Smart Grids (SGs) rely on advanced technologies, generating significant data traffic across the network, which plays a crucial role in various tasks such as electricity consumption billing, actuator activation, resource optimization, and network monitoring. This paper presents a new approach that integrates Machine Learning (ML), Blockchain Technology (BT), and Markov Decision Process (MDP) to improve the security of SG networks while ensuring accurate storage of events reported by various network devices through BT. The enhanced version of the Proof of Work (PoW) consensus mechanism ensures data integrity by preventing tampering and establishing the reliability of known and unknown attack detection. The proposed versions of PoW, namely GPoW 1.0 and GPoW 2.0, aim to make the consensus process more environmentally friendly.
In a smart city environment, various intelligent devices, applications, and digital networks collaborate to provide technological solutions for the public good. The exponential data generated from intelligent interactions among stakeholders within the smart city ecosystem raises concerns regarding security and privacy. Maintaining data openness while safeguarding it against social engineering attacks, network breaches, and data masking threats is paramount to achieving resilience. Blockchain technology has demonstrated great promise in addressing these challenges due to its decentralized, consistent, and tamper-proof nature. Our paper explores how blockchain can address the challenges of integrity, security, and privacy in smart city operations. Despite significant research efforts, the subject remains in need of a comprehensive survey. Consequently, we conduct a study on state-of-the-art blockchain-based reputation and trust management methods within the three fundamental components of a smart city: energy, healthcare, and transportation. The trust and reputation mechanisms are analyzed to identify their strengths and limitations. The investigation reveals that the existing trust schemes are resource-constrained and encounter scalability limitations, high energy consumption, and incompatibility with existing systems.
Supply chain as an industry has gone through four-fold changes in the last century. Born as a bare-bones structure in 1.0 it grew to incorporate some form of record preservation in 2.0 and then integrated communication between two entities in 3.0. Supply chain 4.0, the current one, has total global integration of multiple entities with the records digitised. But increasing entities and pipelines, means increasing complexities, overhead and soft spots. In this paper, a systematic literature review is done with the objective of analysing existing Supply Chain 4.0. The focus of the paper is the usage of blockchain technology in the electronic industry to provide a decentralised architecture. Several papers were compared on the basis of different schemas like the type of blockchain network used, platform deployed on, security of frameworks, representation of unique identity, testing authenticity, working implementation, cost of implementation, etc. The pros and cons of various privacy and security methodologies are also explored and discussed. The paper also discusses the open issues and challenges in the same area of interest. Finally, the paper outlines the future scope to be delved into as a part of the future research.
In terms of digital transformation, organizations today are aware of the critical role that data and information play in their expansion and development in light of the Internet of Things. To increase network performance and stability, many applications are moving from cloud computing to edge computing (EC). However, in order to satisfy customers, applications like intelligent transportation systems, smart grids, smart cities, and healthcare call for even more effective services. This survey addresses extensive research on two aspects: firstly, we present the advancements of two application domains namely maritime areas and aerial systems in terms of integration with EC architecture. Secondly, we cover the most recent technologies, artificial intelligence (AI) and blockchain, combined into the EC paradigm by discussing several experiments conducted in various fields to demonstrate the value of utilizing them in the edge computing architecture. We analyze the results of eleven experiments in each technology from 2015 to 2023.
As decentralization and the use of distributed ledger technology spread through more businesses and sectors, the world is going through a huge change. This change is being made because of a desire for more openness, safety, and efficiency in operations, as well as to give people and groups more autonomy.
This article addresses the dynamic landscape of smart supply chain management, characterized by the integration of cutting-edge technologies. It proposes an IoT-Blockchain system for monitoring equipment status in a smart supply chain environment. The system utilizes IoT sensors to collect temperature and humidity data from the equipment. This collected data is then processed and stored in the cloud using the InfluxDB database. To further enhance security and transparency in the monitoring process, the system incorporates blockchain technology. This ensures the tamper-proof nature of collected data through the deployment of smart contracts. The monitoring platform developed on Grafana provides users with an intuitive dashboard for accessing real-time information about the equipment’s status. The proposed system offers a comprehensive monitoring solution for sensitive supply chain operations, allowing stakeholders to track the equipment’s journey along the supply chain and monitor its status in real-time. This system has the potential to revolutionize supply chain monitoring, providing an efficient and secure way to optimize equipment performance and improve overall supply chain efficiency.
Ahmad Y. A. Bani Ahmad, Neha Verma, Nadia Sarhan, Emad Mahrous Awwad · 6 authors
The process of controlling the flow of products and services from a company by encompassing each stage involved in transforming raw materials and parts into finished items, also delivering them to the final consumer is known as Supply Chain Management (SCM). The development of numerous smart city applications including smart grids, smart homes, smart supply chains, and smart healthcare has drawn attention to the Internet of Things (IoT). Nowadays, researchers are considering the smart healthcare system’s role as a Public Emergency Service (PES) to treat patients promptly. A distributed smart fire brigade system receives little attention like PES to save lives and property from catastrophic fire damage. The conventional PES methods are created using a centralized method that needs a lot of processing power and doesn’t offer timely services. The traditional systems developed for managing the supply chain have drawbacks like single-point failure issues, data integrity, transparency, and lack of trust. To alleviate the existing issues, in this paper, a Blockchain and IoT Enable Secure and Transparent Supply Chain Management framework is utilized for PES in the smart city environment. Further, two edge computing servers, like a service controller and an IoT controller are adapted. The local storage is handled by the service and IoT controller. Thus, it enhances the data processing speed of PES requests and PES fulfillment. The service controller utilizes the Optimal Queue Model to manage the PES requests based on the minimum service queue length. The efficiency of the network is improved by fine-tuning the parameters from the Queue model with the aid of a Revised Fitness-based Political Optimizer (RF-PO). The multi-objective constraints like queue length, utilization, actual arrival time, expected arrival time, and end-to-end delay are utilized for the efficient supply chain system. These stimulated results show the feasibility and effectiveness of the supply chain framework.