Blockchain enabled industrial Internet of Things technology

The emerging blockchain technology shows promising potential to enhance industrial systems and the Internet of things (IoT) by providing applications with redundancy, immutable storage, and encryption. In the past a few years, many more applications in industrial IoT (IIoT) have emerged and the blockchain technologies have attracted huge amounts of attention from both industrial and academic researchers. In this paper we address the integration of blockchain and IIoT from the industrial prospective. A blockchain enabled IIoT framework is introduced and involved fundamental techniques are presented. Moreover, main applications and key challenges are addressed. A comprehensive analysis for the most recent research trends and open issues is provided associated with the blockchain enabled IIoT

FLAME: trusted fire brigade service and insurance claim system using blockchain for enterprises

Smart fire detection and insurance systems have gained considerable attention from researchers and industries. At the same time, automatic requests for fire brigade services to cure fire and instant claims settlement to defend insurance fraud are lacking in literatures. We propose a trusted fire brigade service and insurance claim (FLAME) framework using blockchain for enterprises to provide immediate fire brigade services and prevent insurance frauds. A system model is presented to explain architecture, and overall functionality of FLAME using blockchain. Further, a sensing network and connectivity model is proposed to detect true fire and send an emergency service request to monitoring station. Smart contracts are designed to automate fire brigade service and insurance claim processes. A prototype of the FLAME is implemented on Hyperledger Besu blockchain using Istanbul Byzantine Fault Tolerance 2.0 consensus protocol. Simulation results show that latency and throughput of the FLAME are better compared to state-of-the-art models

The Telehealth Chain: a protocol for secure and transparent telemedicine transactions on the blockchain

Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. This paper proposes a theoretical framework for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technology to provide a secure and reliable platform for medical practitioners to remotely interact with patient transactions. The blockchain will serve as a one-stop digital service to secure patient data, ensure privacy, and facilitate payments. The proposed framework leverages the existing Hyperledger Fabric platform to build a secure blockchain-assisted telemedicine platform

Integrity and Privacy Protection for Cyber-physical Systems (CPS)

The present-day interoperable and interconnected cyber-physical systems (CPS) provides significant value in our daily lives with the incorporation of advanced technologies. Still, it also increases the exposure to many security privacy risks like (1) maliciously manipulating the CPS data and sensors to compromise the integrity of the system (2) launching internal/external cyber-physical attacks on the central controller dependent CPS systems to cause a single point of failure issues (3) running malicious data and query analytics on the CPS data to identify internal insights and use it for achieving financial incentive. Moreover, (CPS) data privacy protection during sharing, aggregating, and publishing has also become challenging nowadays because most of the existing CPS security and privacy solutions have drawbacks, like (a) lack of a proper vulnerability characterization model to accurately identify where privacy is needed, (b) ignoring data providers privacy preference, (c) using uniform privacy protection which may create inadequate privacy for some provider while overprotecting others.Therefore, to address these issues, the primary purpose of this thesis is to orchestrate the development of a decentralized, p2p connected data privacy preservation model to improve the CPS system's integrity against malicious attacks. In that regard, we adopt blockchain to facilitate a decentralized and highly secured system model for CPS with self-defensive capabilities. This proposed model will mitigate data manipulation attacks from malicious entities by introducing bloom filter-based fast CPS device identity validation and Merkle tree-based fast data verification. Finally, the blockchain consensus will help to keep consistency and eliminate malicious entities from the protection framework. Furthermore, to address the data privacy issues in CPS, we propose a personalized data privacy model by introducing a standard vulnerability profiling library (SVPL) to characterize and quantify the CPS vulnerabilities and identify the necessary privacy requirements. Based on this model, we present our personalized privacy framework (PDP) in which Laplace noise is added based on the individual node's selected privacy preferences. Finally, combining these two proposed methods, we demonstrate that the blockchain-based system model is scalable and fast enough for CPS data's integrity verification. Also, the proposed PDP model can attain better data privacy by eliminating the trade-off between privacy, utility, and risk of losing information

Industry 4.0:use of digitalization in healthcare

The primary objective of this chapter is to examine the AI applications for healthcare 4.0. Using a wide range of contemporary technologies, such as digitization, artificial intelligence, user response data (ergonomics), human psychology, the internet of things, machine learning, big data mining, and augmented reality, one of the great success stories of our day is healthcare. Worldwide life expectancy has increased due to the tremendous advancements in medical research. But when people live longer, healthcare systems must deal with more people needing their services, more money spent on them, and a staff that finds it more challenging to care for patients. A healthy, productive society depends heavily on the healthcare industry, making it one of the most critical industries in the larger big data environment. Artificial intelligence (AI), which builds on automation, has the potential to transform healthcare and assist in addressing some of the issues mentioned above. AI can support healthcare professionals, including physicians and nurses, in their day-to-day jobs. Artificial intelligence (AI) can improve patient outcomes by enhancing the quality of life and preventive care and producing more accurate diagnoses and treatment regimens. This book provides an overview of the most recent advancements in artificial intelligence (AI) applications in biomedicine, encompassing pharmaceutical processing, disease diagnosis, patient monitoring, biomedical information, and biomedical research. A summary of the most recent developments in the use of AI in healthcare is also provided, along with a road map for creating safe, dependable, and efficient AI systems and a discussion of potential future directions for AI-assisted healthcare systems. Numerous uses of AI exist in the medical field. Healthcare 4.0 has brought about a paradigm shift in the healthcare industry, drawing inspiration from Industry 4.0. Therefore, how the digital revolution in healthcare will affect the quality of medical care is still being determined. This study results will help the new researchers and healthcare institutions

Impact pathways: technology-aided supply chain planning for resilience

Purpose: Due to emerging uncertainties, supply chain planning (SCP) has become complex for many companies, and practitioners are unsure how emerging technologies can help. We address this gap by identifying pathways of how digital technologies may aid planned flexibility in SCP. Design/methodology/approach: The research builds on engaged scholarship and co-creation of knowledge. Eight senior managers from two companies participated in three rounds of workshops with us discussing and contrasting their planning challenges and potential solutions using examples of industry 4.0 applications drawn from the literature. Findings: Based on a novel framework for digital transition in SCP, this research shows how emerging technologies may aid SCP in building resilience to emergent uncertainties and open new research avenues through four impact pathways. Research limitations/implications: The SCP literature is ruefully short on studies that address technology-aided SC resilience. The research explains why this calls for a paradigm shift in SCP research. Originality/value: This research argues that resilience-building SCP requires planned flexibility and presents a digital transition framework that allows for it

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: problems, challenges and solutions

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, PoA-based Blockchain DLT has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provide recommendations that could facilitate the continuous growth of blockchain application in healthcare. Lastly, the study then explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks