In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.

Electronic Health Records (EHRs) allows patients to control, share, and manage their health records among family members, friends, and healthcare service providers. Thus, pri- vacy, confidentiality, and data consistency are major challenges in such an environment. Although, cloud-based EHRs addresses the aforementioned issues, but these are prone to malicious attacks, trust management, and non-repudiation among servers. Hence, blockchain-based EHR systems are preferable to create the trust, security, and privacy among healthcare users. Motivated from the aforementioned discussions, we proposes a framework called as Blockchain-Based Deep Learning as-a-Service (BinDaaS). It integrates blockchain and deep-learning techniques for sharing EHR records among multiple healthcare users and operates in two phases. In the first phase, an authentication and signature scheme is proposed based on lattices-based cryptography to resist collusion attacks among N-1 healthcare authorities from N. In the second phase, Deep Learning as-a-Service (DaaS) is used on stored EHR datasets to accurately predict future diseases based on current indicators and features of patient. The obtained results are compared using various parameters such as accuracy, end-to- end latency, mining time, and computation and communication costs in comparison to the existing state-of-the-art proposals.

In smart communities, social media has allowed users easy access to multimedia content. With recent advancements in computer vision and natural language processing, machine learning (ML), and deep learning (DL) models have evolved. With advancements in generative adversarial networks (GAN), it has become possible to create fake images/audio/and video streams of a person or use some person’s audio and visual details to fit other environments. Thus, deepfakes are specifically used to disseminate fake information and propaganda on social circles that tarnish the reputation of an individual or an organization. Recently, many surveys have focused on generating and detecting deepfake images, audio, and video streams. Existing surveys are mostly aligned toward detecting deepfake contents, but the generation process is not suitably discussed. To address the survey gap, the paper proposes a comprehensive review of deepfake generation and detection and the different ML/DL approaches to synthesize deepfake contents. We discuss a comparative analysis of deepfake models and public datasets present for deepfake detection purposes. We discuss the implementation challenges and future research directions regarding optimized approaches and models. A unique case study, IBMM is discussed, which presents a multi-modal overview of deepfake detection. The proposed survey would benefit researchers, industry, and academia to study deepfake generation and subsequent detection schemes.

Industry 4.0 have witnessed a paradigm shift from cyber-physical systems (CPS) that aims at massive automation, towards a more customer-driven approach. The shift has been attributed to the design of hyper-cognitive systems, integration of virtual and extended reality, digital machinery prototyping and twin designs, trusted machine boundaries, collaborative robots, and artificial intelligence (AI)-based supply chains. This new wave, termed Industry 5.0, is expected to leverage massive production with user-centric customization outside the scope of Industry 4.0 ecosystems. Industry 5.0 is expected to assist diverse industrial verticals like healthcare, smart farming, drones, smart grids, and supply chain production ecosystems. However, data is shared among multiple heterogeneous networks, spanning different authoritative domains. Thus, trusted and secured data transfer is crucial to synergize and secure the industrial perimeters. Blockchain (BC) is a preferred choice as a security enabler to Industry 5.0 ecosystems owing to its inherent property of immutability, chronology, and auditability in industrial systems. Limited works are proposed that present the vision and holistic view of BC-assisted Industry 5.0 applications. The article presents a first-of-its-kind survey on BC as a security enabler in Industry 5.0. Based on a descriptive survey methodology and research questions, we presented the key drivers, and potential applications, and propose an architectural vision of BC-based Industry 5.0 in diverse applicative verticals. The survey intends to present solutions that would assist industry practitioners, academicians, and researchers to drive novel BC-assisted solutions in Industry 5.0 verticals.

Advancements in Healthcare Internet of Things (H-IoT) systems have created new opportunities and solutions for healthcare services, including the remote treatment and monitoring of patients. In addition, the security and privacy of personal health data must be ensured during data transfer. Security breaches in H-IoT can have serious safety and legal implications. This comprehensive review provides insights about secured data accession by employing cryptographic platforms such as H-IoT in big data, H-IoT in blockchain, H-IoT in machine learning and deep learning, H-IoT in edge computing, and H-IoT in software-defined networks. With this information, this paper reveals solutions to mitigate threats caused by different kinds of attacks. The prevailing challenges in H-IoT systems, including security and scalability challenges, real-time operating challenges, resource constraints, latency, and power consumption challenges are also addressed. We also discuss in detail the current trends in H-IoT, such as remote patient monitoring and predictive analytics. Additionally, we have explored future prospects, such as leveraging health data for informed strategic planning. A critical analysis performed by highlighting the prevailing limitations in H-IoT systems is also presented. This paper will hopefully provide future researchers with in-depth insights into the selection of appropriate cryptographic measures to adopt an energy-efficient and resource-optimized healthcare system.

Modern industry 4.0 applications are shifting towards decentralized automation of computing and cyber-physical systems (CPS), which necessitates building a robust, secure, and efficient system that performs complex interactions with other physical processes. To handle complex interactions in CPS, trust and consensus among various stakeholders is a prime concern. In a similar direction, consensus algorithms in blockchain have evolved over the years that focus on building smart, robust, and secure CPS. Thus, it is imperative to understand the key components, functional characteristics, and architecture of different consensus algorithms used in CPS. Many consensus algorithms exist in the literature with a specified set of functionalities, performance, and computing services. Motivated from these facts, in this survey, we present a comprehensive analysis of existing state-of-the-art consensus mechanisms and highlight their strength and weaknesses in decentralized CPS applications. In the first part, we present the scope of the proposed survey and identify gaps in the existing surveys. Secondly, we present the review method and objectives of the proposed survey based on research questions that address the gaps in existing studies. Then, we present a solution taxonomy of decentralized consensus mechanisms for various CPS applications. Then, open issues and challenges are also discussed in deploying various consensus mechanisms in the CPS with their merits and demerits. The proposed survey will act as a road-map for blockchain developers and researchers to evaluate and design future consensus mechanisms, which helps to build an efficient CPS for industry 4.0 stakeholders.