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•We present a comprehensive review on blockchain-based 5G-enabled IoT and discussed its potential industrial applications in detail.•We also discuss various research challenges for ...integration of blockchain with 5G-enabled IoT for industrial automation.•Finally, this paper bridges the gap between the scalability, interoperability, and other research challenges.
Internet-of-Things (IoT) has made ubiquitous computing a reality by extending Internet connectivity in various applications deployed across the globe. IoT connect billions of objects together for high speed data transfer especially in 5G-enabled industrial environment during information collection and processing. Most of the issues such as access control mechanism, time to fetch the data from different devices and protocols used may not be applicable infor future applications as these protocols are based upon a centralized architecture. This centralized architecture may have a single point of failure alongwith the computational overhead. So, there is a need for an efficient decentralized access control mechanism for device-to-device (D2D) communication in various industrial sectors IoT-enabled industrial automation. In such an environment, security and privacy preservation are major concerns as most of the solutions are based upon the centralized architecture. To mitigate the aforementioned issues, in this paper, we present an in-depth survey of state-of-the-art proposals having 5G-enabled IoT as a backbone for blockchain-based industrial automation for the applications such as-Smart city, Smart Home, Healthcare 4.0, Smart Agriculture, Autonomous vehicles and Supply chain management. From the existing proposals, it has been observed that blockchain can revolutionize most of the current and future industrial applications in different sectors by providing a fine-grained decentralized access control. Various transactions and database logs can be traced efficiently using blockchain for consistency and preivacy preservation in the aforementiioned industrial sectors. The open issues and challenges of 5G-enabled IoT for blockchain-based Industrial automation are also analyzed in the text. Finally, a comparison of existing proposals with respect to various parameters is presented which allows the end users to select one of the proposals in comparison to its merits over the others.
Modern healthcare systems are characterized as being highly complex and costly. However, this can be reduced through improved health record management, utilization of insurance agencies, and ...blockchain technology. Blockchain was first introduced to provide distributed records of money-related exchanges that were not dependent on centralized authorities or financial institutions. Breakthroughs in blockchain technology have led to improved transactions involving medical records, insurance billing, and smart contracts, enabling permanent access to and security of data, as well as providing a distributed database of transactions. One significant advantage of using blockchain technology in the healthcare industry is that it can reform the interoperability of healthcare databases, providing increased access to patient medical records, device tracking, prescription databases, and hospital assets, including the complete life cycle of a device within the blockchain infrastructure. Access to patients’ medical histories is essential to correctly prescribe medication, with blockchain being able to dramatically enhance the healthcare services framework. In this paper, several solutions for improving current limitations in healthcare systems using blockchain technology are explored, including frameworks and tools to measure the performance of such systems, e.g., Hyperledger Fabric, Composer, Docker Container, Hyperledger Caliper, and the Wireshark capture engine. Further, this paper proposes an Access Control Policy Algorithm for improving data accessibility between healthcare providers, assisting in the simulation of environments to implement the Hyperledger-based eletronic healthcare record (EHR) sharing system that uses the concept of a chaincode. Performance metrics in blockchain networks, such as latency, throughput, Round Trip Time (RTT). have also been optimized for achieving enhanced results. Compared to traditional EHR systems, which use client-server architecture, the proposed system uses blockchain for improving efficiency and security.
In recent years, rapid technological advancements in smart devices and their usage in a wide range of applications exponentially increases the data generated from these devices. So, the traditional ...data analytics techniques may not be able to handle this extreme volume of data known as Big Data (BD) generated by different devices. However, this exponential increase of data opens the doors for the different type of attackers to launch various attacks by exploiting various vulnerabilities (SQL injection, OS fingerprinting, malicious code execution, etc.) during data analytics. Motivated from the aforementioned discussion, in this paper, we explored Machine Learning (ML) and Deep Learning (DL)-based models and techniques which are capable off to identify and mitigate both the known as well as unknown attacks. ML and DL-based techniques have the capabilities to learn from the traffic pattern using training and testing datasets in the extensive network domains to make intelligent decisions concerning attack identification and mitigation. We also proposed a DL and ML-based Secure Data Analytics (SDA) architecture to classify normal or attack input data. A detailed taxonomy of SDA is abstracted into a threat model. This threat model addresses various research challenges in SDA using multiple parameters such as-efficiency, latency, accuracy, reliability, and attacks launched by the attackers. Finally, a comparison of existing SDA proposals with respect to various parameters is presented, which allows the end users to select one of the SDA proposals in comparison to its merits over the others.
Due to the proliferation of ICT during the last few decades, there is an exponential increase in the usage of various smart applications such as smart farming, smart healthcare, supply-chain & ...logistics, business, tourism and hospitality, energy management etc. However, for all the aforementioned applications, security and privacy are major concerns keeping in view of the usage of the open channel, i.e., Internet for data transfer. Although many security solutions and standards have been proposed over the years to enhance the security levels of aforementioned smart applications, but the existing solutions are either based upon the centralized architecture (having single point of failure) or having high computation and communication costs. Moreover, most of the existing security solutions have focussed only on few aspects and fail to address scalability, robustness, data storage, network latency, auditability, immutability, and traceability. To handle the aforementioned issues, blockchain technology can be one of the solutions. Motivated from these facts, in this paper, we present a systematic review of various blockchain-based solutions and their applicability in various Industry 4.0-based applications. Our contributions in this paper are in four fold. Firstly, we explored the current state-of-the-art solutions in the blockchain technology for the smart applications. Then, we illustrated the reference architecture used for the blockchain applicability in various Industry 4.0 applications. Then, merits and demerits of the traditional security solutions are also discussed in comparison to their countermeasures. Finally, we provided a comparison of existing blockchain-based security solutions using various parameters to provide deep insights to the readers about its applicability in various applications.
From the past few years, Unmanned Aerial Vehicles (UAVs) has proved an immense potential in providing the cost and time‐efficient solutions to the various societal applications such as healthcare, ...supply chain, and video & surveillance. It has many data security and privacy issues, and researchers across the globe have given many solutions to protect data from cyber‐attacks. Many of them have suggested cryptographic‐based solutions, which is very compute extensive. Very few researchers have suggested Blockchain (BC)‐based solutions, but their solutions may suffer from high data storage cost as well as network latency, reliability, and bandwidth issues. To overcome the above‐mentioned issues, this paper proposed an InterPlanetary File System and BC‐based secure UAV communication scheme over the 6G network. This proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Then, the research challenges and future directions for further improvement of the proposed system have been presented.
Applications of Blockchain (BC) technology and Cyber-Physical Systems (CPS) are increasing exponentially. However, framing resilient and correct smart contracts (SCs) for these smart application is a ...quite challenging task because of the complexity associated with them. SC is modernizing the traditional industrial, technical, and business processes. It is self-executable, self-verifiable, and embedded into the BC that eliminates the need for trusted third-party systems, which ultimately saves administration as well as service costs. It also improves system efficiency and reduces the associated security risks. However, SCs are well encouraging the new technological reforms in Industry 4.0, but still, various security and privacy challenges need to be addressed. In this paper, a survey on SC security vulnerabilities in the software code that can be easily hacked by a malicious user or may compromise the entire BC network is presented. As per the literature, the challenges related to SC security and privacy are not explored much by the authors around the world. From the existing proposals, it has been observed that designing a complex SCs cannot mitigate its privacy and security issues. So, this paper investigates various Artificial Intelligence (AI) techniques and tools for SC privacy protection. Then, open issues and challenges for AI-based SC are analyzed. Finally, a case study of retail marketing is presented, which uses AI and SC to preserve its security and privacy.
In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. ...Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion.
For the past few years, the automation of transportation becomes a hot research topic for smart cities. Intelligent Transportation System (ITS) aims to manage and optimize the traffic congestion, ...road accidents, parking allocation using Autonomous Vehicles (AV) system, where the AVs are internally connected for message passing and critical decision making in time-sensitive applications. The data security in such applications can be offered using Blockchain (BC) technology. But, as per the existing literature, there exists no system which can call AVs automatically based on the situation, i.e., call an ambulance in case of an accident, call logistic service in case of home transfer, and call the traffic department in case of traffic jam. Motivated from the aforementioned reasons, in this article, we propose a BC-based secure and intelligent sensing and tracking architecture for AV system using beyond 5G communication network. Recently, AVs are facing issues with sensing and tracking technology as well as the data thefts. AV system contains sensitive information and transfers it through a communication channel to Connected AVs (CAVs), where the corrupted information or delay of a fraction of a second can lead to a critical situation. So, here we present possib the attacks and safety countermeasures using BC technology to protect the AV system. The proposed architecture ensures secure sensing and tracking of an object through BC by deploying AI algorithms at the edge servers. Also, the beyond 5G network enables communications with low latency and high reliability to meet the desires of the aforementioned time-sensitive applications. The proposed system is evaluated by considering the parameters as mobility and data transfer time against the traditional LTE-A and 5G communication networks. The proposed system outperforms traditional systems and can be suitable for diverse applications where latency, reliability, and security are the prime concerns.
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.
With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment ...analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system.
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