Industry 4.0 relates to the digital revolution of manufacturing and other sectors, such as retail, distribution, oil and gas, and infrastructure. Meanwhile, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector’s productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IIoT characteristics. This paper offers an in-depth review of Industry 4.0 and IIoT, where the primary motivation behind this is to introduce the most recent advancements related to Industry 4.0 and IIoT, as well as to address the existing limitations. Firstly, this paper presents a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Secondly, this paper provides a comprehensive review of the state-of-the-art of Industry 4.0 in terms of concepts, requirements, and supporting technology. In addition, the correlation between enabling technology and technical requirements is discussed in detail. Finally, this paper highlights deep learning, edge computing, and big data as key techniques for the future directions of IIoT. Furthermore, the presented techniques are thoroughly examined to present an alternative method for future adoption. In addition to the showcased techniques, a new architecture for the future of IIoT based on these three primary techniques is also proposed.

An inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server’s computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.

Internet of Vehicles (IoV) is a multinode network that exchanges information in an open, wireless environment. Various communication activities exist between IoV entities to share important information such as (ID, location, speed, messages, and traffic information), necessary for network operation. As part of intelligent transportation, IoV is considered a hot subject for researchers, because it is still facing many unresolved challenges, especially those concerning security and privacy. The variation of security-privacy threats that can menace the safety, privacy, and lives of vehicle occupants makes security the leading point of interest. The development of communication protocols for autonomous vehicles opens to us new issues to study and enhance the performance of IoV networks in terms of security and privacy. Several works have been reported, proposing many solutions for practical security challenges including a considerable number of survey-review papers published in respectable channels. The main motive of this review paper is to present the latest developments related to IoV security, as well as to address existing limitations. The high frequency of publication on IoV architecture, security, and new solutions leads us to write a compact, comprehensive, and up-to-date review. Inclusion criteria for selected papers include recent publications, number of citations, and impact of the research. In the present survey paper, the IoV architecture model is defined with all related communication types, and security and privacy issues are analyzed and presented with recently proposed solutions in a clear method. Clear classifications of threats, attacks, protocols, and solutions are presented. Moreover, the use of blockchain-based IoV to improve system security is discussed highlighting the most important trends and taxonomies. The paper was written to be a candidate as the first to read on the topic of the IoV security challenge, presenting problems and solutions in a clear, smooth, complete, and integrated manner.

The Internet of Vehicles (IoV) is a network that connects vehicles and their environment: in-built devices, pedestrians, and infrastructure through the Internet using heterogeneous access technologies. During communication between vehicles, roadside units, and control rooms, data confidentiality and privacy are critical issues that require effective measures. Several works have been proposed for securing IoV environments based on vehicles-to-infrastructure authentication; However, some schemes have security vulnerabilities, while others have shown efficiency issues. Due to its decentralization, stability, and transaction tracking capabilities, Blockchain as an emerging technology presents a potential solution for IoV security. This article provides an in-depth examination of the benefits of blockchain for a 5G-based IoV environment. In particular, we propose and evaluate a novel blockchain-based secure data exchange (BSDCE-IoV) scheme based on Elliptic Curve Cryptography algorithm. Our solution is designed to eliminate several potential attacks that pose a threat to the IoV environment. Deep examination using the Real-or-Random oracle model and Scyther tool, in addition to the informal security analysis, validates the scheme regarding security and privacy. The Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL) assesses the computational and communication overhead. Computational and communicative overheads were also evaluated using the Multi-precision Integer and Rational Arithmetic Cryptographic Library (MIRACL). BSDCE-IoV shows higher performance in terms of security, functionality, and time delay than a number of recent selective work in IoV security.

Ultra dense network (UDN) is the extreme densification of heterogeneous radio access technologies (RATs) that are deployed closely in a coordinated or uncoordinated manner. The densification of RATs forms an overlapping zone of signal coverage, leading user equipment (UE) to frequent signal handovers among the available RATs. Consequently, this degrades the overall system performance. The traditional approach of RAT selection is network-centric and the decision is primarily focused on the signal aspect. However, the next generation of digital wave is a paradigm shift to being user-centric. In this paper, a context-aware multi-attribute RAT (CMRAT) selection approach is proposed to eliminate unnecessary handover of UE among RATs and determine the best RAT as the next point of attachment among the available ones in the UDN. CMRAT integrates the context-aware concept with multi-attribute decision making (MADM) theory in RAT selection. CMRAT is formed with two mechanisms, including, first, a context-aware analytical hierarchy process mechanism to prioritize the criteria for obtaining the weight. Then, a context-aware technique for order preference by similarity to an ideal solution mechanism is employed to choose the best RAT amongst the available RATs. The proposed CMRAT mechanism was implemented and validated using MATLAB. The obtained simulation findings demonstrate that the proposed CMRAT approach outperforms classic MADM methods, namely TOPSIS, SAW, and GRA with respect to the number of handovers and ranking abnormality metrics. Hence, this paper paves the way to choose RAT based on context information comprising network and user preference criteria information.

The integration of predictive maintenance (PdM) with the Industrial Internet of Things (IIoT) represents a pivotal shift in equipment management, particularly with the incorporation of deep learning (DL) for processing time series data from IIoT devices. This combination offers a sophisticated approach to predictive analysis, harnessing DL’s prowess in analyzing complex patterns in large data sets. However, it also presents notable challenges, including significant security risks associated with centralized organizations and the immense volume of time series data generated by IIoT. To address these issues, our study introduces an innovative decentralized framework thoughtfully segmented into device and edge levels. This framework leverages the strengths of blockchain technology and the interplanetary file system (IPFS). IPFS effectively manages the large-scale storage needs of time series data for DL applications in a decentralized manner, while blockchain provides a robust foundation for ensuring data security and maintaining consistent transactions. Furthermore, we conducted thorough performance analyses, examining aspects, such as accuracy, execution time, and computational cost, which validated the efficacy of our approach. Security considerations were also rigorously evaluated, focusing on potential attacker scenarios, the strengths of a decentralized architecture, and the immutable nature of smart contracts. The results highlight our framework’s exceptional ability to ensure the highest level of security in DL, maintain data integrity, and preserve model accuracy. In conclusion, the seamless integration of DL, PdM, blockchain, and IPFS in our framework marks a significant advancement in contemporary industrial maintenance strategies. It successfully bridges the gap between advanced security needs and the handling of vast quantities of data, positioning our approach at the forefront of modern industrial maintenance solutions.