Editorial:Blockchain‐based 6Gand industrial internet of things systems for industry 4.0/5.0

Abstract

The industrial internet of things (IIoT) and Industry 4.0/5.0 enable the integration of machinery, equipment, processes, and humans across a variety of vertical sectors, including manufacturing and logistics supply chains, transportation, and medical care (Yadav et al., 2022). Several types of sensor nodes link these interconnected machines/appliances, sensing and transmitting data to the nodes or the cloud. To manage those links, next-generation networking technologies, such as 6G and Cybertwin, are being introduced. Sixth-generation (6G) communication will be critical in providing complex wireless interconnections, with 6G networks expected to be capable of supporting millions of linked devices and systems while maintaining high data rates and low latency (Kanwal et al., 2022). Blockchain is one emerging technology that can contribute to IIoT stability. Blockchain looks to offer a solution to maintain user privacy while still preserving the capacity for immutable information and replication. A blockchain technology is used to enforce a free distributed ledger to record transactions by peer-to-peer network nodes (P2Ps) and to avoid the need for a central authority through a distributed consensus process. This special issue focuses on six high-quality studies that address real-world issues in IIoT. Sharma et al. (2021) suggested a blockchain-based IoT architecture that uses the identity-based encryption (IBE) algorithm to improve the security of healthcare data. In this case, the smart contract outlines all of the fundamental processes of the healthcare system, which can benefit all stakeholders. Many tests are carried out in order to assess the efficacy of the suggested strategy. The findings reveal that the suggested system outperforms the current well-known strategies. Al-Haija et al. (2022) built a strong classifier for identifying and categorizing various cyberattacks in IoT networks using the AdaBoost machine learning algorithm paired with decision trees and substantial data engineering techniques. We test our system using the TON IoT 2020 datasets, which are a collection of datasets designed particularly for three-layered IoT systems that include physical, network, and application layers. We compare our system's performance to that of existing cutting-edge technologies. Our experimental results show that our framework is capable of offering improved classification accuracy and reduces kinds 1 and 2 mistakes for building more durable IoT infrastructures. Babu et al. (2022) introduced a unique IoT device authentication technique based on IBE and a blockchain network. Blockchain is utilized as a distributed PKG, removing the single point of failure and key escrow issue associated with PKGs. Furthermore, the suggested work is implemented on Hyperledger Fabric, an open-source blockchain platform that effectively handles the adding, updating, and deletion operations required for successful IoT device authentication and communication. Mehbodniya et al. (2022) created a framework for signature creation and verification using a modified Lamport Merkle Digital Signature technique. It employs a central healthcare controller (CHC) to determine the origin of the created signature as well as verification and authentication. To validate the signature, the validation hash public key with create key is necessary. When compared with conventional approaches, this resulted in more efficient, cost-effective, and speedier security. Vigneysh et al. (2022) provided a successful technique for increasing dynamic responsiveness during system uncertainties such as voltage distortions, frequency changes, renewable energy source variations, and the presence of non-linear and unbalanced loads. It also increases the quality of current fed into the grid during uncertainty. The suggested control approach is used to manage both the dc side capacitor voltage and the current loop of a grid-connected inverter. The suggested system is simulated in the MATLAB Simulink environment, and its performance is compared with that of standard controllers to demonstrate the efficacy of the proposed control technique during system anomalies. Patil et al. (2022) developed a two-phased technique for blockchain and IoT federated networks that use a multi-criteria-based approach to connection selection. The dynamic gateway scheduling technique is capable of supporting both blockchain-based transactions and IoT device connectivity. Furthermore, the suggested technique improves the fairness of data transfer for each gateway, resulting in efficient data transmission. Before using the link selection process, machine learning (ML) approaches are used to examine the state of the communication channels. The links are then selected using multi-criteria statistical approaches. Finally, scheduling is carried out in order to identify the best gateway for quickly channelling blockchain data.