The integration of artificial intelligence (AI) has caused information and communication technology (ICT) to undergo a number of recent rapid fluctuations. These changes have primarily affected the areas of management, end-to-end device interconnectivity, resource organization, communication, networking, and application-related aspects of ICT. Owing to the complex structure of applicational connectedness, evaluating each of the aforementioned opportunities concurrently reflects the idea of heterogeneity. The association of multiple end devices, particularly in interoperable space, integrity, privacy protection, security, provenance, and the massive volume of everyday media data generated in the modern healthcare setting could also provide significant issues. To address these issues, decentralized, secure, economical resource optimization, and intelligent network activities and organization are necessary. Blockchain technology plays a crucial role in providing distributed storage data organization, sharing, and exchange for automated decision-making, privacy, and security in AI-enabled machine learning (ML) models. However, machine learning models—support vector machine, in particular—have a significant impact on the growth of distributed consortium networks and the exchange of information among connected nodes, resolving issues with resource management, scalability, and data processing. By resolving the three main problems of seamless data integrity, peer-to-peer communication between nodes, and infrastructure security, we provide a novel interoperable technique in this proposed architecture. The approach is unique, as demonstrated by the simulation-based results, which display huge differences of 1.37%, 1.56%, and 1.87%, respectively. The background for the evaluation consists of the following three areas: (i) infrastructure security to protect automated decision-making; (ii) integrity between smooth data sharing and exchange; and (iii) network resource optimization to enable smooth communication across heterogeneous devices.

Despite the fact that countless IoT applications are arising frequently in various fields, such as green cities, net-zero decarbonization, healthcare systems, and smart vehicles, the smart grid is considered the most critical cyber–physical IoT application. With emerging technologies supporting the much-anticipated smart energy systems, particularly the smart grid, these smart systems will continue to profoundly transform our way of life and the environment. Energy systems have improved over the past ten years in terms of intelligence, efficiency, decentralization, and ICT usage. On the other hand, cyber threats and attacks against these systems have greatly expanded as a result of the enormous spread of sensors and smart IoT devices inside the energy sector as well as traditional power grids. In order to detect and mitigate these vulnerabilities while increasing the security of energy systems and power grids, a thorough investigation and in-depth research are highly required. This study offers a comprehensive overview of state-of-the-art smart grid cybersecurity research. In this work, we primarily concentrate on examining the numerous threats and cyberattacks that have recently invaded the developing smart energy systems in general and smart grids in particular. This study begins by introducing smart grid architecture, it key components, and its security issues. Then, we present the spectrum of cyberattacks against energy systems while highlighting the most significant research studies that have been documented in the literature. The categorization of smart grid cyberattacks, while taking into account key information security characteristics, can help make it possible to provide organized and effective solutions for the present and potential attacks in smart grid applications. This cyberattack classification is covered thoroughly in this paper. This study also discusses the historical incidents against energy systems, which depicts how harsh and disastrous these attacks can go if not detected and mitigated. Finally, we provide a summary of the latest emerging future research trend and open research issues.

Particularly in the context of smart cities, remote sensing data (RSD) has emerged as one of the hottest study topics in information and communication technology (ICT) today. The development of machine learning (ML) and artificial intelligence (AI) has made it possible to solve a number of issues, including automation, control access, optimization, monitoring, and management. Simultaneously, there are significant issues with the design and development of the process hierarchy, including inadequate training records, centralized architecture, data privacy protection, and overall resource consumption restrictions. The development of Distributed Ledger Technology (DLT), on the other hand, provides a decentralized infrastructure that allows systems to eliminate centralized data-sharing procedures of smart cities while transferring from network node to network node, and third-party access control solves machine learning issues. To process an ideal data delivery mechanism for the smart cities analytical model, the paper employs Partial Swam Optimization (POS) in conjunction with a secure blockchain distributed consortium network. This work makes three contributions. Firstly, it offers a safe transmission method that combines blockchain and machine learning to optimize the path for reliable data delivery across secure channels. Second, neighborhood encryption sequences are carried out using NuCypher proxy re-encryption-enabled value encryption, a public key cryptographic approach that avoids cypher conversion. Third, Artificial Neural Networks (ANNs) can solve the data deliverance classification problem in smart cities by optimizing record management and preservation.

The Industry 5.0 revolution has launched a new age of intelligent manufacturing equipment, which is crucial to our society and economy. Industry 5.0 aims to enhance human potential by integrating cutting-edge IT technology, Artificial Intelligence (AI), the Internet of Things (IoT), robots, and augmented reality into everyday living, especially in smart industrial settings, to maximize human potential. Smart Production Processes (SP2), accessible standards, and resource sharing on the web have increased network vulnerabilities as enterprises adopt them. These vulnerabilities primarily target industry IoT systems, threatening private data. Existing system issues include network vulnerabilities, production capability, and data management. Traditional applications struggle with industrial data’s bulk and complexity, causing data analysis, security, and privacy challenges. Modern industrial systems use AI and ML for big data analysis and data processing to overcome these issues. Optimizing human-machine synergy minimizes costs and equipment maintenance and boosts efficiency. An ML-assisted Smart Production Process (ML-SP2) and Intrusion Detection System (ML-IDS) have been proposed to assess and address Industrial 5.0 concerns. Data capture, predictive maintenance and optimization, transparent decision-making, and proactive maintenance are integrated into the manufacturing process with the ML-SP2. The ML-IDS detects network vulnerabilities using ensemble methods and manages distribution efficiently. The ML-IDS uses the variety of ML techniques such as random forest, decision tree and support vector machines that identifies the intruder with maximum prediction accuracy. In addition, the intruder activities are observed with the help of the convolution networks that improve the overall intruder activities recognition. The smart industrial production phase’s effectiveness is measured by the RPL (0.9), maintenance cost (2500$), production performance (1.0), VDR (97.6%), FAR, accuracy (99%), precision (94%)-recall (96%), and f1-measure (98%) compared to the existing methods such as ML-RF-FLID, NRDD-DBSCAN, and OC-SVM.

Accurate detection of small objects is critically important in high-stakes applications such as military reconnaissance and emergency rescue. However, low resolution, occlusion, and background interference make small object detection a complex and demanding task. One effective approach to overcome these issues is the integration of multimodal image data to enhance detection capabilities. This paper proposes a novel small object detection method that utilizes three types of multimodal image combinations, such as Hyperspectral–Multispectral (HS-MS), Hyperspectral–Synthetic Aperture Radar (HS-SAR), and HS-SAR–Digital Surface Model (HS-SAR-DSM). The detection process is done by the proposed Jaccard Deep Q-Net (JDQN), which integrates the Jaccard similarity measure with a Deep Q-Network (DQN) using regression modeling. To produce the final output, a Deep Maxout Network (DMN) is employed to fuse the detection results obtained from each modality. The effectiveness of the proposed JDQN is validated using performance metrics, such as accuracy, Mean Squared Error (MSE), precision, and Root Mean Squared Error (RMSE). Experimental results demonstrate that the proposed JDQN method outperforms existing approaches, achieving the highest accuracy of 0.907, a precision of 0.904, the lowest normalized MSE of 0.279, and a normalized RMSE of 0.528.

In applications related to smart cities, the Internet of Things (IoT) promotes service scalability regardless of variations in user density. Many customers require several safety procedures to supply reliable and effective application services. Undependable user identity was the cause of the current case of Permanent Denial of Service (PDoS). The article discusses service-dependent application authentication (SRAA), a defense against PDoS attacks, in the context of smart cities. This authentication method uses the controlled access distribution mechanism to provide application security. The user application’s link connectivity and synchronisation capabilities with the user device are used in the monitored access distribution. Backpropagation (BP) learning is used to find errors in the user device, implementation, and verification connections. BP learning minimises the given weights using the anomaly learned from the initial access distribution phase. The anomaly has been identified in order of earlier training eras to enable coordinated authorisation for the distributed services. PDoS causes fewer weights to become disconnected from the service, diminishing the number of service failures for linked devices. The experimental findings have been implemented, and the suggested SRAA model lowers computation overhead by 18.14% and false rate by 10.96%, access success by 9.07%, authenticating duration by 15.38% and synchronisation failure by 8.94% compared to other existing models.