The development of real-time applications integrated with Intent-Based Networking (IBN) integrates an Internet of Things (IoT), providing interconnection between heterogeneous devices and physical objects for the formulation of smart cities. These systems provide seamless communication and maintain the adaptive network policies and infrastructure. Many existing schemes have proposed solutions for efficient routing with support from intelligent architectures; however, although most of them overlook the bounded and limited resources of IoT networks, they impose additional overhead while addressing unpredictable communications in IBN-IoT. Furthermore, security and trustworthiness are significant research challenges that must be addressed to prevent data breaches and allow only the use of authentic devices. This research presents a scalable model for an IBN-IoT environment that utilizes edge computing to enable trustworthy and fault-tolerant communication with energy efficiency. Firstly, Software-Defined Networking (SDN) is explored for load balancing and effective resource allocation in 6G Internet of Things (IoT) systems. Secondly, the proposed model explores artificial intelligence techniques to analyze the network environment and predict anomalies in the fault tolerance approach. Lastly, data is kept private and maintained in integrity using a private blockchain, providing a more reliable, distributed, autonomous system with minimal overhead. Using synthetic data, the proposed model is validated against QGA-ACO and MER-ODLADT solutions for energy consumption, anomaly detection, and time-to-failure metrics across dynamic scenarios.

Abstract In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real‐time data and coped with time‐constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to control wireless channels and improve communication. However, most of the existing lack of handing network interference and a reliable monitoring process. Moreover, many solutions are vulnerable to external threats, resulting in inconsistent and untrustworthy information for end users. Thus, this article proposes a framework that considers possible shortest paths to provide the most reliable and low‐latency healthcare decision system using Q‐learning. In addition, fog devices offer a trusted transmission interference system and are kept secure. The proposed framework is specially designed for rapid real‐time medical data processing while enforcing robust security throughout the IoT‐based transmission process. To identify the health sensors in pairwise objects with the initial computing cost, the proposed framework applies graph theory. It also extracts the most effective and least loaded communication edges by examining the behaviour of devices. Moreover, the identities of devices are verified using lightweight timestamps and secret information, accordingly, it decreases the privacy threats.

Abstract The Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi‐parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in‐depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis.

By exploring the future network, smart technologies promote the development of cutting-edge industrial applications. Internet of Things (IoT) systems use sensing approaches to acquire data and control real-time processing and complex tasks. Several techniques have been proposed for coping with environmental behavior in industrial management and reducing the response in crucial circumstances. However, due to the unique and limited constraints of the industrial environment, managing data routing and sustainable development are recent research concerns. In addition, security is essential for industrial communication systems due to the probability of unauthorized access, thus trust level must be improved. The framework addresses real-world challenges in industrial networks by incorporating a lightweight data verification algorithm designed for green communication, reducing energy consumption while maintaining data integrity. First, predictive computing is implemented using ant colony optimization (ACO) based on real-time requirements and selects the dynamic and communication channels for data transmission across the industrial platform. Second, mobile sinks offer more authentic techniques for verifying sensor data and delivering it securely to the cloud servers. The framework was evaluated and validated in a simulation-based environment, revealing a considerable improvement in terms of network throughput, packet drop ratio, connectivity ratio, and network overhead over the existing approaches.

In the recent era, the communication network with the support of many wireless technologies is giving benefits for remote access. Such a communication model increases the flexibility for data storage with the management of network resources efficiently with the integration of the Internet of Things (IoT). Although, the industrial Internet of Things (IIoT) has enabled the development of numerous machine learning-based solutions to provide real-time applications. However, most of the solutions are not prepared to cope with heterogeneous services and the huge amount of device data in the digital world. Furthermore, conducting financial transactions over the Internet raises several security issues. Such restrictions compromise the sensitive data of financial institutions and also degrade the trust of network users in the system. Thus, this article presents a secured blockchain model for high-performance computing in a big data environment, which aims to protect the business activities for financial interaction with intelligent services of software-defined network (SDN) architecture. First, to keep the security credentials, the SDN controller creates an association between the IoT devices and maintains local and global records. Second, the machine learning approach is explored using reliable and fault-tolerant methods to support the network scalability and extract the updated routing information for transmitting financial data. The proposed protocol also provides data integrity with a high level of network availability and copes with the financial security of big data by investigating cryptographic approaches. Our proposed protocol is tested using simulation, and various experiments are performed to show its efficacy in terms of network throughput, computing overhead, data delay, response time, and dropped packets as compared to tunicate swarm algorithm-based optimized routing mechanism (TORM) and RouteChain.

The Internet of Things (IoT) connects heterogeneous sensors with dynamic networks to monitor smart communication and collect real-time data. Such systems are well adapted to satisfy the needs of smart cities and facilitate remote locations. Many cloud-based solutions for effective routing along with scalable data storage have been presented for constraint IoT systems. However, because of the unpredictable nature of mobile networks and communication links, most of the solutions may not be suitable for realistic applications and usually result in path failure with increasing resource utilization. Hence, data forwarding is only reliable and valuable if the proposed algorithms are trust aware with low overheads and consume balanced energy among nodes. Therefore, this article proposed a fault-tolerant supervised routing (Trust-FTSR) model for trust management in the IoT network, to improve trustworthiness and collaborative communication in smart cities. Each node evaluates the behavior of its neighbors and establishes a direct trust for a reliable and optimized network structure. In addition, using a supervised machine-learning technique, a fault-tolerant relaying system is provided without imposing additional overheads. Moreover, it removes the additional load in determining the optimal decision and training the IoT system to balance the network cost. In the end, a secure algorithm is proposed to ensure the privacy and authentication of the relaying system in the presence of critical attacks with secured keys. The proposed model is tested and its performance has significant improvement as compared to existing work.