The potential of digital twin technology is yet to be fully realised due to its diversity and untapped potential. Digital twins enable systems’ analysis, design, optimisation, and evolution to be performed digitally or in conjunction with a cyber-physical approach to improve speed, accuracy, and efficiency over traditional engineering methods. Industry 4.0, factories of the future, and digital twins continue to benefit from the technology and provide enhanced efficiency within existing systems. Due to the lack of information and security standards associated with the transition to cyber digitisation, cybercriminals have been able to take advantage of the situation. Access to a digital twin of a product or service is equivalent to threatening the entire collection. There is a robust interaction between digital twins and artificial intelligence tools, which leads to strong interaction between these technologies, so it can be used to improve the cybersecurity of these digital platforms based on their integration with these technologies. This study aims to investigate the role of artificial intelligence in providing cybersecurity for digital twin versions of various industries, as well as the risks associated with these versions. In addition, this research serves as a road map for researchers and others interested in cybersecurity and digital security.

“Internet of Things (IoT)” has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g., human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.

The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.

The internet of things is a worldwide technological development in communications. Low Power and Lossy Networks (LLN) are a fundamental part of the internet of things with numerous monitoring and controlling applications. This network has many challenges as well, due to limited hardware and communication resources, which causes problems in applications such as routing, connections, data transfer, and aggregation between nodes. The IETF group has provided a routing model for LLN networks, which expands IPv6 protocol based on Routing Protocol (RPL). The pro-posed decision system DDSLA-RPL creates a list of limited k member optimal parents based on qualitatively effective parameters such as hop, link quality, SNR rate, and ETX energy consumption, by informing child nodes of their connection link to available parents. In the routing section, a decision system approach based on learning automata has been proposed to dynamically determine and update the weight of influential parameters in routing. The effective parameters in the routing phase of DDSLA-RPL include battery depletion index, connection delay, and node queuing and throughput. The results of the simulation show that the proposed method outperforms other methods by about 30, 17, 20, 18, and 24 percent in mean longevity and energy efficiency, graph sustainability, operational power and latency, packet delivery rate test, and finally number of control messages test, respectively.

As a novel concept in technology and communication world, “Internet of Things (IoT)” has been emerged. In such modern technology, the capability to transmit data through data communication networks (such as Internet or Intranet) is provided for each organism (e.g. human being, animals, things, and so forth). Due to the limited hardware and communication operational capability as well as small dimensions, IoT undergoes quite a few challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph is increased through restricting the degree; and network congestion is reduced as a result. Besides, a dynamic data aggregation approach -named as LA-RPL- is proposed for RPL networks. More specifically, each node is equipped with learning automata in order to perform data aggregation and transmissions. Simulation results demonstrate that the proposed approach outperforms previously suggested based approaches in terms of energy consumption, network control overhead, and packet loss rate.

With the widespread adoption of the Internet of Things, underwater control and monitoring systems for applications such as ocean data sampling, natural disaster prevention, and submarine exploration have gained significance. However, underwater communication poses challenges like node mobility, long propagation delays, and limited bandwidth, requiring new solutions. This paper introduces a topology control platform, UWRPL, modeled on the RPL tree structure, with modifications for underwater environments. UWMRPL extends UWRPL to support node mobility, incorporating flexible objective functions, decision-making systems, and optimized scheduling to enhance network performance. Through extensive simulations, our proposed methods demonstrate about 8–23% longer network lifetime, 5–21% higher packet delivery ratio, and up to 30–50% lower end-to-end delay compared to leading protocols while also achieving more balanced energy consumption. These gains stem from reduced communication overhead, a multi-route approach to minimize congestion, and an adaptive mechanism that efficiently accommodates node movement. As a result, the proposed UWRPL and UWMRPL methods offer a more stable and effective platform for underwater IoT applications, addressing the inherent challenges of underwater communication.