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Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption. By using smart contracts for automated access control, a reputation-based trust model for decentralized identity management, and inner-product encryption for fine-grained access control, the framework ensures data security and efficiency. Firstly, smart contracts are employed to automate access control, and software-defined boundaries are defined for different application domains. Secondly, through a trust model based on a consensus algorithm of node reputation values and a registration-based inner-product encryption algorithm supporting fine-grained access control, zero-trust self-sovereign enhanced identity management in the 6G environment of the Internet of Things is achieved. Furthermore, the use of multiple auxiliary chains for storing data across different application domains not only mitigates the risks associated with data expansion but also achieves micro-segmentation, thereby enhancing the efficiency of access control. Finally, empirical evidence demonstrates that, compared with the traditional methods, this paper's scheme improves the encryption efficiency by 14%, reduces the data access latency by 18%, and significantly improves the throughput. This mechanism ensures data security while maintaining system efficiency in environments with large-scale data interactions. [ABSTRACT FROM AUTHOR]

Summary: Internet of things (IoT) has massively adopted the market due to the current era demanding fully intelligent and autonomous services. However, efficient network management and message exchange are challenging in a dynamic and energy‐constrained IoT environment. Hence, for efficient message passing in IoT applications, softwarization of the IoT network is essential, wherein the logical control plane is decoupled from the data plane consisting of hardware devices such as routers and switches. Using softwarization, a centralized software‐defined networking (SDN) controller is responsible for routing data packets from source to destination in a dynamic environment. However, to reduce the computational overhead of filtering malicious and nonmalicious packets, artificial intelligence (AI) classifiers prove beneficial. Moreover, such challenges have trade‐offs with network requirements such as ultralow latency, high reliability, and higher data rates. Motivated by this, we propose an AI‐enabled network softwarization scheme for efficient message exchange under the 6G network. Lastly, the performance of the proposed scheme is evaluated with different performance metrics, such as accuracy, precision, f1‐score, packet drop ratio, and latency. The empirical result revealed that the proposed scheme outperforms in terms of accuracy and controller efficiency, that is, 81.64% and 82.2%, respectively. [ABSTRACT FROM AUTHOR]

Technological innovations in the Internet of Things (IoT) enable the acquisition, distribution, and interpretation of massive data, facilitating the development of numerous versatile and responsive applications in real-world contexts. However, IoT faces challenges in its centralization, privacy preservation, latency, and security. This paper uses prominent decentralized technologies like blockchain and federated learning to provide Quality-of-Services on 6G mobile networks to preserve privacy. In order to protect user privacy, user equipment (UE) at the distributed federated layer is trained using a machine learning model that masks sensitive data from service providers or masqueraders. Consequently, it empowers UE to converge on a global model by executing local gradient updates with network automation. We leverage blockchain as a decentralized sharing model over the federated layer to allow wireless networks to offload computational load in rendering real-time IoT data with an accuracy of 98%. A novel dynamic authentication framework is proposed to enable UE to interact with various base stations in the 6G wireless network with an average computation, aggregation and response time of 105 ms, 3.84 ms and 45.2 s respectively. The Distributed Hash Table at the core Blockchain layer offers decentralized secure storage with an improved throughput of 1890 TPS. [ABSTRACT FROM AUTHOR]

Free Space Optics (FSO)-based UAV-enabled wireless power transfer (WPT) relay systems have emerged as a key technology for 6G networks, efficiently providing continuous power to Internet of Things (IoT) devices even in remote areas such as disaster recovery zones, maritime regions, and military networks, while addressing the limited battery capacity of UAVs through the FSO fronthaul link. However, the harvested power at the ground devices depends on the displacement and diameter of the FSO beam spot reaching the UAV, as well as the UAV trajectory, which affects both the FSO link and the radio-frequency (RF) link simultaneously. In this paper, we propose a joint design of the divergence angle in the FSO link and the UAV trajectory, in order to maximize the power transfer efficiency. Driven by the analysis of the optimal condition for the divergence angle, we develop a hybrid BS-PSO-based method to jointly optimize them while improving optimization performance. Numerical results demonstrate that the proposed method substantially increases power transfer efficiency and improves the optimization capability. [ABSTRACT FROM AUTHOR]

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power p t of 20 dBm and IRS amplification power P A of 4 dBm. Furthermore, the system outage probability is carried out with change in p t and amplification gain a. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements N , p t and P A . In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed. [ABSTRACT FROM AUTHOR]

The advent of sixth-generation (6G) networks promises revolutionary advancements in wireless communication, marked by unprecedented speeds, ultra-low latency, and ubiquitous connectivity. This research paper delves into the integration of Artificial Intelligence (AI) in 6G network applications, exploring the challenges and outlining future directions for this transformative synergy. The study investigates the key AI technologies for 6G: the potential of AI to optimize network performance, enhance user experience, and enable novel applications in diverse domains and AI-enabled applications. Analyzing the current landscape, the paper identifies key challenges such as scalability, security, and ethical considerations in deploying AI-enabled 6G networks. Moreover, it explores the dynamic interplay between AI and 6G technologies, shedding light on the intricate relationships that underpin their successful integration. The research contributes valuable insights to the ongoing discourse surrounding the convergence of AI and 6G networks, laying the groundwork for a robust and intelligent future communication infrastructure. [ABSTRACT FROM AUTHOR]

Abstract The rapid evolution and integration of next-generation Internet-of-things (NG-IoT) applications present new complexities for sixth-generation (6G) mobile communication networks, including the need for extensive connectivity, increased network capacity, and ultralow-latency communications. While ultradense networking, characterized by deploying numerous base stations, offers a potential solution, practical and financial constraints limit its feasibility. Drone-based stations (DBSs) emerge as a flexible alternative, but their optimal positioning remains a critical challenge due to finite energy sources and potential signal degradation. This study introduces a new quasi-opposition-based lemurs optimizer (QOBLO) to address the optimal placement of DBSs in NG-IoT networks. QOBLO combines lemur foraging behaviour with quasi-opposition-based learning to enhance exploration and exploitation in the optimization process. The performance of QOBLO was evaluated across three scenarios and compared with other swarm intelligence algorithms using metrics such as Friedman’s ranking test (FRT) and the Wilcoxon signed-rank test (WSRT). Rigorous simulations emulated real-world conditions and varying network demands, testing QOBLO's adaptability and robustness. Results indicate that QOBLO significantly outperforms other algorithms, achieving a top FRT value of 1.234 and demonstrating superior p values in the WSRT. These findings highlight QOBLO's capability to enhance connectivity, coverage, and energy efficiency in 6G environments. The primary contribution of this research is the development of QOBLO, a scalable and efficient method for optimizing DBS deployment. This new approach improves network performance in complex NG-IoT applications, offering a robust solution to the challenges of 6G networks and ensuring enhanced reliability and sustainability of future communication infrastructures.

The advent of sixth-generation (6G) networks promises revolutionary advancements in wireless communication, marked by unprecedented speeds, ultra-low latency, and ubiquitous connectivity. This research paper delves into the integration of Artificial Intelligence (AI) in 6G network applications, exploring the challenges and outlining future directions for this transformative synergy. The study investigates the key AI technologies for 6G: the potential of AI to optimize network performance, enhance user experience, and enable novel applications in diverse domains and AI-enabled applications. Analyzing the current landscape, the paper identifies key challenges such as scalability, security, and ethical considerations in deploying AI-enabled 6G networks. Moreover, it explores the dynamic interplay between AI and 6G technologies, shedding light on the intricate relationships that underpin their successful integration. The research contributes valuable insights to the ongoing discourse surrounding the convergence of AI and 6G networks, laying the groundwork for a robust and intelligent future communication infrastructure.

The future generation of mobile networks envision Artificial Intelligence (AI) and the Internet of Things (IoT) as key enabling technologies that will foster the emergence of sophisticated use cases, with the industrial sector being one to benefit the most. This survey reviews related works in this field, with a particular focus on the specific role of network softwarization. Furthermore, the survey delves into their context and trends, categorizing works into several types and comparing them based on their contribution to the advancement of the state of the art. Since our analysis yields a lack of integrated practical implementations and a potential desynchronization with current standards, we finalize our study with a summary of challenges and future research ideas. [ABSTRACT FROM AUTHOR]

Impressive advancements and novel techniques have been witnessed in AI-based Human Intelligent-Things Interaction (HITI) systems. Several technological breakthroughs have contributed to HITI, such as Internet of Things (IoT), deep and edge learning for deducing intelligence, and 6G for ultra-fast and ultralow-latency communication between cyber-physical HITI systems. However, human-AI teaming presents several challenges that are yet to be addressed, despite the many advancements that have been made towards human-AI teaming. Allowing human stakeholders to understand AI's decision-making process is a novel challenge. Artificial Intelligence (AI) needs to adopt diversified human understandable features, such as ethics, non-biases, trustworthiness, explainability, safety guarantee, data privacy, system security, and auditability. While adopting these features, high system performance should be maintained, and transparent processing involved in the 'human intelligent-things teaming' should be conveyed. To this end, we introduce the fusion of four key technologies, namely an ensemble of deep learning, 6G, IoT, and corresponding security/privacy techniques to support HITI. This paper presents a framework that integrates the aforementioned four key technologies to support AI-based Human Intelligent-Things Interaction. Additionally, this paper demonstrates two security applications as proof of the concept, namely intelligent smart city surveillance and handling emergency services. The paper proposes to fuse four key technologies (deep learning, 6G, IoT, and security and privacy techniques) to support Human Intelligent-Things interaction, applying the proposed framework to two security applications (surveillance and emergency handling). In this research paper, we will present a comprehensive review of the existing techniques of fusing security and privacy within future HITI applications. Moreover, we will showcase two security applications as proof of concept that use the fusion of the four key technologies to offer next-generation HITI services, namely intelligent smart city surveillance and handling emergency services. This proposed research outcome is envisioned to democratize the use of AI within smart city surveillance applications. [ABSTRACT FROM AUTHOR]

It is an important feature of the 6G that how to realize everything interconnection through large-scale complex heterogeneous networks based on native artificial intelligence (AI).Thanks to the distinct machine learning architecture of data processing locally, federated learning (FL) is regarded as one of the promising solutions to incorporate distributed AI in 6G scenarios, and has become a critical research direction of 6G.Therefore, the necessity of introducing distributed AI into the future 6G especially for internet of things (IoT) scenarios was analyzed.And then, the potentials of FL in meeting the 6G requirements were discussed, and the state-of-the-arts of FL related technologies such as architecture design, resource utilization, data transmission, privacy protection, and service provided for 6G were investigated.Finally, several key technical challenges and potential valuable research directions for FL-empowered 6G were put forward.

The rapid development of the Internet of Things (IoT), 5G and 6G networks, and advancements in hardware and software devices have enabled mobile devices to be equipped with rich sensors (smartphones, smart bracelets, etc.). This led to devices with powerful sensing capabilities that can easily connect and communicate with other devices. As a result, ordinary users could efficiently complete various sensing data tasks and get paid accordingly, promoting the rapid development of Mobile Crowdsourcing (MCS) technology and effectively facilitating people's daily lives. However, the current MCS is still a long way from being complete and still faces several challenges. Therefore, many researchers have conducted extensive research on MCS in recent years. In this survey, we aim to provide a comprehensive summary of the recent research progress on MCS. Concerning the main research directions and real-world applications of MCS, we review the research in three main areas (task allocation, privacy protection, and incentive mechanism) and real-world application scenarios of MCS. Although there is a lot of existing research on MCS, there are still some crucial problems to be solved. Therefore, we conclude with some lessons learned and suggest future research directions by analyzing the studies covered in this paper. [ABSTRACT FROM AUTHOR]

The advent of 6 G networks will emphasize AR/VR-based tactile internet and touch-enabled services, requiring solutions to address latency and reliability issues in tactile data perception, particularly in edge networking. This article highlights the critical role of graph theory in improving network design for both symmetric and randomly deployed networks, and it emphasizes the importance of network slicing in managing densely connected edge networks. These networks typically consist of numerous edge devices that enable data transfer to higher layers, enhancing connectivity and ensuring network stability within a confined range. The paper compares the accuracy of conventional networks before and after implementing network slicing, with the goal of increasing reliability by dividing the edge network into segments, thereby reducing computation time. The study proposes a method for optimal node segregation and path determination in edge networks using both uniform and random network slicing. Network performance is assessed using metrics such as computation time and node failure rate. The paper concludes that network slicing is the most effective design approach for touch technology interfaces within 6 G and IoT frameworks. [ABSTRACT FROM AUTHOR]

In next-generation networks, including the sixth generation (6G), a large number of computing devices can communicate with ultra-low latency. By implication, 6G capabilities present a massive benefit for the Internet of Things (IoT), considering a wide range of application domains. However, some security concerns in the IoT involving authentication and encryption protocols are currently under investigation. Thus, mechanisms implementing quantum communications in IoT devices have been explored to offer improved security. Algorithmic solutions that enable better quantum key distribution (QKD) selection for authentication and encryption have been developed, but having limited performance considering time requirements. Therefore, a new approach for selecting the best QKD protocol based on a Deep Convolutional Neural Network model, called Tree-CNN, is proposed using the Tanh Exponential Activation Function (TanhExp) that enables IoT devices to handle more secure quantum communications using the 6G network infrastructure. The proposed model is developed, and its performance is compared with classical Convolutional Neural Networks (CNN) and other machine learning methods. The results obtained are superior to the related works, with an Area Under the Curve (AUC) of 99.89% during testing and a time-cost performance of 0.65 s for predicting the best QKD protocol. In addition, we tested our proposal using different transmission distances and three QKD protocols to demonstrate that the prediction and actual results reached similar values. Hence, our proposed model obtained a fast, reliable, and precise solution to solve the challenges of performance and time consumption in selecting the best QKD protocol. [ABSTRACT FROM AUTHOR]

Our society is increasingly dependent on digitization. For example, different types of physical and virtual objects are connected to the Internet of Things, all services are digitized, and the number of connected devices continues to grow, which leads to the exchange of large amounts of data. The current communication network 5G cannot meet the needs of the future. Therefore, the demand for high-speed mobile communications is essential to better prepare for the arrival of new services and emerging applications. Namely, extended reality, holographic communication, sensory internet, human digital twin, smart city and industry, etc. These new use cases are applied in many different areas. For example, health, autonomous transportation, climate, network security, etc. Therefore, the research of the new generation network 6G has begun to bear the limits of 5G and deal with new challenges. This paper conducts a related investigation on the sixth-generation communication network. First, the vision, requirements, and expected application scenarios of the 6G network are introduced. Then, it describes the integration of intelligent architecture and space, air, ground, and sea networks. Subsequently, the most important potential key technologies needed for the future sixth-generation were exposed and analyzed. Finally, the main research activities carried out are introduced. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is the fourth technological revolution in the global information industry after computers, the Internet, and mobile communication networks. It combines radio-frequency identification devices, infrared sensors, global positioning systems, and various other technologies. Information sensing equipment is connected via the Internet, thus forming a vast network. When these physical devices are connected to the Internet, the user terminal can be extended and expanded to exchange information, communicate with anything, and carry out identification, positioning, tracking, monitoring, and triggering of corresponding events on each device in the network. In real life, the IoT has a wide range of applications, covering many fields, such as smart homes, smart logistics, fine agriculture and animal husbandry, national defense, and military. One of the most significant factors in wireless channels is interference, which degrades the system performance. Although the existing QR decomposition-based signal detection method is an emerging topic because of its low complexity, it does not solve the problem of poor detection performance. Therefore, this study proposes a maximumlikelihood-based QR decomposition algorithm. The main idea is to estimate the initial level of detection using the maximumlikelihood principle, and then the other layer is detected using a reliable decision. The optimal candidate is selected from the feedback by deploying the candidate points in an unreliable scenario. Simulation results show that the proposed algorithm effectively reduces the interference and propagation error compared with the algorithms reported in the literature. [ABSTRACT FROM AUTHOR]

The rapid development road map of 6G networks has posed a new set of challenges to both industrial and academic sectors. On the one hand, it needs more disruptive technologies and solutions for addressing the threefold issues including enhanced mobile broadband, massive machine-type communications, and ultra-reliable and low-latency communications. On the other hand, the ever-massive number of mobile users and Internet of Things devices conveys the huge volume of traffic throughout the 6G networks. In this context, caching is one of the most feasible technologies and solutions that does not require any system architecture changes nor costly investments, while significantly improve the system performance, i.e., quality of service and resource efficiency. Ground caching models deployed at macro base stations, small-cell base stations, and mobile devices have been successfully studied and currently extended to the air done by unmanned aerial vehicles (UAVs) to deal with the challenges of 6G networks. This paper provides a comprehensive survey of UAV caching models, techniques, and applications in 6G networks. In particular, we first investigate the entire picture of caching models moving from the ground to the air as well as the related surveys on UAV communications. Then, we introduce a typical UAV caching system and describe how it works in connection with all types of the transceivers, end users, and applications and services (A&Ss). After that, we present the recent advancements and analyses of the UAV caching models and common system performance metrics. Furthermore, the UAV caching with assisted techniques, UAV caching-enabled mechanisms, and UAV caching A&Ss are discussed to demonstrate the role of UAV caching system in 6G networks. Finally, we highlight the ongoing challenges and potential research directions toward UAV caching in 6G networks. [ABSTRACT FROM AUTHOR]

• 5G networks might not meet the growing demands of Internet of Things. • It is necessary to develop 6G networks for intelligent society in the future. • 6G networks have powerful functions but face a lot of technical challenges. • Deep learning could promote the achievement of 6G networks with the help of big data and cloud computing. The fifth generation (5G) networks provide high speed, low latency, and reliable communication services, and have been making great differences in our daily life. However, it might be difficult to meet the growing demands of Internet of Things (IoT) devices based on the existing technologies in the future, so the concept of sixth generation (6G) networks were raised to improve the existing 5G networks and promote the development of intelligent applications furthermore. Therefore, we introduce the characteristics, challenges, and potential technologies of 6G networks, and find that deep learning is potential to overcome these challenges with the help of big data and cloud computing. In addition, Deep learning approaches and potential applications in 6G networks are also discussed. We summarize the important viewpoints and studies, and provide some new ideas for developing 6G networks. [Display omitted] [ABSTRACT FROM AUTHOR]

The forthcoming 6G will attempt to rewrite the communication networks' perspective focusing on a shift in paradigm in the way technologies and services are conceived, integrated and used. In this viewpoint, the Internet of Medical Things (IoMT) represents a merger of medical devices and health applications that are connected through networks, introducing an important change in managing the disease, treatments and diagnosis, reducing costs and faults. In 6G, the edge intelligence moves the innovative abilities from the central cloud to the edge and jointly with the complex systems approach will enable the development of a new category of lightweight applications as microservices. It requires edge intelligence also for the service evaluation in order to introduce the same degree of adaptability. We propose a new evaluation model, called CoKnowEMe (context knowledge evaluation model), by introducing an architectural and analytical scheme, modeled following a complex and dynamical approach, consisting of three inter-operable level and different networked attributes, to quantify the quality of IoMT microservices depending on a changeable context of use. We conduct simulations to display and quantify the structural complex properties and performance statistical estimators. We select and classify suitable attributes through a further detailed procedure in a supplementary information document. [ABSTRACT FROM AUTHOR]