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Graphene, a 2D nanomaterial, has garnered significant attention in recent years due to its exceptional properties, offering immense potential for revolutionizing various technological applications. In the context of the Internet of Things (IoT), which demands seamless connectivity and efficient data processing, graphene's unique attributes have positioned it as a promising candidate to prevail over challenges and optimize IoT systems. This review paper aims to provide a brief sketch of the diverse applications of graphene in IoT, highlighting its contributions to sensors, communication systems, and energy storage devices. Additionally, it discusses potential challenges and prospects for the integration of graphene in the rapidly evolving IoT landscape. [ABSTRACT FROM AUTHOR]

The demand for wireless connectivity has grown tremendously over the last few decades. A new perspective of wireless communication will soon be positioned worldwide. In the years beyond, the millimeter-wave spectrum for the mobile communication infrastructure has improved spectral efficiency. The problem of the technical challenges may be detected, identified, and resolved in different designs as traditional solutions involve the software tricks that prevent the modes of operation that trigger the trouble. In this research, we carry out the proposed Millimetre assisted wave technologies using 6G federated learning and high-performance computing for 6G assisted wireless communication systems for voice data optimization. Millimeter-wave technology is a specific part of the radio frequency spectrum, between 24 and 100 GHz. The outcome of this proposed technique will be voice data optimization in the 6G cellular networks for next-generation networks to improve the 6G assisted networks federated learning from existing cellular network technology generation, established on the tool of Flight stack or Autopilot. The observational results express to assess the performance of the device, we acquire theMillimetre assisted wave technologies and federated learning and high-performance computing at various data transmission rates and modulations, including real-time data transfer up to 96 Gbit/s at 420 GHz, with a low bit error rate and good signal-to-noise ratio. With 18 dBm emitted power at 0.4 THz, this method creates and transmits a signal with a 160 Gbits per second net rate across a 15 m distance. This monolithic dual-DFB PIC-based THz generating technique represents an important advancement toward completely integrated, economically viable, and energy-efficient THz transmitters. [ABSTRACT FROM AUTHOR]

Summary Millimeter Wave (mmWave) communication has emerged as a transformative technology at the forefront of wireless communication. One of the key challenges in harnessing the potential of mmWave technology is overcoming the increased susceptibility to propagation losses and environmental obstacles. To address these challenges, Three‐Dimensional Massive Multiple‐Input Multiple‐Output (3D Massive MIMO) systems have gained traction. The 3D aspect extends this concept by considering the elevation dimension, allowing for enhanced spatial resolution and coverage. Accurate estimation of the channel in 3D Massive MIMO scenarios is particularly challenging because of the complex propagation characteristics of mmWave signals. This paper introduces an efficient‐Aided Graph Neural Network Combining with Hierarchical Residual Learning (DPrGNN‐HrResNetL), designed specifically for beamspace Channel Estimation (CE)in mmWave‐Massive MIMO environments. The proposed model leverages deep priors and GNN mechanisms to enhance the extraction of spatial features, while hierarchical residual connections facilitate effective information flow through the network. DPrGNN enables the model to capture and understand complex spatial relationships among different antenna elements. The incorporation of deep priors provides a mechanism for leveraging prior knowledge about channel characteristics. This enhances the efficiency of the learning process, allowing the model to learn and adapt more effectively. The integration of hierarchical residual connections facilitates effective information flow through the network. This is particularly important for modeling complex dependencies within the beamspace channel data, enhancing the learning capacity of the network. The performance of the DPrGNN‐HrResNetL model is evaluated across a range of Signal‐to‐Noise Ratios (SNRs), utilizing metrics such as Normalized Mean Squared Error (NMSE) to measure the accuracy of the estimation. The outcomes underscore the resilience and efficacy of the DPrGNN‐HrResNetL approach in achieving precise CE within demanding mmWave scenarios. [ABSTRACT FROM AUTHOR]

Abstract Graphene, a 2D nanomaterial, has garnered significant attention in recent years due to its exceptional properties, offering immense potential for revolutionizing various technological applications. In the context of the Internet of Things (IoT), which demands seamless connectivity and efficient data processing, graphene's unique attributes have positioned it as a promising candidate to prevail over challenges and optimize IoT systems. This review paper aims to provide a brief sketch of the diverse applications of graphene in IoT, highlighting its contributions to sensors, communication systems, and energy storage devices. Additionally, it discusses potential challenges and prospects for the integration of graphene in the rapidly evolving IoT landscape.

The upcoming sixth-generation (6G) networks are expected to be more heterogeneous, scalable, reliable, secure and energy-efficient. 6G services and applications will benefit from the fast data transmission rates, global coverage, precise positioning, and widespread intelligence capabilities provided by 6G technology. In this complex scenario, new attack surfaces and vectors will emerge, due to the distributed, disaggregated, dynamic, programmable and open nature of the entire end-to-end 6G infrastructure along with the fragmentation of data, as well as the need for supporting cross-platforms interoperability, making the application of security and trust fundamentally challenging. To securely manage services in the future 6G mobile networks along with the set of functions ruling its management, conceptual work is needed to defining functional blocks supporting a secure end-to-end 6G systems management. This is especially critical in handling specific functionalities, such as network disaggregation, risk and threats business impact, energy-efficiency, self-configuration or dynamic discovery. This paper positions an innovative 6G security services management architecture, which builds on a set of innovative building blocks, leveraging key technologies, such as AI-assisted models and Digital Twin, while enabling a human-centric approach toward an end-to-end security solution. Two canonical workflows along with two illustrative application scenarios are proposed, Light Rail Transit and Extended Reality, to conceptually highlight the proposed architecture impact and its expected benefits when high bandwidth, ultra-low latency, and secure communications are required.

In this paper, we investigate the impact of two pulse shapes on the performance of a real-time free-space optical communication link. The two candidate pulse shapes are the square-root raised cosine and Xia pulse, respectively which are tested as the basis function for multi-band carrier-less amplitude and phase modulation. We first develop a real-time system based on a Xilinx Zynq ZCU102 system-on-chip platform utilising a high-resolution analogue-to-digital-converter. We then generate multi-band carrier-less amplitude and phase modulation formats using it and test the error vector magnitude whilst varying parameters. We emulate the fog environment utilising neutral density filters and evaluate the error performance of the link under increasingly poor visibility conditions. We show that contrary to previous reports, the SRRC pulse shape offers superior performance over the first-order Xia pulse in the FSO environment operating at data rates exceeding 1 Gb/s.

The exponential growth in data traffic from smart devices has led to a need for highly capable wireless networks with faster data transmission rates and improved spectral efficiency. Allocating resources efficiently in a 5G communication system with a huge number of machine type communication (MTC) devices is essential to ensure optimal performance and meet the diverse requirements of different applications. The LTE‐A network offers high‐speed mobile data services and caters to MTC devices and has relatively low data service requirements compared to human‐to‐human (H2H) communications. LTE‐A networks require advanced scheduling schemes to manage the limited spectrum and ensure efficient transmissions. This necessitates effective resource allocation schemes to minimize interference between cells in future networks. To address this issue, a joint delay and energy aware Levy flight Brownian movement‐based dragonfly optimization (DELFBDO)‐based uplink resource allocation scheme for LTE‐A Networks is proposed in this work to optimize energy efficiency, maximize the throughput and reduce the latency. The DELFDO algorithm efficiently organizes packets in both time and frequency domains for H2H and MTC devices, resulting in improved quality of service while minimizing energy consumption. The Simulation results demonstrate that the proposed method increases the energy efficiency by producing the appropriate channel and power assignment for UEs and MTC devices. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed. [ABSTRACT FROM AUTHOR]