Abstract: Unmanned Aerial Vehicles (UAVs) are essential in a variety of fields, including emergency rescue, security patrol, and agricultural planting. UAVs present benefits like allowing ground communications even in situations where connectivity is restricted by physical barriers. Conversely, they expand the area that can be attacked. For example, physical drone attacks give the attacker credentials to introduce fake data into the Internet of Things (IoT) network, compromising user safety as well as security. Authentication is essential in this situation to ensure security. One major obstacle to UAV communications is privacy and security concerns. UAVs can potentially give an increase in security concerns due to their open-access communication environments. These concerns may include threats to authentication and the potential for location and other sensitive data to be leaked to unauthorized parties. However, security against drone attacks cannot be ensured by the authentication schemes that are currently in place. Because of its performance and security, elliptic curve cryptography (ECC) is frequently used in the design of authentication protocols. So, in this paper, an elliptic curve cryptography-based scheme is presented. ECC is particularly preferred because it can provide complete safety with shorter key lengths, which is suitable for drones with limited resources. The mathematical properties of elliptic curves strengthen the algorithm's resilience to various cryptographic attacks, ensuring a high level of security.

Abstract: Large Intelligent Surfaces (LIS) have emerged as promising technology for enhancing spectral efficiency and communication capacity in the Sixth Generation of Cellular Communications (6G). Low complexity receiver architectures for LIS rely on Maximum Ratio Combining (MRC) and Equal Gain Combining (EGC) receivers, often complemented by iterative detection techniques for interference mitigation. In this work, we propose a novel approach where a neural network replaces iterative interference cancellation, learning to estimate the transmitted signals directly from the received data, mitigating interference without requiring iterative cancellation. Moreover, this also eliminates the need for channel matrix inversion at each frequency component, as required for Zero Forcing (ZF) and Minimum Mean Squared Error (MMSE) receivers, reducing computational complexity while still achieving a good performance improvement. The neural network parameters were optimized to balance performance and computational cost.

Abstract: The increasing prevalence of encryption enhances network traffic confidentiality and integrity but complicates network management and security by obscuring traffic flows. This challenge makes detecting cyberattacks and enforcing policies increasingly difficult. Encrypted Traffic Intelligence (ETI), particularly network traffic classification (NTC), offers solutions using machine learning techniques. However, practical implementation remains challenging due to the inherent complexity of network environments, where traffic feature distributions vary because of factors such as network topology and delay. This variability undermines the robustness of classifiers trained on static datasets. Moreover, dynamic environments increase the likelihood of encountering unknown traffic, where inaccurate identification can lead to high false positive rates, unacceptable in critical applications like billing and cybersecurity. To address these challenges, we propose e-FlowPrint, an enhanced FlowPrint-based open-set recognition (OSR) classifier designed for robust unknown traffic detection. Inspired by uncertainty sampling techniques in active learning, we introduce two novel methods: Probability Anomaly Recognition (PAR) and Entropy-Based Uniformity Analysis (EnUniA). PAR utilizes the disparity between the highest and second-highest classification probabilities; a small disparity indicates uncertainty, suggesting that the sample is likely to be unknown. EnUniA calculates entropy values across all classes, where high entropy indicates a uniform distribution of probabilities, further increasing the likelihood of the sample being unknown. We evaluate the proposed model using the ITC-Net-blend-60 dataset across diverse real-world network environments and conduct long-term performance assessments through three-year network condition simulations. Our results demonstrate that e-FlowPrint improves FlowPrint's unknown traffic detection performance by approximately 30%. Additionally, it enhances overall classifier performance by 2% in varying network environments.

Abstract: New positioning solutions are a key factor in the pursuit of autonomous driving. GNSS is the most common method, however traditional systems may have high position errors due to multipath signals. In this work, we present a GNSS adaptive antenna with beamforming capabilities that can apply spatial filtering to mitigate interferences and improve satellite connectivity, reducing the positioning error. The array, developed with off-the-shelf GNSS antennas, was used to demonstrate the improvement of the gain, leading to better signal to noise ratio, comparatively to the traditional GNSS antenna, while maintaining circular polarization in all directions. A digital beamforming solution was employed with the software defined platform based on a Xilinx ZCU216. The full system performance was tested in an anechoic chamber, where good results were obtained in both single and multibeam scenarios, with great agreement between the simulated and measured data. The results presented in this paper validate the proposed FPGA-based array and beamforming development platform, paving the way for the seamless and rapid design and test of numerous antenna array geometries with up to 16 channels and beamforming algorithms, including adaptive ones. This powerful and versatile tool will accelerate research on performance improvement of GNSS reception.

Abstract: The rise of civilian drone technology is reshaping smart city development, offering new capabilities for urban management, service delivery, and surveillance. However, effective integration of drones into the urban environment requires carefully crafted public policies—especially in regions like the Gulf Cooperation Council (GCC) countries and Egypt, where security and privacy concerns are paramount. This paper presents a policy framework for **network-integrated** civilian drones in smart cities, emphasizing strategies that facilitate innovation while maintaining public safety and national security. The framework is informed by an analysis of current regulations and initiatives in GCC states and Egypt, as well as global best practices in unmanned aerial systems (UAS) governance. Key components include regulatory reform to permit controlled drone use, technological infrastructure for airspace management via telecommunications networks, and safeguards for privacy and cybersecurity. The results highlight how GCC cities can leverage drones for urban services under unified guidelines, and how Egypt might modernize its restrictive stance to reap smart city benefits. We discuss the implications for stakeholders—ranging from city planners to civil aviation authorities—and offer recommendations for implementing the framework. Our conclusions underscore that **network integration** of drones, supported by robust policy, can enable their safe and productive use in smart cities across the Middle East.

Abstract: Rural communities in India face a range of socio-economic and technological challenges that hinder their access to essential services, including digital platforms, healthcare, and education. This review paper explores these issues through a case study of Salempur village, where residents encounter specific challenges such as susceptibility to digital scams, limited healthcare access, lack of technological awareness, and inadequate network connectivity. Based on interviews and observations from the community, the paper identifies key areas needing intervention: enhanced digital literacy to prevent financial scams, improved healthcare infrastructure, investment in educational resources, and network upgrades. Our findings underscore the need for targeted policies and programs to bridge the digital divide and improve the quality of life in rural areas. Recommendations include implementing digital literacy workshops, expanding telemedicine services, enhancing rural education with technological tools, and investing in telecommunications infrastructure. This study provides insights into the pressing needs of rural communities and suggests strategies for fostering sustainable development.

Abstract: The increasing adoption of Internet of Things (IoT) devices, coupled with the transition to Industry 4.0, has highlighted the necessity of evaluating LoRaWAN performance in complex geographical environments. This study presents an automated system for assessing LoRaWAN coverage in mountainous regions. The system addresses critical performance parameters such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), transmission power, velocity, and data transmission intervals. The proposed methodology integrates qualitative and quantitative approaches in three phases: Analysis, Implementation, and Evaluation. The system is deployed in urban and rural regions of southern Ecuador, where real-time data acquisition enables both uplink and downlink performance assessment. The results demonstrate the feasibility of automating LoRaWAN coverage evaluation, minimizing manual intervention, and enhancing network monitoring efficiency. The findings support the optimization of IoT deployments in challenging terrains, facilitating improved communication reliability and scalability.

Abstract: Digital twin (DT) will revolute network autonomy. Current studies have promoted DT-native 6G network by deeply integrate DT into mobile network architectures to improve the timeless of physical-digital synchronization and network optimizations. However, DT has mainly acted as just a tool for network autonomy, leading a gap towards the ultimate goal of network self-evolution. This paper analyzes the future direction of the DT-native network. Specifically, the proposed architecture introduces a key concept called "future shots", which gives accurate network predictions under different time scales of self-evolution strategies for various network elements. To realize the future shots, we propose a long-term hierarchical convolutional graph attention model for cost-effectively network predictions, a conditional hierarchical graph neural network for strategy generations, and methods for efficient small-large scale interactions. The architecture is expected to facilitate high-level network autonomy for 6G networks.

Abstract: Internet Protocol Television (IPTV) is a transformative approach to delivering audio and video services through high-speed Internet networks, enabling direct access to television content via home computers or set-top boxes. Despite its promising advantages, including flexibility, interactivity, and bundled services such as triple play (voice, Internet, and TV) and quadruple play (adding mobile services), IPTV is still in its development phase. Key challenges include achieving a Quality of Service (QoS) comparable to traditional broadcasters, addressing limited bandwidth, and overcoming a lack of standardization among service providers. This paper explores the technical, operational, and consumer-oriented aspects of IPTV. It discusses data compression techniques, protocols like IGMP and RTSP, and the role of advanced codecs like H.264 in ensuring efficient data transmission. The study also examines the distinctions between IPTV and open-network Internet TV, the importance of security and privacy, and the emergence of new business opportunities through targeted advertising and interactive services. Although IPTV is unlikely to completely replace traditional broadcasting, it is poised to play an important role in shaping the future of television by offering personalized, secure, and scalable viewing experiences.

Abstract:

This paper focuses on relay semantic communication systems and deeply investigates their outage probability performance under different conditions. First, a three-point, two-link communication transmission model is constructed, which includes the source node, relay node, and destination node. The relay operates in half-duplex mode and is based on the DeepSC communication transmission model, with semantic and channel encoding/decoding implemented at both the source and relay nodes. Next, depending on the computational capabilities of the destination node, background knowledge, and channel conditions, the channel transmission strategies are classified into four scenarios: 1) Both the SR and RD links use semantic transmission, with the source and destination nodes having either the same or different background knowledge; 2) Both the SR and RD links use semantic transmission, but the signal is impaired, preventing the destination node from decoding; 3) The SR link uses semantic transmission, while the RD link performs bit transmission. For each scenario, the channel capacity formula and outage probability expressions are derived. In calculating the outage probability, the results are obtained by analyzing and deriving the distributions and conditions of relevant variables. Finally, Monte Carlo simulations are used to validate the theoretical analysis. The research results provide a theoretical basis for comprehensively understanding the performance of relay semantic communication systems and are of significant importance for advancing semantic communication technology.

Abstract: In a multilingual country like India Language barrier between communities present significant challenges for effective cross-lingual communication. The development of robust translation systems that handle both text and speech across these languages is essential for fostering inclusivity and accessibility. In order to address these challenges this study aims to the exploration of multilingual speech and text translation using the model, a state-of-the-art transformer-based model tailored for Indian languages. The proposed application leverages IndicTrans2’s capabilities to seamlessly translate text and spoken content between 22 Indian languages, enhancing accessibility and inclusivity. This solution is specifically designed to bridge communication barriers for speakers of different Indian languages, offering real-time translations that support both speech-to-text and text-to-speech functionalities and make our code available on GitHub. The system architecture, underlying model mechanics, training methodologies, and evaluation metrics are thoroughly discussed in paper. Future work aims to expand the model’s capabilities by incorporating additional dialects and optimizing real-time speech translation performance.

Abstract: The rapid advancement of technology has led to an increasing demand for enhanced communication systems, particularly in the realm of cellular networks. With diverse applications such as real-time video streaming, online gaming, critical operations, and Internet-of-Things (IoT) services relying more on cellular connectivity, optimizing the cellular networks to meet evolving requirements while mitigating the associated power consumption challenges is crucial. This paper provides a comprehensive overview of initiatives undertaken by industry, academia, and researchers to reduce the power consumption of cellular network systems. Special emphasis is placed on emerging technologies like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Cloud-Radio Access Network (C-RAN), which hold promise for reshaping cellular infrastructure. Additionally, the paper delves into the convergence challenges and solutions associated with SDN, NFV, and C-RAN. The paper proposes a novel cellular architecture grounded in SDN, NFV, and C-RAN paradigms. This proposed framework offers a blueprint for developing energy-efficient cellular networks capable of meeting the diverse demands of modern communication applications and able to reduce power consumption by approximately 40% to 50% with careful placement of virtual network functions.

Abstract: Free-space optical communications have emerged as a powerful solution for inter-satellite links, playing a crucial role in next-generation satellite networks. This paper introduces a comprehensive model that enables the dynamic evaluation of optical power requirements for realistic low Earth orbit satellite constellations, throughout the orbital period. Our approach incorporates the constellation architecture, link budget analysis and optical transceiver design, to accurately estimate the power required for sustaining connectivity for both intra- and inter-orbit links. We apply the model considering Walker delta-type constellations of varying densities. We show that in dense constellations, even at high data rates, the required transmission power can be low enough to mitigate the need of optical amplification. Dynamically estimating the power requirements is vital when evaluating energy savings in adaptive scenarios where terminals adaptively change the emitted power depending on link status. Our model is implemented in Python and is openly available under an open-source license. It can be easily adapted to various alternative constellation configurations.

Abstract: This paper presents the design and analysis of a high-performance microstrip patch antenna (MPA) operating at 28 GHz, along with a multiple-input multiple-output (MIMO) configuration optimized for beyond 5G and IoT applications. The proposed MPA incorporates a rectangular patch with two symmetrical L-shaped slots and a central rectangular slot, enhancing impedance matching and radiation characteristics. Fabricated using Rogers RT5880 substrate with a dielectric constant (εr) of 2.2 and a loss tangent of 0.0009, the antenna achieves a return loss of -33.04 dB, ensuring minimal signal reflection and efficient power transfer. The simulated gain reaches 10.11 dBi, and the voltage standing wave ratio (VSWR) is measured at 1.04, indicating excellent performance. To further enhance system efficiency, a MIMO configuration is implemented, improving parameters such as return loss, isolation, and diversity gain. The MIMO system achieves gains of 10.2, 9.91, 10.2, and 9.84 dBi for individual elements, with a diversity gain of 10 dB, ensuring robust signal performance and increased data throughput. The design was fabricated and experimentally validated using a vector network analyzer (VNA), demonstrating strong agreement between simulations and measurements. These results confirm the antenna’s suitability for high-speed wireless communication in next-generation IoT networks.

Abstract:

As the discussions on sixth generation (6G) wireless networks progress at a rapid pace, various approaches have emerged over the last years regarding new architectural concepts that can support the 6G vision. Therefore, the goal of this work is to highlight the most important technological efforts in relation to the definition of a 6G architectural concept. To this end, the primary challenges are firstly described, which can be viewed as the driving forces for the 6G architectural standardization. Afterwards, novel technological approaches are discussed to support the 6G concept, such as the introduction of artificial intelligence and machine learning for resource optimization and threat mitigation, cell-free deployments and novel physical layer techniques to leverage high data rates, open access protocols for flexible resource integration, security and privacy protection in the 6G era, as well as the digital twin concept. Finally, recent research efforts are analyzed with emphasis on the combination of the aforementioned aspects towards a unified 6G architectural approach. To this end, limitations and open issues are highlighted as well.

Abstract: This study explores the application of wireless wearable devices within the emerging domain of biomechanical feedback systems for sports and rehabilitation. A critical aspect of these systems is the need for real-time operation, where wearable devices must execute multiple processes concurrently while ensuring specific tasks are performed within precise time constraints. To address this challenge, we developed a specialized, lightweight periodic scheduler for microcontrollers. Extensive testing under various conditions demonstrated that sensor sampling frequencies of 650 Hz and 1750 Hz are achievable when utilizing 1 and 26 sensor samples per packet, respectively. Receiver delays were observed to be a few milliseconds or more, depending on the application scenario. These findings offer practical guidelines for developers and practitioners working with real-time biomechanical feedback systems. By optimizing sensor sampling frequencies and packet configurations, our approach enables more responsive and accurate feedback for athletes and patients, improving the reliability of motion analysis, rehabilitation monitoring, and training assessments. Additionally, we outline the limitations of such systems in terms of transmission delays and jitter, providing insights into their feasibility for different real-world applications.

Abstract: Unmanned aerial vehicles (UAVs) have emerged as a promising solution to enhance communication networks, especially in scenarios where traditional infrastructure is impractical or unavailable. However, the reliance on line-of-sight (LoS) channels in UAV-assisted relay networks introduces significant security vulnerabilities. This paper proposes a novel framework that jointly optimizes transmit power and UAV trajectory to achieve secure communications in such networks. To enhance system performance against environmental uncertainties, we employ a model predictive control (MPC)-based approach, which allows for real-time adaptive control of the UAV’s trajectory by considering future states and disturbances. This approach significantly improves the network’s resilience to dynamic environments and potential eavesdropping threats. Simulation results show that the proposed joint optimization of power allocation and trajectory design not only enhances communication security but also demonstrates the effectiveness of the MPC framework in real-time trajectory tracking, ensuring robust performance under varying environmental conditions.

Abstract: Smart greenhouses offer crucial solutions for reducing atmospheric impact and resource waste. However, two fundamental challenges persist in their implementation: massive energy consumption and a high level of human intervention, particularly for sensor battery replacement or recharging. Unfortunately, sensors are indispensable in greenhouses and agriculture, such as for monitoring environmental parameters for air quality assessment. Therefore, while sensors cannot be eliminated, it is essential to optimize their energy consumption. This work introduces an energy-efficient monitoring system for smart greenhouses, aiming to reduce the energy consumption of individual sensors and enhance system sustainability. The study focuses on optimizing the sampling intervals of commonly monitored environmental parameters to minimize sensor energy usage while maintaining data acquisition accuracy adequate for the intended purpose. Additionally, to further reduce battery energy draw, an energy harvesting system using solar panels has been implemented. In conclusion, adopting an optimal sampling strategy for each parameter significantly reduces energy consumption compared to fixed, inefficient sampling intervals commonly used in commercial weather stations. Furthermore, by employing an energy harvesting system for each sensor, leveraging the light emitted by greenhouse lamps and external sources ensures the autonomy of sensors within the greenhouse, thereby minimizing the need for human intervention for battery replacement and recharging.

Abstract:

The exponential growth of the Internet of Things (IoT) has revolutionized connectivity, generating vast amounts of data, and introducing significant challenges in security and privacy. Traditional security protocols often fail to meet the demands of IoT environments due to the limited computational and energy capacities of devices, as well as the need for scalability and adaptability in dynamic networks. This study evaluates IoT application protocols to identify those lacking native key-exchange mechanisms and examines the integration of Owl, a password-authenticated key-exchange protocol, to improve security from the initial stages of communication. By focusing on lightweight and resource-efficient designs, this research demonstrates how Owl addresses critical security gaps, reinforces authentication, and ensures robust session establishment in IoT systems. The findings provide a road-map for incorporating Owl into IoT architectures, offering practical solutions to achieve seamless, scalable, and secure communication in constrained environments.

Abstract:

Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure the compatibility with existing wireless system. Specifically, a series of physical-layer processing modules in the end-to-end transmission are designed. Additionally, we develop a semantic communication simulator to implement and evaluate this framework. To optimize the performance of this framework, we introduce a novel physical-layer metric, termed Integer Error Rate (IER), which provides a more suitable evaluation criterion for semantic communication compared to the conventional Bit Error Rate (BER). On the basis of IER, a minimum Manhattan distance constellation mapping scheme is proposed, which can improve the transmission quality of semantic communication under the same BER condition. Furthermore, we propose a hybrid Joint Source-Channel Coding (JSCC) and Separate Source-Channel Coding (SSCC) transmission scheme. This scheme decouples the semantic quantization output from the modulation order by segmenting the bits to be transmitted. Simulation results demonstrate that the hybrid JSCC/SSCC transmission scheme can improve the semantic performance such as Peak Signal-to-Noise Ratio (PSNR) at the low Signal-to-Noise Ratio (SNR) environment while reducing bandwidth usage by up to 50% compared to the benchmark scheme.