This paper reviews the state-of-the art technologies and techniques for integrating satellite and terrestrial networks within a 5G and Beyond Networks (5GBYNs). It highlights key limitations in existing architectures, particularly in addressing interoperability, resilience, and Quality of Service (QoS) for real-time applications. In response, this work proposes a novel Software-Defined Networking (SDN)-based framework for reliable satellite–terrestrial integration. The proposed framework leverages intelligent traffic steering and dynamic access network selection to optimise real-time communications. By addressing gaps in the literature with a distributed SDN control approach spanning terrestrial and space domains, the framework enhances resilience against disruptions, such as natural disasters, while maintaining low latency and jitter. Future research directions are outlined to refine the design and explore its application in 6G systems.

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

This research presents the development of an android-based, voice-controlled autonomous robotic vehicle. This article was developed in a way that the robotic vehicle was controlled using voice commands. An android application combined with an android microcontroller was used to achieve this task. The connection between the android app and the autonomous vehicle was facilitated using Bluetooth technology. The vehicle was controlled with either the aid of the buttons on the app, or by spoken commands from the user. The movement of the vehicle was achieved by using four DC motors connected with the microcontroller at the receiver side. The commands from the app were converted into digital signals using the Bluetooth RF transmitter within a specific range (of around 100 m) of the autonomous vehicle. At the receiver end, the data gets decoded by the receiver and is fed to the microcontroller which moves the DC motors of the vehicle for navigation. The voice-controlled autonomous robotic vehicle performed navigational tasks by listening to the commands of the user. This was achieved by converting voice commands into text strings which are readable by the Arduino microcontroller on the android app in order to control the navigation of the robot. The vehicle was tested under different conditions and was observed to perform better using this technique and also the results were satisfactory when compared with other previous research that has been conducted in this area.

Device-to-Device (D2D) communication plays a vital role in enhancing spectral efficiency and data rates in next-generation wireless networks, but interference management and optimal power control remain key challenges. This study aims to address these gaps by developing a modified Power Control Scheme for D2D communication (mPCS-D2D) using a Hierarchical Clustering Algorithm (HCA) to improve throughput while minimizing interference. The scheme combines proximity-based clustering for general D2D users and social relationship-based clustering for mmWave D2D communication. Simulations were conducted to evaluate the throughput performance of mPCS-D2D under varying D2D transmit power levels, user densities, and inter-user distances. Results showed that the proposed scheme significantly outperformed the baseline PCS-D2D model across multiple scenarios. At 10 m and 15 m distances with a pathloss exponent of 4.5, mPCS-D2D improved throughput by 5.15% and 4.42%, respectively. Under varying user densities and pathloss exponents (3.5 and 4.5), throughput gains ranged from 4.33% to 4.77%, while across 10 m, 15 m, and 20 m distances, it achieved improvements of 4.13% to 5.53%. These findings demonstrate that the proposed mPCS-D2D scheme effectively enhances data transmission rates under diverse network conditions. The study concludes that integrating hierarchical clustering into power control mechanisms can significantly improve D2D communication efficiency. The proposed method offers practical implications for designing scalable, interference-aware D2D systems in future wireless networks.