The Internet of Things (IoT)-centric concepts like augmented reality, high-resolution video streaming, self-driven cars, smart environment, e-health care, etc have a ubiquitous presence now. These applications require higher data-rates, large bandwidth, increased capacity, low latency and high throughput. In light of these emerging concepts, IoT has revolutionized the world by providing seamless connectivity between heterogeneous networks (HetNets). The eventual aim of IoT is to introduce the plug and play technology providing the end-user, ease of operation, remotely access control and configurability. This paper presents the IoT technology from a bird’s eye view covering its statistical/architectural trends, use cases, challenges and future prospects. The paper also presents a detailed and extensive overview of the emerging 5G-IoT scenario. Fifth Generation (5G) cellular networks provide key enabling technologies for ubiquitous deployment of the IoT technology. These include carrier aggregation, multiple-input multiple-output (MIMO), massive-MIMO (M-MIMO), coordinated multipoint processing (CoMP), device-to-device (D2D) communications, centralized radio access network (CRAN), software-defined wireless sensor networking (SD-WSN), network function virtualization (NFV) and cognitive radios (CRs). This paper presents an exhaustive review for these key enabling technologies and also discusses the new emerging use cases of 5G-IoT driven by the advances in artificial intelligence, machine and deep learning, ongoing 5G initiatives, quality of service (QoS) requirements in 5G and its standardization issues. Finally, the paper discusses challenges in the implementation of 5G-IoT due to high data-rates requiring both cloud-based platforms and IoT devices based edge computing.

To have uninterrupted wireless connectivity, higher throughput, and latency rate down to nanoseconds; future networks will rely heavily on higher frequency bands, yet these signals suffer from attenuation and can be easily blocked by objects. Hence, those signals lose strength over longer distances significantly compared to their low-frequency counterparts. To overcome these constraints, reconfigurable intelligent surfaces (RIS) can increase the wireless network capacity and coverage by consciously altering the wireless propagation environment, which makes them one of the key enablers of the 6G communication networks. The potential benefits of RIS-assisted communication systems can be effectively increased using machine learning (ML) to pave the path towards Wireless 2.0, having a vision of digitization, programmability, and intelligence. This paper reviews recent developments in RIS technology with a special emphasis on tuning techniques, hardware concepts, RIS empowered by artificial intelligence techniques, and applications for next generation of networks. Finally, a viewpoint on the difficulties and potential directions of intelligent metasurfaces are also presented.

We present GumbelRIS, a lightweight convolutional policy network that outputs discrete 2-bit RIS phase optimization in large-scale MIMO systems. It employs Gumbel-Softmax relaxation with a custom capacity-maximizing loss for differentiable training and hard quantization for inference. GumbelRIS outperforms metaheuristics and learning-based baselines across varying RIS sizes and channel conditions. It demonstrates superior capacity (126.757 bps/Hz), fast inference (28 ms), and robustness in NLOS scenarios. The model generalizes well without retraining and is scalable for practical deployment, making it suitable for dynamic and hardware-constrained RIS-aided 5G/6G environments.

There is a growing trend toward employing two-coil and three-coil systems in magnetically coupled resonance wireless power-transfer (MCR-WPT) technology. However, these configurations have limitations at longer transmission distances and are sensitive to load variations. This paper provides a comprehensive review of four-coil WPT systems, focusing on their design, optimization, and applications. The reviewed literature indicates that four-coil configurations generally maintain higher power-transfer efficiency (PTE) over longer transmission distances, exhibit greater tolerance to misalignment, and show reduced sensitivity to load variations. The literature review indicates that while symmetric designs are easier to analyze, asymmetric configurations may provide higher efficiency and extended transmission range. The review also discusses how four-coil technology can be used in medical implants, sensors, and consumer electronics. It also addresses the current challenges of power and compliance in the electric vehicle (EV) industry. Finally, this paper identifies important areas of research that need to be addressed. Future research should aim to optimize power and efficiency together, explore multi-receiver systems for public charging, and investigate the potential of four-coil technology for Electric Micromobility Vehicles (EMVs).

Reconfigurable Intelligent Surfaces (RIS) promise large array gains but raise a critical challenge: configuring hundreds of discrete phase shifters in real time under stringent latency and control overhead constraints. This paper proposes Turbo-FQCA, a quantization-aware, coordinate-ascent solver designed for single 5G-NR slot feasibility. Turbo-FQCA integrates a rank-1 proxy objective, Gauss–Southwell element ordering, warm-start initialization, and compact delta payload signaling to deliver near-optimal spectral efficiency within tight update and timing budgets. The proposed method achieves millisecond-level runtime through rank-1 updates and early stopping, while bit-efficient payloads scale only logarithmically with RIS size. Extensive simulations confirm three key outcomes: i) Turbo-FQCA consistently reaches $\varepsilon $ –stationarity within a dozen flips, with nearly one-third of runs requiring no updates from warm start; ii) the achieved spectral efficiency ( $\ge 120$ bps/Hz) remains robust across number of RIS elements, quantization levels and update toggles; and iii) control payloads remain in the range of a few bytes per slot, making signaling overhead negligible. Accordingly, Turbo-FQCA is not positioned as a classically optimal solver, but as a system-aware RIS control strategy that explicitly balances spectral efficiency, latency, and control signaling overhead under practical 5G-NR constraints.