The Hypersurface is a novel architecture for the realization of Reconfigurable Intelligent Metasurfaces which brings software defined electromagnetic behavior by embedding a network of nano controllers on the metasurface for software driven reconfiguration of each of the unit cells. The considered controller network, severely constrained by hardware limitations, is characterized by a Manhattan-like topology and clock-less unidirectional channels between nodes rendering the use of traditional Network on Chip fault-tolerant (FT) routing mechanisms infeasible. Thus, in this work we develop and evaluate a robust, deterministic FT, deadlock- and livelock-free routing protocol that is able to deliver reliably, software directives to connected network controllers in the presence of multiple failing nodes. We adopt the notion of faulty blocks (FBs) where faults are contained in convex regions surrounded by healthy boundary nodes. We assess the protocol in terms of routing efficiency and the effects of node victimization for FB formation on the electromagnetic performance of the HSF under the presence of two distribution fault models. Our results show that the algorithm has little to no effect on the hop count of routed packets and its effect on the electromagnetic performance of the surface can be tolerated for up to 8% of faulty nodes.

A UAV-based wireless network is made up of UAVs working together to upload data to the internet. During crisis scenarios like earthquakes or floods, UAVs are highly used in transmitting valuable health-related data. Basic physiological life-saving parameters like blood pressure, temperature, and respiration rate can be tracked by sensors built into UAVs. One of the most reliable routing techniques in UAVs is clustering, where the network is separated into clusters, each of which has a cluster head (CH) and cluster members (CM). Having a damaged CH results in a disconnected cluster and loss of data. Several routing schemes target reducing delays, but ensuring a functional CH is a topic rarely discussed. Our proposed scheme targets both reducing delays and ensuring a functional CH through multiple redundancy and node type variation to ensure health data transmission. This paper suggests an enhanced redundant routing scheme based on four weighted parameters, which are the distance stability, the rewarding stability, the velocity stability, and the energy stability. The network will be formed of UAVs and vehicles to maximize the likelihood of a functional CH. The weighted calculation will result in a customized metric utilized in cluster formation. To guarantee connection stability, whenever the primary CH is not operating, a redundant CH handles the routing responsibilities. The proposed protocol was simulated using MATLAB. The results obtained demonstrated that our proposed scheme is promising in optimizing the cluster formation while reducing total delays and ensuring a functional CH.

Adapting mobile networks to the diverse and evolving demands of 5G and forthcoming 6G technologies requires flexible, efficient, and dynamic strategies—especially in ultra-dense environments and infrastructure-limited areas. This paper proposes a robust two-stage Machine Learning (ML) heuristic framework to dynamically select a group of User Equipment (UEs) to act as Virtual Base Stations (UE-VBSs) for network augmentation. In the first stage, Self-Organizing Maps (SOM) are employed to cluster UEs based on their spatial characteristics while preserving topological relationships, achieving a silhouette score of 0.64—a 30% improvement over conventional methods such as K-Means (0.46) and Mean-Shift (0.43). In the second stage, a Random Forest classifier enhanced via the Synthetic Minority Over-sampling Technique (SMOTE) attains an average accuracy of 97% and an F1-Score of 0.88 in identifying eligible devices to become UE-VBSs, outperforming recent frameworks that typically report accuracies ranging between 85% and 92%.Comparative evaluation results demonstrate that our two-stage ML heuristic framework not only improves clustering accuracy and UE-VBS classification but also consistently outperforms state-of-the-art clustering methods in terms of network sum rate, power consumption, and scalability. Specifically, across all device densities (i.e., 200, 400, 600, 800, and 1000 UEs), our approach achieves the highest sum rate—peaking at nearly 1.8 billion bps (or 1.8 Gbps) at 1000 UEs—thus surpassing methods such as Affinity Propagation and Grid-based Clustering. Furthermore, by intelligently selecting UE-VBSs, the framework significantly reduces power consumption by effectively minimizing redundant transmissions and interference, making it an energy-efficient solution for large-scale 5G networks. Although the complexity of SOM clustering and Random Forest classification introduces higher computational overhead, the resulting improvements in throughput, energy efficiency, and scalability justify this cost, making it a robust and practical solution for real-world deployments. Validated on both synthetic and real-world datasets, our findings underscore the efficacy, scalability, and high impact of employing robust unsupervised and ensemble learning techniques for dynamic network optimization in next-generation architectures, delivering up to a five-fold increase in network sum rate under high-density conditions compared to state-of-the-art approaches like grid-assisted clustering and affinity propagation.

A swarm of unmanned aerial vehicles (UAVs) is a wireless network made up of UAVs cooperating to upload data to the internet. During critical scenarios like earthquakes, one or more UAV could get damaged. Clustering is one of the most dependable routing systems since the network is divided into clusters with a cluster head (CH) and cluster members (CM). Having a damaged CH results in a disconnected cluster and loss of valuable data. Ensuring a functional CH is a crucial topic that is rarely covered in routing schemes. Our proposed scheme targets ensuring a functional CH through multiple redundancies while redeucing the delays. This paper suggests an enhanced redundant routing scheme based on three weighted parameters, which are the distance stability, the rewarding stability, and the energy stability. The weighted calculation will result in a new metric named cluster index which will be utilized in CH and CMs selection. To guarantee connection stability, whenever the primary CH is not operating, a redundant CH handles the routing responsibilities. The proposed protocol was simulated using MATLAB. The results obtained demonstrated that our proposed scheme is promising in optimizing the cluster formation while reducing total delays and ensuring a functional CH and data delivery.

User Equipment as a Virtual Base Station (UE-VBS) computing paradigm represents a significant advancement in wireless networking. It enables User Equipment (UE) to form: i) Virtual Base Stations (VBSs) by dynamically integrating Cluster Heads (referred to as UE-VBSCH), or Virtual Relays (referred to as UE-VBSRL), in the far-edge domain. This research focuses on enhancing the Quality of Service (QoS) (and thereby improving user experience) in networks supported by UE-VBS computing through outage prediction, network optimization, and advanced wireless techniques. In addition, the paper presents a detailed outage probability analysis and explores the trade-off between efficiency and reliability (namely, spectral and energy efficiency and link-level reliability (outage probability)), which are core contributions of this work. For a representative urban density of 2 UEs per m(2), a single-hop UE-VBS slice lowers the outage probability from 0.78 to 0.23, raises the peak area-spectral efficiency to 4.3 bits-1 Hz(-1) ( approximate to 4.8 x the baseline), and delivers an energy efficiency of 2.4 x 10(5) bit J(-1) ( approximate to 4.6 x improvement). These concrete figures substantiate the claimed gains and illustrate how UE-VBS computing simultaneously improves efficiency and reliability. Specifically, it provides a thorough examination of UE-VBS computing's capacity to enhance service quality, reduce congestion, and promote energy efficiency. Also, it empirically confirms UE-VBS computing's superior performance, including mitigating coverage gaps coverage gaps are localized areas inside a nominally covered cell where received SINR falls below the outage threshold because of shadowing or cell-edge distance), optimizing network traffic, and reducing battery consumption compared to traditional networks/non-UE-VBS computing-supported networks. Enhanced QoS aims to minimize the challenges associated with restricted network coverage, ensuring consistent data transmission rates and improving overall user satisfaction. The potential exists for adopting effective network traffic offloading to mitigate the heavy traffic on primary base stations known as Next Generation Node B (gNodeB). Consequently, this can result in enhanced spectrum utilization and heightened data throughput. Leveraging UE-VBS computing also contributes to power conservation and fosters sustainability.

Ultra-reliable low-latency communication (URLLC) refers to cellular applications in fifth and sixth-generation (5G/6G) networks with specific latency, reliability, and availability demands. Most of the reported 5G/6G applications are focused on URLLC, which necessitates a latency of milliseconds and very high dependability for transmitted data. These systems encounter several obstacles since conventional networks cannot fulfill such demands. According to the standards of the 3rd generation partnership project URLLC, it is predicted that the dependability of a single transmission of a 32-byte packet would be no less than 99.999%, and the latency will not exceed 1 ms. The exceptional degree of dependability and minimal delay will result in the emergence of many novel applications, including smart grids, industrial automation, and intelligent transport systems. This review discusses several methods for maximizing capacity in URLLC, focusing on resource allocation strategies, multi-access approaches, and beamforming with massive MIMO. Furthermore, it explores the requirements and constraints of URLLC and the role of AI/ML in URLLC. Finally, this study examines possible future research areas and obstacles to achieving the URLLC standards.

Due to their minimal environmental impact, green energy sources like wind turbines and solar panels are increasingly utilized in power systems. However, the power they generate is highly variable, leading to unpredictable fluctuations in power supply. Additionally, advanced smart functions in consumer devices and their unpredictable usage patterns contribute to similar fluctuations in power consumption. These fluctuations present a significant challenge to the stability and quality of the power grid, creating a complex issue of power imbalance that becomes harder to manage. Innovative management and control approaches are necessary to address these challenges and thus support the shift to sustainable energy sources. Artificial intelligence (AI) techniques are increasingly proposed as promising solutions, albeit mostly implemented as isolated solutions within centralized power control systems. To effectively manage the complex and often large scale power systems, this paper advocates the use of a Distributed AI (DAI) framework as imperative in enhancing their agility and stability. An illustrative Nano-Grid example (including the potential use of battery sources in extreme scenarios) is adopted to demonstrate the framework’s utility, and a number of power control strategies to safeguard the power system against the variability of both power generators and loads are theoretically formulated and then realized within the proposed framework. Linear Programming, Ant Colony Optimization, Genetic Algorithms, and Particle Swarm Optimization techniques are experimented with, and through simulations, the utility of the DAI framework is demonstrated. The findings underscore the effectiveness and potential benefits of the proposed framework in ensuring the safe and effective operation of power systems with the use of particle swarm optimization amid fluctuating energy scenarios with a small to large number of devices in the nano-grid.

In the rapidly developing field of wireless communication, the control of beams in Reconfigurable Intelligent Surfaces (RISs) has emerged as a promising element beyond 5G wireless communication systems. Due to their distinctive reflecting elements, Reconfigurable Intelligent Surface (RIS) is essential in several operations, including beamforming and beam steering. However, the optimization of these functions necessitates complex solutions. In this study, the authors introduce the Feedback DNN strategy, which combines the Feedback Neural Network and Deep Neural Network techniques specifically designed for channel estimation. This methodology utilizes deep neural networks to provide the RIS and user equipment communication path, enabling improved beamforming and steering capabilities. This study highlights the incorporation of machine learning (ML) within the field of communication engineering, intending to enhance the reliability and effectiveness of wireless communication systems. The contributions encompass a novel methodology for managing RIS beams, sophisticated approaches for channel estimates, optimization of beam operations, and the potential to enhance the performance of wireless systems by utilizing RISs via a Feedback DNN (called DeepRISBeam). The proposed approach is compared against other state-of-the-art ML approaches regarding their training accuracy. At the same time, it evaluated Bit Error Rate performance in high- and low-mobility vehicular communication scenarios.

Swarm of UAVs (S-UAVs) refers to an assembly of unmanned aerial vehicles (UAVs) working together to accomplish prearranged missions. In emergency scenarios, such as a fire, any UAV is susceptible to damage. Among the routing algorithms that S-UAVs utilize the most frequently is clustering, where UAVs are divided into clusters. Each cluster consists of a cluster head (CH) and cluster members (CM). The selection of the CH is a topic of continuous research because of its crucial significance in intercluster communication. We propose a clustered weighted method with dynamic weight modification and redundancy to ensure end-to-end communication despite non-functional CH. Interspace, speed, and performance indicators are combined into a weighted metric that is used to select the CH, redundant CHs, and CMs. The suggested technique optimizes UAV role selection by dynamically and autonomously adjusting the weights. Based on the results of the executed simulation, this is a promising strategy that minimizes data loss in an emergency situation and minimizes delay.

Although continuous advances in theoretical modelling of Molecular Communications (MC) are observed, there is still an insuperable gap between theory and experimental testbeds, especially at the microscale. In this paper, the development of the first testbed incorporating engineered yeast cells is reported. Different from the existing literature, eukaryotic yeast cells are considered for both the sender and the receiver, with α-factor molecules facilitating the information transfer. The use of such cells is motivated mainly by the well understood biological mechanism of yeast mating, together with their genetic amenability. In addition, recent advances in yeast biosensing establish yeast as a suitable detector and a neat interface to in-body sensor networks. The system under consideration is presented first, and the mathematical models of the underlying biological processes leading to an end-to-end (E2E) system are given. The experimental setup is then described and used to obtain experimental results which validate the developed mathematical models. Beyond that, the ability of the system to effectively generate output pulses in response to repeated stimuli is demonstrated, reporting one event per two hours. However, fast RNA fluctuations indicate cell responses in less than three minutes, demonstrating the potential for much higher rates in the future.

Renewable energy sources, expected to form about 70% of power systems by 2050, bring challenges like fluctuating outputs and grid instability. Advanced power monitoring systems, crucial in environments like research facilities and hospitals, must navigate these dynamic scenarios. Traditional power management, especially Uninterruptible Power Supply (UPS) systems, often needs to catch up due to high costs and limited response to varied power demands, focusing mainly on constant power supply without differentiating between constant and fluctuating loads. In response, Artificial intelligence (AI) techniques are becoming indispensable for real-time power prediction and control. A distributed AI framework forecasts power needs, considering renewable sources, loads, and storage. This is key to ensuring smooth mini-grid operations, balancing operational demands with environmental considerations, and advancing intelligent energy management. Such systems are essential in optimizing energy usage, aligning it with available power to enhance efficiency and reduce waste. This is particularly important for mini-grids, with or without UPS systems, where predictive monitoring can substantially cut operational costs and extend lifespan.The paper focuses on providing consistent, constant, and fluctuating power by predicting mini-grid power needs hourly from the previous day's data. We use a Temporal Convolutional Network (TCN) for time series prediction, integrated within the BDIx agent's belief system through TensorFlow Lite. This approach accurately predicts upcoming power needs, ensures smooth operation, and prevents power outages. The TCN model's predictive capabilities highlight a significant stride in combining AI with energy management to address the complexities of modern power systems.

In a predefined geographical field, a group of sensor nodes communicating wirelessly form a Wireless Sensor Network (WSN). The sensors' goal is to upload the sensed data to the control station to determine if any immediate action should be taken or to analyze and monitor the situation. In clustering algorithms, the sensors are grouped as clusters, where each cluster has one sensor selected as the cluster head (CH) responsible for all inter-cluster communication. In a crisis scenario, the CH might be non-functional, resulting in a disconnected cluster. An enhanced WSN weighted cluster routing scheme is proposed in this paper. A cluster index based on distance, rewarding index, and energy is used to select the CH and cluster members (CM). The proposed scheme aims at ensuring that the data is uploaded even though the CH is non-functional by selecting a redundant CH for every node. Therefore, no matter how many sensors are inactive, a CH is still selected to ensure inter-cluster communication. The delays generated in the proposed routing scheme are studied using MATLAB simulation. In addition, the effect of different weights is studied on the delay.