Indoor Internet of Things (IoT) deployments in complex buildings often encounter irregular propagation, moving obstacles, uneven demand, and tight energy budgets, which render static designs fragile and costly to maintain. This paper presents a reconfigurable planning and optimization framework that unifies Voronoi-guided node placement, mobile-agent coordination, and Reconfigurable Intelligent Surface (RIS) control under a single analytics loop. The initial deployment utilizes Voronoi tessellation to allocate nodes, taking into account obstacles and access constraints. Mobile agents utilize pathfinding and swarm behaviours to map, relay, and restore connectivity as conditions change. RIS panels are tuned by a Reinforcement Learning (RL) agent that selects among adaptive activation patterns using real-time feedback on coverage, Quality of Service (QoS) and energy. A real-time analytics layer tracks coverage ratio, connectivity, latency, throughput, energy per bit and resilience, enabling closed-loop reconfiguration. Simulations show that coverage improves by 35% over random placement, connectivity increases by 40%, and the energy per delivered bit decreases by 28% through RL-optimized RIS control. Learning converges within practical horizons and adapts to dynamic events. The result is a valuable tool for IoT network planning and operations, transforming building layouts into actionable deployment blueprints and resilient runtime policies.

Digital Twin (DT) applications in smart buildings depend on continuous, high-quality sensing updates. In practice, walls, furniture, and human mobility introduce frequent shadowing and link fluctuations, which degrade update freshness and increase energy spent on retransmissions. This paper proposes an integrated orchestration framework that links geometric deployment, indoor reconfiguration and aerial edge assistance under a unified mathematical formulation. Deployment is driven by spherical-oriented 3D Voronoi partitioning to structure volumetric coverage, while a multi-objective genetic search selects ceiling-mounted anchors and relay positions that balance coverage, cost, and energy. When the environment changes, an event-triggered control loop adapts RIS configurations and local relay actions to maintain link quality with minimal overhead. For periods that require rapid synchronization, a mobile edge node carried by an Autonomous Aerial Vehicle (AAV) provides on-demand support from AAV-accessible locations such as perimeter hovering points, courtyards, atriums, or authorized indoor corridors in large venues. The AAV augments connectivity and compute by establishing short, high-quality links to selected gateways and anchors. Also, by offloading burst DT processing. Joint waypoint selection, bandwidth allocation and compute scheduling are formulated as a constrained Markov decision process and learned using proximal policy optimization.

The complexity of 6G and beyond networks makes manual configuration impractical when coverage, resource allocation, energy and service quality must be optimized jointly. This paper introduces an automation framework that combines large language model guidance with automated machine learning and Voronoi-based spatial optimization. Weighted Voronoi cells create adaptive service regions and enable region-specific state construction that captures load, link quality, backlog and energy. Operator intent and a policy knowledge base drive an LLM mapping layer that cut back the pipeline search space to policy-compliant candidates. Each region then performs rolling-data training with regularized empirical risk minimization and validation-based selection to choose a lightweight model that outputs control actions. A trust gate combines explainability and compliance to approve actions or trigger a safe fallback under human supervision. The resulting closed-loop design reduces search overhead, supports green operation and provides transparent decisions for 6 G orchestration. Simulations show stable objectives under varying load.

Green communication and sustainable operations have become critical objectives in Information and Communications Technology (ICT) systems, particularly when integrating energy-intensive technologies such as Autonomous Aerial Vehicles (AAVs). Therefore, this paper introduces a dynamic resource allocation framework for AAV-enabled green edge networks that adaptively manages bandwidth and computational power while optimizing AAV trajectories. By explicitly formulating the problem as a Markov Decision Process (MDP) and employing Deep Reinforcement Learning (DRL) with Proximal Policy Optimization (PPO), the proposed system strikes a balance between high data synchronization demands and strict energy constraints, leading to improved throughput and sustainability. The simulation results reveal that this approach significantly boosts data throughput and communication efficiency while reducing energy consumption. These findings pave the way for environmentally responsible edge networks that meet both performance requirements and sustainability targets.

Network slicing has emerged as a transformative enabler for meeting the diverse requirements of SG and beyond networks, including 6G. However, network slices' dynamic and virtualized nature introduces significant security challenges, particularly against evolving cyber threats. We propose a Deep Reinforcement Learning (DRL) based Moving Target Defense (MTD) strategy tailored for secure network slicing to address these challenges. Our approach utilizes a Q-Learning framework to manage MTD actions dynamically, optimizing security while maintaining service quality. Extensive simulations demonstrate the effectiveness of our framework in minimizing attack success rates and ensuring operational stability, significantly outperforming baseline methods such as random decision-making.

Semantic communications prioritize transmitting meaningful information over raw data in communication systems. However, these systems face significant optimization challenges, particularly concerning resource efficiency and fidelity due to the costly and delicate nature of photonic resources. Quantum computing offers promising solutions to these challenges through its unique capabilities, such as superposition and entanglement, within high-dimensional Hilbert spaces. This survey reviews the integration of quantum computing into semantic communications, tracing developments from foundational concepts to current advancements. It explores how quantum embeddings and machine learning techniques enhance semantic representation and transmission, enabling the encoding and processing of only relevant information. This approach addresses issues of polysemy and contextual variations in large datasets more effectively than classical methods. Key topics include the utilization of quantum probability and cognition in semantic analysis, optimization of quantum protocols for faster information retrieval, and the role of variational quantum circuits in improving computation latency, communication bandwidth, data privacy, and transmission delays. It also examines the implications for future communication systems like next-generation networks. It highlights the shift towards intelligent computing-intensive architectures and the support for advanced applications such as extended reality and holographic communications. Potential pathways are identified to achieve more efficient, secure, and intelligent data transmission, aiming to provide researchers and practitioners with a thorough understanding of this emerging field, outlining open research challenges and future directions to inspire further exploration and innovation.

This paper addresses a critical gap in Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) networks, where existing works inadequately integrate UAV deployment optimization with privacy-preserving Federated Learning (FL) and adaptive resource allocation under dynamic network conditions. The research explores the deployment of multi-UAV networks in IoT environments, emphasizing their dual roles in expanding cellular network coverage and facilitating efficient data collection. Unlike prior studies that treat UAV placement and FL-driven resource optimization separately, we present a unified hybrid framework leveraging Deep Reinforcement Learning (DRL) and FL. The proposed framework incorporates the Multi-UAV Network Formation (MUNF) algorithm, which employs Particle Swarm Optimization (PSO) to improve the Signal-to-Noise Ratio (SNR) for effective data collection. Additionally, the Dynamic Adaptive Strategy (DAS) utilizes a Deep Deterministic Policy Gradient (DDPG) approach to optimize resource allocation, reduce latency, and enhance throughput. Extensive simulations demonstrate a 26% increase in data throughput, an 18% reduction in latency, and more stable SNR distribution compared to state-of-the-art baselines. These results indicate a consistent improvement in network efficiency and scalability, validating the proposed framework’s capability to address real-world UAV-assisted IoT challenges more effectively than prior work.

Ensuring Cyber security professionals are well-prepared to handle real-world cyber threats requires continuous training and access to diverse and up-to-date threat scenarios. Within this context, Cyber Ranges play a crucial role through the provision of controlled environments for Cyber Security training and simulations. However, a significant challenge identified in the literature is the lack of interoperability between Cyber Ranges, which hinders the exchange of exercises and resources. This paper showcases the utilization of a Knowledge Repository with a unified data model to address the interoperability challenge among Cyber Ranges, enabling seamless data exchange and integration. To the best of our knowledge, this approach has not been provided in any other existing solution. The paper further explores the challenges of implementation and the potential impact of this solution on cross-sector data sharing and regulatory compliance.

This demo paper presents a Java-based simulator that enables optimized deployment and adaptive management of IoT nodes in healthcare environments using 3D Voronoi diagrams, hybrid algorithms, and Reconfigurable Intelligent Surfaces (RIS). It supports planning, validation, and analysis through real-time simulation and analytics.

This paper presents a framework designed to significantly enhance the performance and visibility of Autonomous Aerial Vehicles (AAVs) deployed for maritime border surveillance. Traditional approaches often struggle with limited coverage and inefficiencies due to static deployment and isolated optimization strategies. To address these issues, the proposed approach integrates 3D Voronoi-based node deployment with hybrid optimization techniques. Specifically, it combines a modified Genetic Algorithm (GA) to strategically refine sensor node placements, along with a Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm enhanced by Particle Swarm Optimization (PSO) for dynamically optimizing AAV trajectories and computational task allocations. Simulation results demonstrate that the proposed method achieves significantly enhanced area coverage, reduced energy consumption, improved task completion rates, and overall better Quality of Service (QoS) compared to conventional methods.

Modern IoT and Fog environments are complex and diverse ecosystems that consist of numerous devices. Some of these devices can receive and process offloaded tasks. For such devices to operate at the highest capacity levels, there is a need for mechanisms that could optimize their performance with offloaded tasks. That includes, but is not limited to, such aspects as resource management, workload balancing and scheduling. Unlike local tasks, offloaded ones are not a part of device’s environment. Therefore, processing them should not irreparably disrupt a device’s functionality. This requires devices to have a mechanism for managing offloaded tasks differently from their local. The current work attempts to research possible ways to optimize in-device execution of offloaded tasks, while reducing detrimental effects to a device’s state. To achieve that, the solution involves application of Reinforcement Learning techniques. The work proposes to utilize Deep Deterministic Policy Gradient (DDPG) Actor/Critic method, to allow devices continuously learn optimal scheduling strategies for offloaded tasks. The contribution of this work is in its exploration of the impact machine learning makes on in-device scheduling, application feasibility and the overall execution time optimization of offloaded tasks.

The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time monitoring, diagnostics, and secure data-driven decision-making. However, IoMT networks are vulnerable to adversarial attacks, data breaches, and privacy threats, making secure and optimized node deployment a critical challenge. This paper presents a novel framework integrating 3D Voronoi diagrams and K-means clustering with a hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) to optimize IoMT node placement while enhancing security and resilience. Initially, K-means clustering distributes nodes, followed by spatial partitioning with 3D Voronoi diagrams. The PSO-GA hybrid algorithm then iteratively refines node positions, balancing rapid convergence with global exploration to achieve optimal configurations that improve coverage, energy efficiency, and secure data exchange. Additionally, the proposed approach integrates risk assessment techniques and privacypreserving mechanisms to mitigate adversarial threats, ensuring robustness against poisoning and evasion attacks. By dynamically adapting to changing healthcare environments, the framework enhances network resiliency while aligning with AI security and privacy-by-design principles. Experimental results validate the algorithm's scalability and effectiveness, making it a promising solution for real-world IoMT applications in secure medical monitoring, diagnostics, and AI-driven threat intelligence.

A group of unmanned aerial vehicles (UAVs) cooperating to complete predetermined tasks is referred to as a swarm of UAVs (S-UAVs). Clustering, which divides UAVs into clusters, is one of the routing techniques that S-UAVs use the most. A cluster head (CH) and cluster members (CM) make up each cluster. Due to its critical importance in routing packets to their destination, the CH selection process is an ongoing study area. Any UAV is vulnerable to damage in emergency situations, such as a fire. To guarantee end-to-end communication in the event of a non-functional CH, we suggest an optimized clustered weighted approach based on multiple redundancy. The CH, redundant CHs, and CMs are chosen using an optimized weighted measure that combines separation, speed, energy, and performance parameters. The proposed method automatically assigns a redundant node for every UAV to ensure a functional CH despite the number of non-functional UAVs that might be. According to the outcomes of the simulation that was run using MATLAB, this is a promising approach that ensures data delivery with a minimal delay in any emergency.

The rapid advancement of Artificial Intelligence (AI) is reshaping industries and driving global innovation. However, the increasing complexity of AI models demands substantial data and computational resources, leading to significant energy consumption and environmental impact. This article explores the integration of quantum computing and end-to-end automation strategies in cloud-edge architectures. It proposes a hybrid quantum-classical AI framework that enhances training efficiency and reduces data and processing intensity by minimizing energy consumption. The framework leverages automated model orchestration, adaptive resource allocation, and intelligent data processing at the edge to improve system efficiency. In addition, it addresses ethical considerations, including privacy, fairness, and trustworthiness, to ensure alignment with human values. This approach significantly improves AI performance while fostering a sustainable and ethical AI ecosystem.

In indoor smart spaces, ensuring reliable IoT connectivity is a significant challenge. Walls, furniture, and other obstacles often create coverage gaps that disrupt wireless links. This work presents a system-level integration and validation study of a hybrid networking framework that combines static, mobility-enabled, and reconfigurable components to sustain connectivity in complex indoor environments. First, a 3-D Voronoi-based deployment of static IoT nodes provides an optimized baseline coverage map. Next, a set of mobility-enabled platforms, ceiling-rail robots and floor Automated Guided Vehicles (AGVs), form an adaptive layer that can reposition as needed to restore links or extend coverage around obstacles. In parallel, Reconfigurable Intelligent Surfaces (RIS) are strategically placed on walls or ceilings to dynamically redirect wireless signals, creating virtual line-of-sight paths and mitigating shadowing. A multi-objective Genetic Algorithm (GA) coordinates these elements, optimizing coverage, connectivity, and latency simultaneously. Across hospital, office, and warehouse layouts, the integrated system achieves on average 95.6% volumetric coverage and 100% connectivity. Latency and energy-efficiency results are reported together with 95% confidence intervals to highlight statistical robustness. The study quantifies the gains of a unified controller versus sequential or disjoint orchestration and provides sensitivity analyses under RIS quantization and shadowing effects. The contribution is therefore positioned as a practical, unified framework that couples planning, mobility, and RIS operation under non-ideal hardware and channel conditions.