There is a lack of adaptive mechanisms for handling dynamic E2 subscriptions in O-RAN architectures to enable high-volume message processing in near-real-time RIC environments. In this paper, we present the design and implementation of a high-performance xApp for near-real-time RAN control within the O-RAN architecture. The xApp manages E2 subscriptions dynamically, processes incoming KPM indication messages in a multi-threaded manner, and forwards enriched metrics to message brokers for cloud analytics. It employs closure-based callbacks to inject E2 node identifiers into messages without modifying the standard interface, unlike prior studies that break standardization efforts. The system is implemented in C using the libffcall library to enable closure functionality. The xApp is evaluated in a Kubernetes environment with FlexRIC and multiple simulated gNBs. Evaluation shows that the proposed architecture achieves significant performance gains over a single-threaded baseline. The average end-to-end latency decreases from 47 ms to 14 ms, and throughput increases from 820 to 3150 messages per second. These results demonstrate that the proposed design provides lower-latency data handling for near-real-time RIC applications.

Next-generation networks, including IoT, edge, vehicular, and 6G systems, require cryptographic schemes that not only resist attacks but also withstand channel degradation without disrupting secure data flows. This paper presents a novel adaptive encryption architecture for quantum-resilient networks that integrates real-time attack detection in quantum channels with per frame key management. A lightweight controller has been designed, which tracks the quantum bit error rate (QBER) via a sliding-window hysteresis and buffer thresholds, deciding per frame whether to derive symmetric keys from Quantum Key Distribution (QKD) or from a post-quantum cryptographic fallback, while maintaining a unified AES–GCM AEAD dataplane. Moreover, a deterministic HMAC-based key derivation function (HKDF) schedule binding eliminates key reuse across modes, retransmissions, and re-encapsulations. Experimental evaluation on image payloads shows: (i) timely QBER-spike detection with 9.5-frame latency and zero misses; (ii) full decryptability and tamper detection across both key sources; (iii) symmetric throughput of ≈ 1.1GB/s with negligible adaptation overhead; and (iv) near-ideal ciphertext metrics including entropy 8.02bits/pixel, NPCR ≈ 99.6%, and UACI ≈ 30.7%. The framework achieves secure, continuous encryption by preventing unsafe QKD use and maintaining authenticated operation under quantum-channel degradation.

The security of emerging 6G small-cell networks increasingly depends on intelligent deception mechanisms, where honeypots serve as proactive defence components. However, conventional honeypots lack semantic and behavioural realism and often risk leaking sensitive data when emulating complex network states. Existing systems remain limited by static personas, weak sanitisation, and unexplainable reactions, making them ineffective against adaptive attackers. To address this challenge, we propose TwinPot-Gen, a digital-twin-assisted honeypot framework that integrates semantic communication , graph-based knowledge modelling, and generative AI to achieve realistic and privacy-preserving deception. The framework establishes two isolated domains-the Digital-Twin Network and the Honeypot Network-connected through two secure gateways: the Generative Knowledge Gateway (GKG) and the Defence-Orchestration Gateway (DOG). GKG performs semantic sanitisation and persona generation using a large language model, while DOG enforces adaptive, policy-driven responses. Experimental results on a Kubernetes-based 6G testbed show that TwinPot-Gen improves persona realism by 35-50%, achieves a 0.94 F1-score in attack detection, and maintains sub-100 ms orchestration latency, confirming its efficiency and trustworthiness in dynamic 6G environments.

The growing scale and sophistication of modern cyberattacks demand anomaly detection models capable of capturing non-Euclidean and highly relational patterns embedded within network traffic. This study introduces a unified and interpretable graph-based framework for network anomaly detection that systematically evaluates four representative graph neural network (GNN) architectures—GCN, GAT, GIN, and GraphSAGE—under identical experimental settings. Using the UNSW-NB15 benchmark dataset, IP addresses are modeled as graph nodes and communication flows are modeled as directed edges with flow-level attributes, enabling the extraction of structural attack behaviors. To provide complementary external robustness evidence, the framework is additionally evaluated on CIC-DDoS2019 under a DDoS-focused scenario and on a CSE-CIC-IDS2018-based diverse intrusion dataset under a broader multi-attack benchmark setting. A consistent preprocessing pipeline, standardized model configuration, 5-fold stratified cross-validation, statistical validation, and sensitivity analysis are employed to evaluate robustness and stability. Experimental results show that GIN and GraphSAGE achieve the strongest performance, with F1-scores of 0.9693 ± 0.0007 and 0.9721 ± 0.0006, respectively, and ROC-AUC values above 0.99 across folds. In addition, computational profiling is conducted to analyze inference latency, throughput, the number of trainable parameters, and GPU memory usage, highlighting the scalability advantages of aggregation-based models. Beyond quantitative evaluation, a dynamic D3.js-based attack topology visualization is presented to reveal attacker–target interactions, dominant attack categories, and high-frequency communication paths. Overall, this study provides a reproducible benchmarking and visualization framework for interpretable and structurally aware graph-based cybersecurity analytics.

Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has received significant attention as a potential technology for the sixth generation (6G) of wireless network due to its ability to boost signal coverage and enhance system efficiency. In this paper, we investigate the potential of a near-optimal hybrid quantum-classical optimization approach to jointly optimize beamforming and the discrete phase shifts of the STAR-RIS assisted wireless network. In particular, we formulate a discrete optimization problem to maximize the total power transmitted to the ground users. This is achieved by optimizing the beamforming at the base station (BS) and the phase shift of the STAR-RIS under minimal power allocation for each user and the maximum power budget at the BS. Since the addressed problem is NP-hard, we propose a quantum approximate optimization algorithm with alternating optimization (QAOA-AO) method that iteratively addresses beamforming components and discrete phase shifts to search for the near-optimal solutions for the problem. Numerical results validate the effectiveness and robustness of the proposed QAOA-AO compared to the classical benchmarks in terms of runtime and system power, and highlight its potential for practical deployment when solving medium-to-large-scale networks.

In this paper, we explore a maritime 6G-enhanced satellite-air-ground integrated network (SAGIN) that incorporates a UAV-carried reconfigurable intelligent surface (UCR) relay, and low Earth orbit (LEO) satellites equipped with mobile edge computing (MEC) facilities. The system captures dynamic maritime conditions, including ultra-reliable low-latency communication (URLLC) user mobility and UCR movements across harbor environments. The primary objective is to minimize the total system cost by jointly optimizing task offloading decisions, bandwidth allocation, local computational resource distribution, transmission power control, and caching management, while satisfying strict latency and resource constraints. To address this, we formulate a mixed-integer nonlinear programming (MINLP) problem that captures the complexity of resource optimization in the maritime 6G-enhanced SAGIN. Two quantum-enhanced deep reinforcement learning algorithms, namely quantum-enhanced deep deterministic policy gradient (QEDDPG) and quantum-enhanced proximal policy optimization (QEPPO), are proposed to solve the formulated MINLP problem. Moreover, higher-order quantum feature encoding and quantum neural networks are utilized to accelerate learning and enhance decision-making. Simulation results demonstrate that QEDDPG and QEPPO significantly outperform conventional deep reinforcement learning methods by achieving lower system costs and more efficient resource allocation. These findings shows that the potential of quantum-driven reinforcement learning for enabling scalable, efficient, and intelligent resource management in future 6G-enhanced SAGINs.

Energy-efficient space–air–ground integrated networks (SAGINs) are vital for sustainable communications. This study presents an energy-aware SAGIN framework that utilizes a uncrewed aerial vehicle (UAV)-mounted mobile edge computing (MEC) platform enhanced by digital-twin technology, UAV energy harvesting via wireless power transfer, and a nano-satellite constellation with MEC facilities. We formulate a joint optimization problem for UAV trajectory planning, task offloading, computational resource allocation, and satellite load balancing as a mixed-integer nonlinear programming (MINLP) problem that minimizes the weighted system cost while satisfying energy and latency constraints. To address this complex problem, two quantum-driven deep reinforcement learning (QD-DRL) algorithms namely quantum-driven cost-effective advantage actor–critic (QD-CE-A2C) and quantum-driven cost-effective proximal policy optimization (QD-CE-PPO) are proposed. These algorithms employ angle encoding with learnable parameters and variational quantum neural networks to enhance policy exploration and accelerate convergence. Simulation results demonstrate that the proposed QD-DRL approaches achieve superior cost efficiency and ensure effective service to all access points within the defined mission duration. Moreover, QD-DRL approaches achieved higher cumulative rewards and faster convergence compared to classical DRL baselines. Consequently, the proposed frameworks provide a scalable and intelligent paradigm for cost-efficient resource management in future 6G-enabled SAGINs.

This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

Handover management in mobile networks is challenged by high latency and reduced reliability in dense deployments and under user mobility. Here, existing schemes improve handover initiation by optimising the candidate handover at the decision time. However, these are applied after a non-negligible delay due to the control-plane signalling. Then, when applied, it may become invalid or degrade performance. To address this, we propose a Digital Twin (DT)-assisted handover scheme that performs predictive execution-time validation prior to the preparation of the Next Generation (NG)-based handover. To this end, the DT-What-If Generator (DT-WIG) is used to emulate short-horizon future network states under uncertainty. Here, the DT-WIG is a spatiotemporal graph generative model that uses variational latent sampling to generate counterfactual post-handover trajectories for the candidate handover decision. Then, the AMF estimates the failure and QoS risks associated with the candidate handover and approves/rejects it via standard-compliant signalling. With this, we form a policy-agnostic mechanism that runs on the underlying handover policy. Consequently, we evaluate performance using ns-3/5G-LENA trace generation and replay-based policy analysis, with OpenAirInterface-based signalling evaluation. The results show that the proposed method reduces the handover failure rate and handover interruption time while improving latency, jitter, throughput, and packet loss.

This paper propose a two-stage quantum neural network (QNN) framework for cell-free multiple-input and multiple-output (MIMO) wireless communication systems. Cell-free MIMO, which has been regarded as a key technology for enhancing the performance of the next-generation wireless communication systems, leverages the collective capability of multiple distributed access points (APs), allowing collaboration between them. However, optimizing cell-free MIMO can pose challenges for centralized optimization schemes. In particular, complexities associated with the joint optimizations of user-transmission assignment and transmission precoding, two factors which are of much importance for determining the quality-of-service, grow with the number of APs and served users. To this end, a unified scheme employing distributed QNNs is used to optimize downlink transmitter-user assignment and transmit precoding with the goal of maximizing the achieved sum rate. Firstly, the cloud processing unit, which holds holistic information about the particular wireless communication network, employs QNN to assign each AP to its designated mobile terminal. Secondly, the edge processing units, which are computed in proximity relative to the AP in order to reduce latency, estimate transmission precoding for their corresponding APs. Moreover, numerical results are presented to showcase the performance of the proposed protocol.

The Network Digital Twins (NDTs) have become a frontier thanks to their real-time monitoring, analysis, prediction, and optimisation capabilities. However, their architecture has not been designed for the scale of next-generation networks that will ensure ubiquitous connectivity. At this, different NDT applications may require distinct levels of quality of service requirements to be met. With increasing size and heterogeneity in the networks, the processing load at the NDTs escalates, and these requirements may not be met. Therefore, we propose a microservice-based architecture with application-oriented slicing. Here, we present the scaling methodology, which enables scaling at both microservice and slice levels. Then, we evaluate the throughput, delay, and quality of service requirement violation rate metrics under two scenarios. Thanks to the slicing approach with microservice-based implementation, the end-to-end delay and the QoS requirement violation rate are reduced while having higher throughput performance.

Although the emergence of 6G IoT networks has accelerated the deployment of enhanced smart city services, the resource limitations of IoT devices remain as a significant problem. Given this limitation, meeting the low-latency service requirement of 6G networks becomes even more challenging. However, existing 6G IoT management strategies lack real-time operation and mostly rely on discrete actions, which are insufficient to optimise energy consumption. To address these, in this study, we propose a Digital Twin (DT)-guided energy management framework to jointly handle the low latency and energy efficiency challenges in 6G IoT networks. In this framework, we provide the twin models through a distributed overlay network and handle the dynamic updates between the data layer and the upper layers of the DT over the Real-Time Publish Subscribe (RTPS) protocol. We also design a Reinforcement Learning (RL) engine with a novel formulated reward function to provide optimal data update times for each of the IoT devices. The RL engine receives a diverse set of environment states from the What-if engine and runs Deep Deterministic Policy Gradient (DDPG) to output continuous actions to the IoT devices. Based on our simulation results, we observe that the proposed framework achieves a 37% improvement in 95th percentile latency and a 30% reduction in energy consumption compared to the existing literature.

In vehicle-to-everything (V2X) networks, real-time telemetry is essential for enabling predictive analytics and fault detection in intelligent transportation systems. However, frequent wireless disruptions due to interference, mobility, and congestion lead to telemetry gaps that degrade downstream decision-making. To address this challenge, we propose a framework that enhances wireless telemetry robustness using large language models (LLMs) guided by digital twin-based context. Our system combines retrieval-augmented generation with environmental priors to recover high-dimensional, time-correlated telemetry streams lost during communication outages. We also integrate federated continual learning to maintain fault classification performance across non-i.i.d. V2X conditions without centralized data exchange. Extensive evaluations on real-world driving datasets with simulated wireless impairments show that our method significantly improves reconstruction fidelity, reduces degradation from multi-step gaps, and sustains long-term classifier stability. This work demonstrates how AI-driven semantic recovery mechanisms can improve the functional reliability of wireless V2X telemetry under dynamic and lossy network conditions.

Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and verifiable physical-digital binding, making them unsuitable for latency-sensitive and distributed deployments. To address this gap, we introduce PRZK-Bind, a lightweight and decentralised authentication protocol that combines Schnorr-based zero-knowledge proofs with elliptic curve cryptography to establish secure, real-time correspondence between physical entities and DTs without relying on pre-shared secrets. Simulation results show that PRZK-Bind significantly improves performance, offering up to 4.5 times lower latency and 4 times reduced energy consumption compared to cryptography-heavy baselines, while maintaining false acceptance rates more than 10 times lower. These findings highlight its suitability for future smart city deployments requiring efficient, resilient, and trustworthy DT authentication.

Wireless networks are vulnerable to jamming attacks due to the shared communication medium, which can severely degrade performance and disrupt services. Despite extensive research, current jamming detection methods often rely on simulated data or proprietary over-the-air datasets with limited cross-layer features, failing to accurately represent the real state of a network and thus limiting their effectiveness in real-world scenarios. To address these challenges, we introduce JamShield, a dynamic jamming detection system trained on our own collected over-the-air and publicly available dataset. It utilizes hybrid feature selection to prioritize relevant features for accurate and efficient detection. Additionally, it includes an autoclassification module that dynamically adjusts the classification algorithm in real-time based on current network conditions. Our experimental results demonstrate significant improvements in detection rate, precision, and recall, along with reduced false alarms and misdetections compared to state-of-the-art detection algorithms, making JamShield a robust and reliable solution for detecting jamming attacks in real-world wireless networks.

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50 % due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1].

Digital Twins (DTs) have become indispensable in 6G for intelligent applications with real-time monitoring, modelling, and optimization. However, validating them in real-world conditions using IoT integration remained a significant challenge. Due to specialized designs, current implementations often lack reusability, which prevents integration. Here, data streams have to be formed while managing the diverse IoT data sources, formats, and volumes. However, interoperating these requires extensive manual programming. Considering these challenges, this paper proposes the Scenario Emulated IoT-DT architecture that defines the layers of Data, Ingestion, and DT. Here, the Scenario Emulator can form and integrate the data streams with the different intelligent applications. Within this, we use graph-encoding in Device and Data Schema Registry to define specifications. With this, we can embed the required information for interoperability and integration. Moreover, this approach supports scalability thanks to the fast lookup capability of graphs as the number of objects increases. Using the proposed architecture, we implement the use case of smart building management and test it in different scenarios. The results show that the proposed architecture can effectively operate with up to 74.7 % line-of-code improvement and querying latency reduction.

Even though the emergence of 6G IoT systems has accelerated the deployment of hyper-connected networks, the inherent resource limitations of IoT sensors remain a significant problem. In addition, maintaining energy efficiency and low response times in such environments has become more challenging. However, the existing management methods often lack the real-time adaptability and intelligence to optimize energy consumption in 6G IoT networks. To tackle this, we propose a DT-based collaborative management consisting of a multi-agent twin layer, a collaboration protocol and a Reinforcement Learning (RL)-based learner model. In the multi-agent twin layer, each physical network sensor is modelled as an individual agent for enhanced granularity in the management. The collaboration protocol ensures information sharing among the sensors and, thus, lowers response times. Furthermore, in the learner model, we utilize a multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimise actions according to the novel energy-aware reward function. According to our simulation results, the proposed DT-based collaborative management surpasses the traditional method by 27 % for longer battery levels and 65 % more rapid responses.

With the development of software-defined edge networks, network management has become more flexible and realtime. However, this advancement has also led to critical security concerns, especially when detecting attacks efficiently in resourceconstraint environments. Existing solutions often suffer from high computational load, making them unsuitable for the fast, dynamic environments of resource-constrained edge environments. To tackle this issue, we introduce a lightweight attack detection system that combines digital twins with advanced machine learning techniques. Our approach uses a stacked sparse autoencoder (ssAE) for feature extraction and reduction and a hybrid CNNGRU model for accurate attack classification. The simulation results show that our solution significantly outperforms existing models, which are ANOVA-DNN, AE-MLP and CNN-LSTM. It achieves the highest detection accuracy at$\mathbf{9 9. 7 2 \%}$and a suitable low time-cost at 0.215 ms, providing a good balance between accuracy and speed. Moreover, it delivers the lowest computational load compared to others, which makes it ideal for deployment in real-time resource-limited environments.

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications; however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation.

Maritime transportation is essential for global trade but presents significant environmental challenges due to its greenhouse gas emissions. Existing studies have addressed these challenges through integrated routing and speed optimization frameworks, yet frequently lack explicit quantification of environmental impacts and exhibit limited scalability for large-scale ship routing operations. Conversely, existing quantum optimization research in vehicle routing predominantly targets land-based transportation scenarios, restricting its direct applicability to maritime logistics. Maritime logistics inherently involve distinct operational complexities, such as nonlinear interactions among speed, payload, fuel consumption, and numerous operational uncertainties. These combined limitations underscore the critical need for quantum optimization methods explicitly designed for green maritime supply chains. To bridge this gap, this paper proposes an efficient quantum-centric optimization framework that uses the quantum approximate optimization algorithm (QAOA) to jointly optimize ship routing and speed management within sustainable maritime supply chains. Specifically, we formulate an NP-hard cost minimization problem integrating critical maritime parameters, including fuel consumption, payload constraints, and operational speeds. We further develop a hybrid quantum-classical alternating optimization approach that iteratively addresses routing decisions through quantum computing techniques and optimizes ship speed using an analytical solution. Simulation results and real quantum hardware experiments demonstrate that our quantum-centric methodology achieves substantial cost reductions and highlights the potential for practical applicability in realistic maritime operations, significantly outperforming classical optimization benchmarks.

Network backbone black holes(BH) pose significant challenges in the Internet by causing disruptions and data loss as routers silently drop packets without notification. These silent BH failures, stemming from issues like hardware malfunctions or misconfigurations, uniquely affect point-to-point packet flows without disrupting the entire network. Unlike cyber attacks and network intrusions, BHs are often untraceable, making early detection vital and challenging. This study addresses the need for an effective forecasting solution for BH occurrences, especially in environments with unlabeled traffic data where traditional anomaly detection methods fall short. The Type-Independent Black Hole Forecasting Model is introduced to predict BH occurrences with high precision across various anomalies, including contextual and collective anomaly types. The three-stage methodology processes unlabeled time-series network data, where the data is not pre-labeled as anomaly or normal, using machine learning and deep learning techniques to identify and forecast potential BH occurrences. The ’Point BH Identification and Segregation’ stage segregates point BH traffic using Density-Based Spatial Clustering of Applications with Noise(DBSCAN), followed by Reintegration and Time Series Smoothing. The final stage, Advanced Contextual and Collective BH Detection leverages Convolutional AutoEncoder(Conv-AE) with window sliding for advanced anomaly detection. Evaluation using a dual-dataset approach, including real backbone network traffic and a time-series adapted public dataset, demonstrates the adaptability of the model to real backbone BH detection systems. Experimental results show superior performance compared to state-of-the-art unsupervised anomaly forecasting models, with a 98% detection rate and 90% F-1 score, outperforming models like MultiHeadSelfAttention, which is the main building block of Transformers.

This paper investigates the potential of Digital Twins (DTs) to enhance network performance in densely populated urban areas, specifically focusing on vehicular networks. The study comprises two phases. In Phase I, we utilize traffic data and AI clustering to identify critical locations, particularly in crowded urban areas with high accident rates. In Phase II, we evaluate the advantages of twinning vehicular networks through three deployment scenarios: edge-based twin, cloud-based twin, and hybrid-based twin. Our analysis demonstrates that twinning significantly reduces network delays, with virtual twins outperforming physical networks. Virtual twins maintain low delays even with increased vehicle density, such as 15.05 seconds for 300 vehicles. Moreover, they exhibit faster computational speeds, with cloud-based twins being 1.7 times faster than edge twins in certain scenarios. These findings provide insights for efficient vehicular communication and underscore the potential of virtual twins in enhancing vehicular networks in crowded areas while emphasizing the importance of considering real-world factors when making deployment decisions.

The advancement of sophisticated communication technologies and robust computing systems has unlocked opportunities for new applications across various domains. While these applications promise enhanced convenience and improved living standards, they also raise a critical concern regarding the trade-off between convenience and environmental sustainability. This paper addresses this concern by investigating sustainable resource management, employing a digital twin approach to minimise CO2emissions in edge computing systems. Specifically, our aim is to reduce the amount of CO2emissions by optimising the allocation of computing and communication resources. This includes optimising transmit power, adjusting the clock speed for task processing, and making optimal decisions regarding task offloading. To tackle this complex optimisation problem, we employ an iteratively alternating optimisation algorithm. Through extensive simulations, we illustrate the efficacy of our proposed solution in not only mitigating CO2emissions but also optimising resource allocation, thereby contributing to both environmental sustainability and technological efficiency.

Despite the crucial importance of addressing Black Hole failures in Internet backbone networks, effective detection strategies in backbone networks are lacking. This is largely because previous research has been centered on Mobile Ad-hoc Networks (MANETs), which operate under entirely different dynamics, protocols, and topologies, making their findings not directly transferable to backbone networks. Furthermore, detecting Black Hole failures in backbone networks is particularly challenging. It requires a comprehensive range of network data due to the wide variety of conditions that need to be considered, making data collection and analysis far from straightforward. Addressing this gap, our study introduces a novel approach for Black Hole detection in backbone networks using specialized Yet Another Next Generation (YANG) data models with Black Hole-sensitive Metric Matrix (BHMM) analysis. This paper details our method of selecting and analyzing four YANG models relevant to Black Hole detection in ISP networks, focusing on routing protocols and ISP-specific configurations. Our BHMM approach derived from these models demonstrates a 10% improvement in detection accuracy and a 13% increase in packet delivery rate, highlighting the efficiency of our approach. Additionally, we evaluate the Machine Learning approach leveraged with BHMM analysis in two different network settings, a commercial ISP network, and a scientific research-only network topology. This evaluation also demonstrates the practical applicability of our method, yielding significantly improved prediction outcomes in both environments.

Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused by the lack of right-time data capturing in traditional approaches, resulting in inaccurate modelling and high resource and energy consumption challenges. To fill this gap, we explore spatiotemporal graphs and propose the Reinforcement Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN). By doing so, our study contributes to advancing Green Cities and showcases tangible benefits in accuracy, synchronisation, resource optimization, and energy efficiency. As a result, we note the spatiotemporal graphs are able to offer a consistent accuracy and 55% higher querying performance when implemented using graph databases. In addition, our model demonstrates right-time data capturing with 20% lower overhead and 25% lower energy consumption.

The effectiveness of Intrusion Detection Systems (IDS) is critical in an era where cyber threats are becoming increasingly complex. Machine learning (ML) and deep learning (DL) models provide an efficient and accurate solution for identifying attacks and anomalies in computer networks. However, using ML and DL models in IDS has led to a trust deficit due to their non-transparent decision-making. This transparency gap in IDS research is significant, affecting confidence and accountability. To address, this paper introduces a novel Explainable IDS approach, called X-CBA, that leverages the structural advantages of Graph Neural Networks (GNNs) to effectively process network traffic data, while also adapting a new Explainable AI (XAI) methodology. Unlike most GNN-based IDS that depend on labeled network traffic and node features, thereby overlooking critical packet-level information, our approach leverages a broader range of traffic data through network flows, including edge attributes, to improve detection capabilities and adapt to novel threats. Through empirical testing, we establish that our approach not only achieves high accuracy with 99.47% in threat detection but also advances the field by providing clear, actionable explanations of its analytical outcomes. This research also aims to bridge the current gap and facilitate the broader integration of ML/DL technologies in cybersecurity defenses by offering a local and global explainability solution that is both precise and interpretable.

The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS), significantly enhancing road safety and vehicular communication. However, the intricate and dynamic nature of VANETs presents formidable challenges, particularly in vehicle-to-infrastructure (V2I) communications. Roadside Units (RSUs), integral components of VANETs, are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks. These vulnerabilities pose grave risks to road safety, potentially leading to traffic congestion and vehicle malfunctions. Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity. Our study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs and address this gap. This framework enables real-time monitoring and efficient threat detection while also improving computational efficiency and reducing data transmission delay for increased energy efficiency and hardware durability. Our framework outperforms existing solutions in resource management and attack detection. It reduces RSU load and data transmission delay while achieving an optimal balance between resource consumption and high attack detection effectiveness. This highlights our commitment to secure and sustainable vehicular communication systems for smart cities.

This study explores implementing a digital twin network (DTN) for efficient 6 G wireless network management, aligning with the fault, configuration, accounting, performance, and security (FCAPS) model. The DTN architecture comprises the Physical Twin Layer, implemented using NS-3, and the Service Layer, featuring machine learning and reinforcement learning for optimizing carrier sensitivity threshold and transmit power control in wireless networks. We introduce a robust “What-if Analysis” module, utilizing conditional tabular generative adversarial network for synthetic data generation to mimic various network scenarios. These scenarios assess four network performance metrics: throughput, latency, packet loss, and coverage. Our findings demonstrate the efficiency of the proposed what-if analysis framework in managing complex network conditions, highlighting the importance of the scenario-maker and the impact of twinning intervals on network performance.

Coordinated beamforming is one of the crucial improvements for WiFi7. However, to provide sufficient Quality of Service, the corresponding beams have to be aligned accurately. Conventional approaches search for optimal beams in the available codebook to align the beams. However, this introduces considerable delays because of high search space. To address this issue, we utilize Ultra-Wide-Band (UWB) assisted localization to swiftly adjust the beams for users to provide accurate beams. Then by using Received Signal Strength Indicator (RSSI) and state estimation error of the Kalman filter in the position system, we propose a task oriented beamforming alignment system that uses UWB assisted localization only when necessary. Our approach improves beamforming gain by 11.6% compared to conventional beam scanning approaches. On top of this, the proposed solution improves communication effectiveness by 55.6% against naive UWB localization-assisted beam alignment.

Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, adaptability to dynamic environments, and making sequential decisions based on real-time feedback. Despite these advantages, the use of DRL for UAV trajectory planning requires significant retraining when the UAV is confronted with a new environment, resulting in wasted resources and time. Therefore, it is essential to develop techniques that can reduce the overhead of retraining DRL models, enabling them to adapt to constantly changing environments. This paper presents a novel method to reduce the need for extensive retraining using a double deep Q network (DDQN) model as a pre-trained base, which is subsequently adapted to different urban environments through Continuous Transfer Learning (CTL). Our method involves transferring the learned model weights and adapting the learning parameters, including the learning and exploration rates, to suit each new environment's specific characteristics. The effectiveness of our approach is validated in three scenarios, each with different levels of similarity. CTL significantly improves learning speed and success rates compared to DDQN models initiated from scratch. For similar environments, Transfer Learning (TL) improved stability, accelerated convergence by 65%, and facilitated 35% faster adaptation in dissimilar settings.

Digital twin technology is crucial to the development of the sixth-generation (6G) Internet of Vehicles (IoV) as it allows the monitoring and assessment of the dynamic and complicated vehicular environment. However, 6G IoV networks have critical challenges in network security and computational efficiency, which need to be addressed. Existing digital twin technologies in 6G IoV networks often suffer from limitations, such as reliance on static models and high computational demands, leading to unstable attack detection and inefficiencies. Their results for attack detection performance metrics, precision, detection rate, and F1-Score are insufficient for 6G IoV. Moreover, these systems concentrate all computational processes within the digital twin’s service layer, leading to inefficiencies. To address these challenges, we introduce a novel artificial intelligence (AI) enhanced digital twin framework designed to significantly improve 6G IoV network security and computational efficiency under dynamic conditions. Our framework employs an advanced feature engineering module that uses feature selection methods and stacked sparse autoencoders (ssAE) to reduce feature dimensions within the cyber twin layer, effectively distributing the overall computational load. It also utilizes an online learning module which enables a network-aware attack detection mechanism for precise attack detection. The proposed solution exhibits a stable performance of around 98% success rate regarding attack detection metrics against two data sets. Specifically, our solution reduces system latency by 12%, energy consumption by 15%, RAM usage by 20%, and improves packet delivery rates by 6.1%. These findings underscore the potential of our framework to enhance the robustness and responsiveness of 6G IoV systems, offering a significant contribution to vehicular network security and management.

With the rapid development of intelligent devices in wireless communication, the fifth generation (5G) mobile networks have limited high data rates, low latency, high avail-ability demands. The sixth-generation (6G) mobile network can use Digital-twin (DT) techniques to meet these demands. DT is the virtual representation of physical aspects such as 6G edge nodes. DT optimize the 6G edge nodes parameters using artificial intelligence (AI) and especially machine learning (ML) algorithms. However, AI and ML bring along privacy and security concerns. Therefore, user data must be protected from unauthorized persons during the 6G edge network recovery and expansion phases. In this paper, we proposed a new reliable Digital Twin-based 6G edge network recovery framework using Blockchain technology. We applied the Transfer Learning (TL) technique to improve our proposed framework’s performance. We ensured data privacy and security using TL and Blockchain.

Aeronautical Ad-hoc Networks (AANET) is a fairly new concept that connects airplanes via wireless air-to-air links, allowing passengers to access the Internet during a flight. The unstable air-to-air link characteristics and ultra-dynamic topology become the main differences between AANETs and usual ad-hoc architectures. To handle these differences, the AANET topology could be created in the form of clusters by collecting airplanes having similar features under the same set. However, it is also difficult to sustain the cluster topologies since ultra-dynamic characteristics still affect them. Therefore, the current cluster topology must be continuously mapped to the airplanes to notify them as a part of sustainability. If we do not ensure the sustainability of the clusters, the packet transfer success of AANET is decreased with higher end-to-end latency. At that point, to solve this aircraft notification problem and map the current cluster topology to them at each timestamp, in this paper, we propose a Gated Recurrent Unit (GRU)-based topology mapping mechanism for AANETs. Here, the GRU can continuously notify the airplanes at each timestamp about topology changes. Therefore, airplanes can forget the old topology when it changes. Otherwise, the topology taken from the previous timestamp is continuously remembered if it does not change. Finally, the performance evaluations reveal that the GRU-aided topology mapping can achieve roughly 42% higher packet delivery ratio with 34% reduced end-to-end latency.

Complete topology discovery is the most important MAC service in broadband networks but it holds spatial and temporal complexities for the network-wide, i.e in the data link layer it requires considerable time amount to update all link status management information. Moreover, many service providers are complaining about high operational time and resource usage in the complete topology discovery process. Additionaly, consuming high resources leads to a huge amount of management traffic on the links. At this point, partial topology discovery arises as an alternative solution as a more efficient MAC service for the next generation broadband networks to reduce complexities and maintain smooth functioning. However, manual execution of partial topology discovery leads to risky situations for the network arising from human intervention. Therefore, in this paper, we propose an AI-driven partial topology discovery approach to maintain a global MAC service which serves both physical and virtual connections in a broadband network. Besides, with this approach, we not only preserve the network resources but also have the ability of forecasting the device-based network statistics. For this aim, we use Hidden Markov Model in order to estimate the path to be discovered regarding the arrived log patterns of the devices. Thanks to the partial path estimation, we eliminate the usage of every node in the discovery and achieve up-to-date topology information more rapidly. Consequently, according to our simulations, we succeed in a significant reduction in the number of nodes used by 60%, required time to have up-to-date topology by 35%. And finally, as a consequence of using less amount of nodes, we reduce the management traffic on the links on average 50%.

Today, it is hard to assume the difficulties of providing network communication over environments that include changeable and mobile nodes. In that kind of situation, traditional applications of cellular networks do not keep up with this unpredictable and mobile environment. Drones are the most preferred technologies that are useful with their characteristic features for that kind of environment. Our goal is to provide network connectivity by placing drones without including any terrestrial base stations in this paper. To set and optimize the number of used drones in related environments, we propose a heuristic method. Finding 3-D placement with the optimum number of used drones, we aim to serve and cover the target percentage of users. And to verify our optimization algorithm, which provides the 3-D placements of drones, we use a simulation tool ns-3 to create network environments that include mobile users and aerial base stations. Our simulation results are based on users' network requirements. We verify that our optimization approach with the heuristic method satisfies the users according to their QoS needs.

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the internet of things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defined networks and sensor networks. In this paper, we propose a network-aware automated machine learning (AutoML) framework, which detects DDoS attacks in software-defined sensor networks. Our framework selects an ideal machine learning algorithm to detect DDoS attacks in network-constrained environments, using the metrics such as variable traffic load, heterogeneous traffic rate, and detection time while preventing over-fitting. Our contributions are two-fold: (i) we first investigate the trade-off between the efficiency of ML algorithms and network/traffic state in the scope of DDoS detection. (ii) we design and implement a software architecture containing open-source network tools, with the deployment of multiple ML algorithms. Lastly, we show that under the denial of service attacks, our framework ensures the traffic packets are still delivered within the network with additional delays.

Forecasting the users’ movements and behaviors is extremely valuable for early warning systems to provide high-quality service in wireless and cellular networks. However, forecasting the service of the specific network devices with the additional knowledge of user behaviors is underexplored. This study proposes a WiFi6-specific QoS forecasting engine, which uses a spatio-temporal graph approach to predict QoS parameters, e.g., throughput, in terms of user position in WiFi6 networks. Since WiFi6 networks are planning to meet various traffic types with dense users, it is crucial to analyze it in both spatial and temporal manner by preserving graph-structured data. In this study, we modeled the problem with a novel deep learning approach, Graph Convolution Networks (GCNs), by adapting the Omni-Scale 1D CNN for temporal analysis. Then, we analyze the forecasting performance with two datasets in terms of variety error metrics over loss rate, link speed, throughput, and round trip time (RTT). Also, we give the baselines, ARIMA, FARIMA, SVR, and RNN to compare the proposed solution in terms of accuracy. Finally, we present the simulation results to compare the proposed QoS forecasting approach with user mobility forecasting. All experiments show that proposed WiFi6-specific QoS forecasting gives superior results for multi-horizon QoS prediction with respect to user positions considering heterogeneous traffic types.

In Software Defined-Content Delivery Networks (SD-CDN), the policies of tenants such as Youtube, Netflix, Office 365, etc. are not the same due to having different 5G traffic requirements for such contents as enhanced Mobile Broadband (eMBB), ultra-reliable low-latency (URLLC) and massive machine-type communication (mMTC). This leads SD-CDN multi-tenant slicing to provide different services with limited network resources, where each tenant can functionally manage their own virtual slice of a physical component according to service level agreements (SLAs). However, they are not permitted to dynamically configure their own components. Therefore, the physical end-to-end configuration of all edge devices causes extra hardware and bandwidth costs. Although software as a service (SaaS) is more preferred to handle cost-efficiency on a switch configuration that increases forwarding throughput (Mbps) with a less number of physical components in SD-CDN, the edge devices can be only served as infrastructure as a service (IaaS) currently. This motivation leads us to serve the switch as a service that includes both IaaS and SaaS characteristics. Therefore, we propose an OpenFlow as a service (OFaaS) design where each tenant has flow management and switch configuration permissions on their own virtual slice. In flow management, we define a novel Service Oriented Architecture (SOA) to orchestrate OFaaS driven topology by isolating each tenant from physical complexity. Here, each tenant can dynamically alter QoS on a flow and load balance between a sub-set of contents via OpenFlow protocol. In switch configuration; a novel OFaaS Management Algorithm for a multi-tenant slicing increases the number of tenants served per OpenFlow switch thanks to OFaaS design. It enables an end-to-end configuration via the NETCONF protocol with a novel YANG model of OFaaS. According to performance evaluation; OFaaS has the same forwarding throughput as conventional IaaS based OpenFlow switch for a homogenous content, whereas it has 71% more forwarding throughput (Mbps) and it has 40% more cost-efficient than conventional one for a heterogeneous content with $17 savings per tenant.

Wireless RF energy transfer for indoor sensors is an emerging paradigm ensuring continuous operation without battery limitations. However, high power radiation within ISM band interferes with packet reception for existing WiFi devices. The paper proposes the first effort in merging RF energy transfer within a standards compliant 802.11 protocol, realizing practical and WiFi-friendly Energy Delivery with Mobile Transmitters (WiFED Mobile). WiFED Mobile architecture is composed of a centralized controller coordinating the actions of multiple energy transmitters (ETs), and deployed sensors that periodically requires charging. The paper first describes 802.11 supported protocol features that can be exploited by sensors to request energy and for ETs to participate in energy transfer. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of ETs to sensors for efficient energy delivery. Thirdly, it detects outlier sensors (OS), which have limited power reception from static ETs and utilizes mobile ETs (METs) to satisfy their charging cycles. The proposed in-band and protocol supported coexistence in WiFED Mobile is validated via simulations and partly in a software defined radio testbed, showing that METs reduce latency by 42% and improve throughput by 83% in scenarios where using only static ETs fails to satisfy charging cycles of OS.

With the excessive demand on the mobile and wireless usage, IEEE 802.11ax is released to enable concurrent transmissions in the dense WLANs, enhanced with the Spatial Reuse (SR) techniques. However, IEEE 802.11ax officially has not adaptive physical carrier sensing mechanism to be used with the SR techniques. The lack of adaptive carrier sensing leads to hidden/exposed nodes problems in dense networks. Also, it induces unfair access between the stations. Therefore, thorough the study, we try to solve how to design an adaptive carrier sensing mechanism to balance hidden/exposed problems and increase fairness for all stations in the dense WLANs. Consequently, we propose a two-scale Fair Sensitivity Control (FSC), which operates with both local-scale and global-scale to adjust the Carrier Sensitivity Threshold (CST) for stations. In contrast to other studies, we use local scale control to adaptively adjust CST to decrease interference and global-scale control using Artificial Intelligence (AI) to decrease the fairness issue caused by stations' placements. Thanks to the learning capabilities of the AI, specifically Multilayer Perceptron (MLP), that we have implemented in this work, the WLANs learn the particular carrier sensing parameter, called Margin for stations. We evaluate the FSC mechanism under the five metrics: i) aggregated throughput, (ii) number of collisions, (iii) number of hidden terminals, (iv) number of exposed terminals, and (v) fairness. Simulation results show a clear performance improvement over the five metrics compared with the current state of the art.

Although Content Delivery Networks (CDN) do their best to quickly deliver the contents, Internet Service Providers (ISP) and network conditions cause unexpected changes on the routing paths. Hence, it gets difficult for CDNs to adhere to the delay promises within the Service Level Agreements. This paper presents a jitter-free overlay routing framework for CDNs, which struggle to adapt timely content delivery to end users due to the dynamicity of the Internet. The Nearest Origin-Shield (NOS) framework which we propose in this paper overcomes this issue by creating an overlay network on top of the existing physical ISP networks and dynamically determining the fastest routes to the content sources (Origins) on this overlay network. NOS keeps track of changes in underlay network through periodically measuring the end-to-end routing paths delays via Helper Modules. Based on the underlay network delay measures and the load (i.e., CPU, disk I/O and network usage) of the overlay network entities, NOS Central Module determines the fastest routes to the Origins using our novel Delay-Aware and Jitter-Free Overlay Routing Algorithm. Hence making it possible for CDNs to provide jitter-free and timely content deliveries meanwhile ensuring the cache servers not to be overloaded. We created a new testbed for performance evaluations with real CDN servers, Origin accounts and measurements. Results show that, NOS provides shorter, more stable and jitter-free routing paths with jitter gains up to 98 % and improves the Hit ratio by 0.74%.

Wireless RF energy transfer for indoor sensors is an emerging paradigm that ensures continuous operation without battery limitations. However, high power radiation within the ISM band interferes with the packet reception for existing WiFi devices. The paper proposes the first effort in merging the RF energy transfer functions within a standards compliant 802.11 protocol to realize practical and WiFi-friendly Energy Delivery (WiFED). The WiFED architecture is composed of a centralized controller that coordinates the actions of multiple distributed energy transmitters (ETs), and a number of deployed sensors that periodically request energy from the ETs. The paper first describes the specific 802.11 supported protocol features that can be exploited by sensors to request energy and for the ETs to participate in the energy delivery process. Second, it devises a controller-driven bipartite matching-based algorithmic solution that assigns the appropriate number of ETs to energy requesting sensors for an efficient energy transfer process. The proposed in-band and protocol supported coexistence in WiFED is validated via simulations and partly in a software defined radio testbed, showing 15% improvement in network lifetime and 31% reduction in the charging delay compared to the classical nearest distance-based charging schemes that do not anticipate future energy needs of the sensors and are not designed to co-exist with WiFi systems.

Unmanned Aerial Vehicular (UAV) networks extend wireless access for devices without infrastructure coverage, and also help establish a connectivity backbone during military reconnaissance and disaster events. This paper focuses on the design of a resilient end-to-end connectivity paradigm under unique architectural and scenario assumptions. First, the UAVs themselves are equipped with multiple interfaces that use standardized protocols, with associated variation in data throughout, range, and bit error rates. Second, there may be adversarial agents seeking to disrupt connectivity through targeted jamming in 3D spaces. Third, we assume an overlay software defined control plane, where the UAVs function as software switches, able to execute forwarding commands and determine preferred routes under controller directives. Our proposed approach devises metrics that influence the choice of the wireless interface and weights edges formed between UAV pairs. Further, it also uses a multi-layer graph model and creates maximally separated paths in 3D space to ensure resiliency to jamming. Simulation results conducted for urban scenarios reveal 34% improvement in enhanced resiliency for end-to-end outages by trading off 12% increase in latency over competing approaches.

In wireless multimedia sensor systems (WMSSs), the devices are equipped with multiple energy‐constrained camera sensors (CSs) distributed over bandwidth‐constrained and lossy wireless channels, in catastrophe‐prone areas. Meanwhile, multimedia applications, e.g. video streaming, require considerable energy and bandwidth resources to gain long lifetime and high streaming quality. This study proposes an energy, bandwidth, and quality (EBQ) optimisation framework for green two‐tiered WMSSs. The first tier contains the CSs and the second tier includes cluster heads (CHs) selected from the CSs with higher available energy and processing capacity. In the EBQ optimisation framework, a rate allocation optimisation problem is formulated under given constraints of available backhaul bandwidth of the CHs and quality of received videos at base stations (BSs). This problem is solved for optimal encoding rates to packetise each video captured from different environments into multiple descriptions for transmission. Consequently, the average energy consumption per CS is minimised for long lifetime while conserving the bandwidth of the CHs and guaranteeing high quality of received videos for the purpose of monitoring at the BSs. Simulations demonstrate that the proposed EBQ optimisation framework can efficiently enhance the performance of green two‐tiered WMSSs in terms of minimum energy consumption, bandwidth efficiency, and high quality.

The tremendous increase on mobile data traffic has stressed conventional cellular networks recently. In order to handle this rapid increase, Software Defined Networking (SDN) is proposed as one of the novel approach that makes it easier to orchestrate physical devices in Data Plane with its centralized control fashion. On one hand, SDN provides scalability and flexibility on network management with dummy OpenFlow(OF) switches and its nature of centralized authority; on the other hand, these properties cause spatial and temporal complexity in the Data Plane. Spatial complexity, described as Memory Usage in an OF switch, should be minimized by removing redundancy on OF switch flow table pipeline. Temporal complexity, defined as Flow Forwarding Delay, should also be reduced by lowering number of comparison in OF switch pipeline to enhance Quality of Service (QoS) of a flow. Therefore, in this paper, a novel Multi-Stage OF (MsOF) Switch model is proposed and examined considering Queuing Theory in the light of spatial complexity and temporal complexity parameters. According to performance evaluation results, specially in urban areas, MsOF has much less spatial and temporal complexity considering conventional OF switch model as the number of input ports in an OF switch (N) increases. MsOF gains much memory space by deploying more tables with less memories, total of (n · k). Furthermore, with such a Multi-Stage deployment of flow tables, MsOF provides approximately 7 times less flow forwarding delay, compared with a conventional OF Switch for a network load more than %60.

The deployment of Self-Organizing Networks (SONs) based architectures has emerged as one of the key points in the 3GPP LTE-Advanced Standard, which aims to embed auto-management skills into the next generation mobile networks. However, the high traffic demands and the increased number of nomadic users have led dense eNodeB coverage, thus challenging the SON management in terms of energy efficiency. Considering these crucial SON challenges, we propose a novel adaptive network coverage model for energy-efficient SONs using a special spatial optimization method. This novel method is based on the Voronoi diagram optimization to provide the minimum number of active eNodeBs for high energy saving. The proposed model mathematically analyzes all the operating eNodeBs deployed in a specific SON area in terms of the utilization, by identifying them by a two-parameter function. These are the spatial coordinates and the utilization of the eNodeB. This eNodeB-specific mathematical model leads to find the redundant eNodeBs with less utilization, deactivate them and rearrange the coverage area with the remaining active eNodeBs using the Voronoi specific optimization. This optimization is solved by a novel heuristic with the aid of a parameter called assignment factor, in order to maximize the utilization for the remaining active eNodeBs in the green SON architecture. This spatial optimization based algorithm aims to adaptively deploy energy-effective cell coverage. The thorough evaluation results prove the generic energy-efficiency of the proposed adaptive coverage algorithm while maintaining the ENodeB utilization above the satisfying QoS levels.

The tremendous increase in the mobile data traffic has led the network operators to use offloading solutions in Radio Access Technologies (RATs) such as WiFi or smallcells. However, these solutions are not efficient since none of them controls the offloading from a centralized global network view. Current offloading methods are mostly user-based and they do not completely consider the user satisfaction, and more significantly they do not consider the Quality of Service (QoS) of the whole network system. These aforementioned motivations have led us to design a software based orchestration model for Heterogeneous Networks (HetNets) which is provided by our SDoff framework in a Software-Defined Network (SDN) fashion. This paper presents a novel SDN-based control of smallcell offloading in order to enhance the satisfaction of users and QoS of the overall system by considering a dissatisfaction parameter (ψ). The proposed framework analyzes the distance of each user to the all HetNet base stations in the topology by considering different type of users. After that, the offloading decision algorithm resolves which user must be offloaded to which base station by identifying the most dissatisfied user in the topology according to (ψ), by evaluating the QoS matrix and user types. Moreover, a new beacon format is proposed in order to maintain a standardized communication between the SDoff plane and virtual representatives (Avatars) of the Data Plane elements. We showed the overall system dissatisfaction difference between SDoff controlled offloading and on-the-spot offloading in the evaluation.

Smallcell technology is gaining significance as part of the next-generation cellular systems due to their performance benefits in terms of increased network capacity and improved indoor and local coverage. Hybrid access smallcells, which provide service to both indoor as well as neighboring users, adopt adhoc policies to guarantee performance benefits to indoor home users in the presence of external neighboring users. Such policies must be able to stabilize user queues as well as to provision performance benefits in terms of delay and throughput, especially for the indoor users. As a result, classification of user data in terms of traffic type and user type is required to effectively achieve the differentiated QoS performance. In this paper, a traffic-aware utility function is proposed, which takes into account for the user's priority index and traffic characteristics to efficiently provide differentiated QoS benefits to users served under an OFDMA hybrid smallcell. The problem of the traffic-aware utility based scheduling under power constraints is posed as an optimization objective and an optimal algorithm for the scheduling problem is presented. The results show that the proposed scheme achieves QoS performance benefits in terms of throughput and delay.

The deployment of Heterogeneous Networks (Het-Nets), specifically the smallcell networks ranging from picocells, microcells and femtocells, have been increased dramatically with the proliferation of next generation wireless devices and broad traffic demands. Embedding the promising Cognitive Radio (CR) technology into the smallcells have brought complementary increases in spectrum utilization as well as the topological flexibility. This paper deals with the topological control of such CR based smallcell networks by considering the spectrum utilization, packet losses, delay and jitter parameters. The proposed framework analytically models the spectral topology assignment taking into account the analysis of the CR user requests and decides to assign the proper spectrum to the CR smallcell users by dividing the topology in an optimal manner. This optimality is achieved by taking into account different Quality of Service (QoS) parameters like throughput, delay, jitter and packet losses. The proposed frameworks' mathematical model also provides optimal QoS parameter values for more efficient and fair topology assignment, considering the heterogeneous background CR traffic in the surrounding. The proposed method is evaluated for different types of CR traffics and thorough simulation results show that the proposed topology control mechanism provides an optimum number of smallcell CR networks to maintain fair and efficient QoS requirements in the unlicensed bands.

In Cognitive Radio (CR) Networks, the licensed but vacant spectrum bands are shared by the unlicensed users (CR users) in an opportunistic manner. The CR users should operate and cohabit in the licensed bands without causing any interference to the Primary Users (PUs). This CR user cohabitation which is managed by a spectrum coordinator, enables several design challenges. Therefore, A User Cohabitation Coordinator, UCC, should be designed considering the heterogeneous Quality of Service (QoS) requirements for the CR users and the short-term fluctuations in the available licensed spectrum bands. Moreover, the spectrum coordination among the CR network operators for CR users should also be considered by the UCC for an effective and fair spectrum sharing. Considering these challenges, the main contribution of this paper is to design a QoS-based spectrum coordinator for CR user cohabitation in order to achieve high throughput and fairness. The proposed UCC uses the first-difference filter clustering and correlation based PU modeling to integrate the fluctuations of the PU activities into the spectrum sharing. The UCC characterizes the QoS requirements of CR users by adopting queuing theoretic models. The proposed scheme enables the cohabitation of the CR operators dynamically. The evaluations demonstrate that the proposed UCC provides high throughput while maintaining the fairness in the CR networks.

In Cognitive Radio (CR) networks, the accurate information gathering for the channel availability brings some major challenges. It is seen that the availability should be classified effectively and opportunistically considering the channel utilization levels of the licensed users. Moreover, local spectrum monitoring mechanisms should also be used for accurate determination of the channel availability. Considering these challenges, in this paper, an estimation and correlation based scheme is proposed to build the availability map of the licensed channels according to the utilization requirements of the CR users. The proposed scheme is aided by a monitoring network for more accurate and robust detection of the available spectrum. The monitoring network is only responsible of sensing the channels and estimating the availability using auto-regression analysis. The monitoring nodes disseminate the estimations to the centralized entity, who builds the availability map for the channels to be used by the CR users. This classification map is prepared by considering the characteristics of PU channels and it is based on the cross-correlation analysis of the estimations by the monitoring nodes. The evaluations show that the proposed estimation and correlation based scheme improves the estimation accuracy of the licensed channels as well as the overall utilization performance compared to the fully-cooperative traditional scheme.

Two-tier femtocell-based networks have been proposed as an economic solution to improve coverage and capacity in wireless cellular systems. Although widely studied in the literature, interference management in these networks remains as a technical challenge in need of effective solutions. In particular, the interference estimation is a relevant portion of the problem that enables correct operation of interference management schemes relying on this information. In this paper, a novel downlink cross-tier interference estimation approach is proposed based on spatio-temporal correlation techniques. An ordinary Kriging interpolator using Semivariogram Analysis is applied to the interfering signal followed by an autorregressive model. The signal estimation at the interfered users' location is exploited at the radio resource manager of the femtocell or macrocell base station by formulating a resource allocation problem that is solved by means of a heuristic algorithm. A practical procedure implementing this scheme in the network is also proposed. Numerical results show how performance of cross-tier interference management approaches can be optimized by implementing this idea.

In many recent studies on cognitive radio (CR) networks, the primary user activity is assumed to follow the Poisson traffic model with exponentially distributed interarrivals. The Poisson modeling may lead to cases where primary user activities are modeled as smooth and burst-free traffic. As a result, this may cause the cognitive radio users to miss some available but unutilized spectrum, leading to lower throughput and high false-alarm probabilities. The main contribution of this paper is to propose a novel model to parametrize the primary user traffic in a more efficient and accurate way in order to overcome the drawbacks of the Poisson modeling. The proposed model makes this possible by arranging the first-difference filtered and correlated primary user data into clusters. In this paper, a new metric called the Primary User Activity Index, , is introduced, which accounts for the relation between the cluster filter output and correlation statistics. The performance of the proposed model is evaluated by means of traffic estimation accuracy, false-alarm probabilities while keeping the detection probability of primary users at a constant value. Simulation results show that the appropriate selection of the Primary User Activity Index, higher primary-user detection accuracy, reduced false-alarm probabilities, and higher throughput can be achieved by the proposed model.

The growing problem of spectrum scarcity and the inefficient spectrum utilization in the licensed bands, are addressed by the emerging Cognitive Radio (CR) paradigm. It is seen that the choice of the spectrum bands, called as spectrum decision, must be organized carefully by considering the challenges in the spectrum availability over time, the short term fluctuations in the availability, and the heterogeneous Quality of Service (QoS) requirements of the cognitive radio users. Taking into account these challenges, the main contribution of this paper is to design a QoS-aware spectrum decision framework that achieves higher throughput and fairness in CR networks. The available spectrum fluctuations are characterized by using queueing theoretic models and are parametrized by a novel QoS parameter called opportunity index, Ψ. The heterogeneous QoS requirements of CR users are classified by defining another novel QoS parameter called request index, κ. An admission control algorithm is designed to stabilize the heterogeneous QoS requirements according to Ψ and κ. A spectrum decision algorithm is developed to select most appropriate spectrum bands considering the stabilized QoS requirements and the characterized spectrum. Moreover, by using a novel spectrum mobility algorithm the available spectrum is continuously monitored for dynamic variations in the CR network. The simulations demonstrate that our QoS-aware spectrum characterization and decision framework maximizes the total throughput while maintaining the fairness.