Orthogonal multiple access assigns disjoint time-frequency resources and limits reuse in dense 6G settings. Non orthogonal multiple access mitigates this by superposing users and separating streams with successive interference cancellation, but performance is sensitive to SIC errors and channel uncertainty. Many RIS-NOMA studies assume perfect channels and ideal SIC, ignore feedback delay and noise, and restrict RIS to diagonal control, while the wideband joint design of base-station precoding, RIS configuration, and inter/intra-cluster power is nonconvex. In this research we address these gaps with a wideband OFDM downlink that employs beyond diagonal RIS with two-bit eigen-phase quantization, imperfect feedback (delay, noise, random phase flips), MMSE precoding from estimated cluster representatives, and an explicit per-subcarrier SIC feasibility rule. We develop OFE-REDQ, a reinforcement-learning agent that combines an optimized feature encoder with a randomized ensemble of critics and a deterministic actor to jointly control RIS settings, inter-cluster power shares, and intra-cluster splits. Stability is ensured by Polyak target updates, subset-minimum bootstrapping, and target-value clipping. In 3GPP TR 38.901 CDL-D UMi simulations, OFE-REDQ achieves higher sum rate, faster and more stable convergence, and higher SIC success than DDPG and vanilla REDQ, indicating suitability for practical RIS-aided NOMA under realistic hardware and feedback constraints.

Modern network environments generate vast streams of complex and evolving traffic data, presenting significant challenges for accurate and real-time intrusion detection. Conventional deep learning approaches often fail to effectively capture the multi-scale temporal patterns and are frequently hampered by severe class imbalance and catastrophic forgetting when faced with non-stationary data streams. To overcome these limitations, this paper introduces a novel Multi-scale Attention Fusion (MAF) module, a general-purpose architectural enhancement for transformer-based models designed to achieve synergistic optimization across three critical dimensions: computational efficiency, continual learning adaptability, and multi-scale temporal perception. We present two instantiations of this approach: MEGA+MAF and FNet+MAF . These models synergistically combine short-term local context, long-range dependencies, and global sequence information through an adaptive, learnable gating mechanism. A comprehensive evaluation across four diverse benchmark datasets— ToN-IoT, X-IIoTID, CICEVS2024 , and CSE-CIC-IDS2018 —demonstrates state-of-the-art performance with balanced optimization across all three dimensions: (1) FNet+MAF achieved superior computational efficiency with up to 8.5 × lower memory and 5.3 × faster inference while maintaining high accuracy; (2) MEGA+MAF demonstrated exceptional continual learning capability, achieving 99.10% accuracy in dynamic streaming environments while effectively eliminating backward forgetting ( 0.00% ) and minimizing forward forgetting ( 0.06% ); and (3) Both models exhibited robust multi-scale perception, capturing threats across short-term bursts, mid-range sessions, and global traffic patterns with up to 99.97% F1-score. Our ablation study after 20 epochs of training identifies 80 tokens as optimal, achieving 85.20% accuracy with 145.1 samples/second throughput. Interpretability analyses further confirm that the models learn robust and semantically meaningful feature representations aligned with network security semantics. The proposed framework represents a significant advancement toward building adaptive, next-generation intrusion detection systems capable of evolving with emerging threats while maintaining operational efficiency in resource-constrained environments.

The rapid growth of multimedia applications, cloud services and machine to machine communications has created dynamic and unpredictable traffic patterns in mobile networks. Understanding these patterns and reacting quickly to anomalies are essential for maintaining service quality and security. Digital Twins (DTs) are emerging as an enabler technology for network emulation, offering a virtual environment for what-if case scenario planning, impact analysis and closed-loop control. In this paper, we propose a Network Digital Twin (NDT) framework that simultaneously forecasts key network traffic operational indicators, the ratio of TCP to UDP packets and the ratio of TCP to UDP bytes and further we use the forecasting errors to detect anomalies in near real-time. Our proposed twin integrates deep learning and ensemble models, provides an interactive interface for configuring observation windows, training cycles and prediction horizons, and supporting anomaly injection to test detection performance. We further enhance the framework by integrating inference with new data to evaluate excellent performance. Experimental findings show excellent prediction with errors RMSE and MAE values as low as 0.09-0.10 and anomaly detection performance above 80 %. These results demonstrate how network digital twins can move from prediction to protection, enabling proactive network resilience.

Recent advances in reinforcement learning (RL) have opened promising opportunities for autonomous drone navigation. However, bridging RL-based methods with high-fidelity simulation environments remains an open challenge. In this paper, we introduce a novel multi-task approach for real-time RL-based autonomous drone navigation inside Unreal Engine 5. The idea is simple but effective: by using a physically accurate drone model and systematically increasing the complexity of simulated scenarios — from predefined path following to dynamic visual tracking with advanced sensing modalities — we achieve impressive generalization to multiple dynamically changing tasks. Our method addresses critical gaps in drone autonomy research, such as obstacle avoidance in swarm coordination and robust visual target tracking under varying environmental conditions. Through comprehensive experimental evaluation on pedestrian detection data in an urban scenario, we highlight the essential factors influencing drone learning performance. Our fine-tuned YOLOv8 model improves pedestrian detection recall from 72.41% to 84.71% on synthetic aerial images, demonstrating the effectiveness of drone-collected data for domain adaptation. https://mmlab-cv.github.io/DroneAgents/

NET4EXA aims to develop a next-generation high-performance interconnect for HPC and AI systems, addressing the increasing demands of large-scale infrastructures, such as those required for training Large Language Models. Building upon the proven BXI (Bull eXascale Interconnect) European technology used in TOP15 supercomputers, NET4EXA will deliver the new BXI release, BXIv3, a complete hardware and software interconnect solution, including switch and network interface components. The project will integrate a fully functional pilot system at TRL 8, ready for deployment into upcoming exascale and post-exascale systems from 2025 onward. Leveraging prior research from European initiatives like RED-SEA, the previous achievements of consortium partners and over 20 years of expertise from BULL, NET4EXA also lays the groundwork for the future generation of BXI, BXIv4, providing analysis and preliminary design. The project will use a hybrid development and co-design approach, combining commercial switch technology with custom IP and FPGA-based NICs. Performances of NET4EXA BXIv3 interconnect will be evaluated using a broad portfolio of benchmarks, scientific scalable applications, and AI workloads.

The HORSE (Holistic, Omnipresent, Resilient Services for future 6G wireless and computing Ecosystems) framework integrates AI to address the complexity and vulnerabilities of next-generation networks. This paper focuses on an LLM-agent-based subsystem of HORSE that automates the creation and enforcement of security mitigation actions. This subsystem utilizes a specialized Knowledge Base enriched with threat-mitigation pairs from trusted sources like MITRE ATT&CK, through a collaborative workflow between an LLM and human expertise. LLMs translate high-level mitigation actions into executable Ansible commands, while a feedback-driven workflow, powered by LLM agents, ensures accuracy and reliability. One LLM agent (the "translator") generates the commands, while another (the "executor") executes them and provides feedback to the translator in case of unsuccessful execution, allowing the translator to refine the commands for a retry. Experiments with open-source LLMs (like Llama 2 and Falcon) demonstrate rates of up to 73% in execution success, and high accuracy in translating mitigation intentions. This approach automates threat mitigation, reduces human intervention, and scales effectively for the dynamic security demands of 6G networks. Future work will focus on enhancing LLM capabilities for complex environments and improving adaptability in cybersecurity operations.

In this research study, a Reconfigurable Intelligent Surface (RIS)–assisted Multiple-Input Single-Output (MISO) downlink wireless communication system is implemented in three-dimensional (3D) space. Each entity—base station (BS), RIS, and user equipments (UEs)—has its own geographical coordinates (X, Y, Z). The distances between the fixed entities (BS and RIS) and the randomly located UEs are computed using the Euclidean norm. The BS–RIS and RIS–UEs links are modeled as rician fading channels, whereas the BS–UEs links are modeled as rayleigh fading channels. Joint beamforming at the BS and phase shift at the RIS is formulated to provide a better spectral efficiency to the UEs. To achieve this, the entire communication system is translated into a Reinforcement Learning (RL) framework with state, action, and reward. A Randomized Ensembled Double Q-Learning (REDQ) RL agent, which belongs to the model-free, off-policy RL family, is trained on this RL framework. REDQ has produced a better average spectral efficiency compared to other model-free, off-policy RL agents such as Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft-Actor Critic (SAC) in all experimental setups (signal power and RIS elements). This improvements is attributed to REDQ’s better update-to-data (UTD) ratio, ensemble of critics, and controlled Q-function variance with a common target value for all critics. These findings underscore the potential of REDQ in enhancing performance in RIS-assisted wireless communication systems.

As we evolve towards more heterogeneous and cutting-edge mobile networks, Network Digital Twins (NDTs) are proving to be a promising paradigm in solving challenges faced by network operators, as they give a possibility of replicating the physical network operations and testing scenarios separately without interfering with the live network. However, with mobile networks becoming increasingly dynamic and heterogeneous due to massive device connectivity, replicating traffic and having NDTs synchronized in real-time with the physical network remains a challenge, thus necessitating the need to develop real-time adaptive mechanisms to bridge this gap. In this part II of our work, we implement a novel framework that integrates an adaptive Proportional–Integral–Derivative (PID) controller to dynamically improve synchronization. Additionally, through an interactive user interface, results of our enhanced approach demonstrate an improvement in real-time traffic synchronization.

In recent years, network slicing has embraced artificial intelligence (AI) models to manage the growing complexity of communication networks. In such a situation, AI-driven zerotouch network automation should present a high degree of flexibility and viability, especially when deployed in live production networks. However, centralized controllers suffer from high data communication overhead due to the vast amount of user data, and most network slices are reluctant to share private data. In federated learning systems, selecting trustworthy clients to participate in training is critical for ensuring system performance and reliability. The present paper proposes a new approach to client selection by leveraging an XAI method to guarantee scalable and fast operation of federated learning based analytic engines that implement slice-level resource provisioning at the RAN-Edge in a non-IID scenario. Attributions from XAI are used to guide the selection of devices participating in training. This approach enhances network trustworthiness for users and addresses the black-box nature of neural network models. The simulations conducted outperformed the standard approach in terms of both convergence time and computational cost, while also demonstrating high scalability.

The optimization of Precoding Matrix Indicators (PMIs) is crucial for enhancing the performance of 5G networks, particularly in dense deployments where inter-cell interference is a significant challenge. Some approaches have leveraged Artificial Intelligence (AI)/Machine Learning (ML) techniques for beamforming and beam selection, however, these methods often overlook the multi-objective nature of PMI selection, which requires balancing Spectral Efficiency (SE) and interference reduction. This paper proposes an interference-aware PMI selection method using an Advantage Actor-Critic (A2C) reinforcement learning model, designed for deployment within an Open Radio Access Network (O-RAN) framework as an xApp. The proposed model prioritizes User Equipment (UE) based on a novel strategy and adjusts PMI values accordingly, with interference management and efficient resource utilization. Experimental results in an O-RAN environment demonstrates the efficacy of this method in improving network performance metrics, including SE and interference mitigation.

A deep learning model is presented for the cutting-edge wireless communication framework to ensure efficient channel estimation for a drone operating at sub-terahertz frequencies by leveraging Flying-Reconfigurable Intelligent Surfaces (Flying-RIS) in conjunction with a ground Base Station. Terahertz (THz) frequencies are envisioned as key enablers for different 6G applications such as ultra-broadband, but this technology still has its own limitations, for instance, high path loss and limited signal wavelength. Channel estimation is an essential component at such high frequencies to enhance overall system performance. To address this problem, we have employed multi-layer Long Short-Term Memory (LSTM) to handle temporal dependencies and OFDM channel complexities. The performance of our proposed approach is tested on different evaluation parameters in comparison with deep learning models including gated recurrent unit (GRU), convolutional neural networks (CNN), and fully connected networks (FCN). The findings for our proposed multi-layer LSTM are better than those of all other deep learning models across all tested parameters, proving its effectiveness in dynamic wireless communications operating at high frequencies.

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the “look-back period” and “forecast horizon” significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network.

Intrusion Detection Systems (IDSs) are evolving to utilize machine learning techniques more frequently, in order to effectively and reliably identify even attacks with small footprints on the network traffic. This paper presents a detailed evaluation of two advanced graph neural network models, D-GSAGE-MARC and GFN-GA, for intrusion detection across a diverse range of IoT and cybersecurity datasets, including CIC-ToN-IoT, NF-UQ-NIDS, WUSTL-IIOT-2021, InSDN, etc. By integrating multi-head attention mechanisms and Graph Attention Network (GAT) layers into the D-GSAGE-MARC model, we effectively capture complex relationships within graph-structured data while leveraging residual connections to enhance performance. Our comprehensive analysis employs multiple performance metrics to assess both models in multi-class and binary classification scenarios, highlighting their capabilities and shortcomings in identifying different types of cyber-attacks. The results show that the D-GSAGE-MARC model achieves remarkable performance, achieving an accuracy of 99.97% recall of 99.97%, and an F1 score of 99.97% on the WUSTL-IIOT-2021 dataset, establishing it as a highly effective solution for intrusion detection. Meanwhile, GFN-GA excels in detecting frequent threats. Additionally, we visualize the learned embeddings using Uniform Manifold Approximation and Projection (UMAP) techniques to elucidate feature representations utilized during classification. The results highlight the models’ stability and adaptability across different datasets, particularly in addressing imbalanced data and rare attack detection.

The simulation of crowds is complex and challenging. Every individual in a crowd exhibits a different behaviour, targets a different goal, and undergoes different types of interactions. Within crowds, groups can be identified in both static and dynamic configurations, with varying levels of responsiveness, leading to the emergence of complex avoidance mechanisms. In the past, rule-based models have been proposed to simulate crowds, unlocking the potential for large-scale simulations. Over the years, learning-based solutions have been presented, achieving acceptable results despite the lack of high-quality ground truth data for training. While both rule-based and learning-based methods have recently been integrated into 3D simulation engines, they usually rely on navigation meshes or B-spline functions, hindering their generalization to open-world scenarios. In this work, we propose a reinforcement learning-based solution to learn meaningful crowd dynamics inside the Unreal Engine 3D engine, enabling massive and highly dynamic crowd simulations. We show how our proposed method makes it possible to simulate crowd setups that require complex dynamic routing mechanisms, which are otherwise hard to achieve using rule-based approaches or even deep learning-based methods. Our approach also al-lows us to easily collect large synthetic datasets that are both photorealistic and provide accurate ground truth data without the need for any manual annotation. Some demonstration videos are available at mmlab-cv.github.io/DynamicCrowdRouting; Code, complete experiments and analysis will be made available upon acceptance.

The emergence of 5G networks plays a crucial role in satisfying the diverse demands of the rapidly evolving telecommunications market. The introduction of Network Function Virtualization (NFV) and Software-Defined Networks (SDN) offers significant networking advantages by transforming hardware-dependent network functions into programmable and cost-efficient Virtual Network Functions (VNFs). However, for effective monitoring strategies and enforcing Service Level Agreements (SLAs), these virtualized networks must be made visible (observable) to service providers or network operators. Extensive research has been undertaken to address monitoring and analytics challenges as networks transition to virtualized environments. This paper presents the development and evaluation of a real-time monitoring solution for 5G Service-Based Architecture (SBA) by leveraging a Network Data Analytics Function (NWDAF) developed by the authors. This solution allows and demonstrates real-time monitoring of 5G networks via a standardized interface. In addition, NWDAF is extended to utilize Machine Learning (ML) models to predict Key Performance Indicators (KPIs) related to network traffic, enhancing the overall network analytics capabilities. Furthermore, the collected metrics from the 5G core are pushed into a Prometheus database, with Grafana providing a visual analytics tool for assessing network performance.

Network Softwarization revolutionizes infrastructure by introducing programmable network functions, leading to scalable, agile, and efficient networks. This innovation facilitates adaptable, secure Next Generation Networks, transforming connectivity. This study conducts a comparative analysis of SDN-based network functions utilizing three different controllers: Open vSwitch (OVS), OpenFlow Reference controller, and RYU, to understand their impact on packet loss and jitter. The OpenFlow Reference controller, primarily used for educational and basic experimental purposes, serves as a baseline. The OVS controller, operating within the Open vSwitch framework in Mininet, offers a practical level of network management capability, more advanced than the OpenFlow Reference controller yet not as comprehensive as the RYU Controller, a production-grade SDN controller. Our research aims to fill the gap in comparative performance analysis of these controllers, particularly in hybrid round-robin-based load balancing and firewall implementations. Recognizing the operational reality of SDN deployments, we explore both reactive and proactive modes, acknowledging that real-world applications like firewalls and load balancers often operate proactively with pre-populated rules, affecting data plane performance. The findings provide insights into each controller’s effectiveness, with OVS and RYU showing enhanced capability in reducing packet loss and jitter compared to the OpenFlow Reference controller. This research not only sheds light on the functional behaviors and performance trade-offs of these controllers but also contributes to the broader SDN and NFV discourse, offering a foundation for future academic and practical advancements in the field.

Traffic steering is an essential aspect of the radio access network (RAN). The O-RAN Alliance architectural framework provides an environment for intelligent traffic steering using different AI/ML techniques. In this work, we present an xApp in the RAN intelligence controller (RIC) for traffic steering and load balancing to ensure that the user equipment (UE) achieves an acceptable throughput. We present a K-means learning clustering technique on the UEs based on the throughput and quality of service metrics. The clustering technique is used to determine UEs experiencing low throughput for handover. Cell throughput prediction is then performed using long-short-term memory (LSTM) to predict the average cell throughput generated by the individual cells. A steering algorithm is developed to select UEs for handover. A Handover request message is generated and then sent to the E2 nodes. The achieved results demonstrate the effectiveness of our proposed algorithm with a significant gain in throughput and a fair distribution of UEs among cells.

Due to the increasing popularity of UAVs, and UAV applications, the need for accurate simulation tools is now greater than ever. Simulations allow for a fast, cheap, and most importantly, safe way to test new applications and protocols. However, due to their complexity, most of the existing UAV simulators only allow simulating either the UAV physics or their communication with a high accuracy. Yet, real applications require a tool that can simulate both. Hence, in this paper, we present a real time framework where our multi-UAV simulator ArduSim, and the popular network simulator OMNeT++, are combined to achieve an advanced co-simulation tool. We show that this co-simulation approach allows for a high accuracy in terms of both UAV physics, and communication between the UAVs, factors that have a clear impact on the performance of different protocols and applications that rely on UAV-to-UAV communications. Validation experiments show that the proposed co-simulation framework is able to perform adequately in real time under moderate workloads, even in standard desktop PCs.

Other than pure performance and cybersecurity, a value that is becoming increasingly important for a mobile network is its power consumption. In fact, the transition from legacy network deployments tightly coupled with the underlying hardware towards fully virtualized ones yields distinct options based on the adopted virtualization technology, each of which deserve appropriate evaluation in terms of energy efficiency. In this paper, we aim at providing a preliminary assessment of the realistic power consumption of a fifth-generation core network deployed in a network edge environment leveraging bare metal, containers, and virtual machines. The results are based on a testbed consisting of commercial off-the-shelf hardware and open-source software, and show that the deployment based on virtual machines is the first one that saturates the power consumption, thus reducing the maximum achievable throughput. These preliminary insights show the feasibility of a real-time power monitoring system that can condition the dynamic policies applied by the 5G network orchestrator.

Multi-access edge computing (MEC) represents an emerging solution to improve the performance of mobile networks by bringing computing resources closer to the edge of the network. However, MEC requires the implementation of virtualization and can be deployed using different hardware platforms, including COTS devices. In this highly heterogeneous scenario, the digital twin (DT), assisted by proper AI/ML solutions, is envisioned to play a crucial role in automated network management, operating as an intermediate and collaborative layer enabling the orchestration layer to better understand network behavior before making changes to the physical network. In this paper, we aim to develop a DT model that captures the behavior of a MEC node supporting services with varying workloads. In pursuit of this objective, we adopt a data-driven methodology that effectively learn a model predicting three critical key performance indicators (KPIs): throughput, computational load, and power consumption. To demonstrate the viability and potential of such approach, a measurement campaign is conducted on MEC nodes deployed with different virtualization environments (bare metal, virtual machine, and containerized), and the results are used to build the DT of each node. Furthermore, machine learning models, including k-nearest neighbors (KNN), support vector regression (SVR), and polynomial fitting (PF), are used to understand the amount of actual measurements required to achieve a suitably low KPI prediction error. The results of this study provide a basis for further research in the field of MEC DT models and carbon footprint-aware orchestration.

On behalf of the Technical Program Committee, it is my great pleasure to welcome you to the IEEE GLOBECOM 2022 for the second time in Rio De Janeiro, Brazil. Under the theme of “Accelerating the Digital Transformation through Smart Communications,” IEEE GLOBECOM 2022 brings together researchers from all over the world to discuss the latest advances in communications technology. IEEE GLOBECOM 2022 marks the first of its series being held after the pandemic and it is organized in hybrid mode to enable every researcher in the World to participate, in-person or remotely.

Network softwarization, containerization, and cloudification in a distributed and centralized environment are the current tread in 5G, Beyond 5G, and 6G. In that sense, significant activities are going on in the research community to softwarize the network functions deploying them in a cloud-native environment. Cloud-native architecture is an approach for network function and service to be built specifically to deployed in the cloud. In this paper, we emulated Long LTE/LTE-A/5G in lightweight containers. We have evaluated the feasibility of using very lightweight environments such as k3s. We show the possibility of emulating a simple 4G/5G scenario without requiring a full Kubernetes infrastructure. The final results are based on available open projects, used as inspiration and as a starting point to modify deployment techniques and configurations. We have also explored scalability, orchestration, automation, and reliability of the deployments. Finally, we measured and tested the performance. The performance evaluation shows the potential application of the open platform-based emulations and deployment in 5G and beyond.

An autonomic network management system (ANMS) is expected to play a significant role in fifth and sixth-generation (5G and 6G) networks. It enables the network to manage itself with minimum or no human intervention. Recently, an ANMS architecture called multi-agent-based network automation of the network management system (MANA-NMS) architecture was presented. The article discussed a multi-agent service decomposition architecture, defining atomic network-functions (ANFs). These ANFs are proposed to be intelligent and autonomous agents. The agents are designed as independent atomic decision elements incorporating machine learning (ML) as an internal cognitive component. The atomic units are used as a building block for an ANMS. In line with this approach, this article proposes a network traffic classifier agent (NTCA) as a part of the network traffic management system. We first design and implement a NTCA using an ML algorithm as a cognitive component of the agent. To compare, we used K-Nearest Neighbors (K-NN), Decision Tree, Support Vector Machine (SVM), and Naive Bayes in the agent design. We perform an evaluation using classification accuracy, training latency, and classification latency. Finally, we tested the performance of the NTCA by implementing it in the MANA-NMS conceptual framework. The results show that the Decision Tree NTCA has the highest mean classification accuracy, the least mean training latency, and the lowest mean classification latency.

Internet of Things (IoT) network is dominating both the research and industry. There are numerous emerging IoT connectivity Technologies such as Sigfox, LoRa, NB-IoT, LTEM. However, these IoT connectivity technologies have different protocols and packet/message formatting. Thus, IoT devices are usually not able to interact with one another, causing interoper-ability challenges. This is creating the so-called network island or silos. Interoperability between different IoT networks needs to be achieved to fully exploit IoT potential. This is required at each level of the network. Different solutions have been proposed to tackle the interoperability problem at different levels reducing the difficulty in defining a solution breaching the vertical silos barrier. In this article, we focus on addressing network-level interoperability. We provide a network format translator in a virtualized environment as a flexible and lightweight deployment. As a proof of concept, a testbed is developed implementing the proposed translator using NS3. Using the testbed, we can communicate with different IoT technologies sending packets between each device in each type of IoT network. For example, sending a LoRaWAN packet to Wi-Fi and 6LoWPAN and visa-versa. Finally, we have measured the latency introduced by the translator.

The introduction of the Internet of Things (IoT) and massive machine-type communications has implied an increase in network size and complexity. In particular, there is already a huge number of IoT devices in the market in various sectors, such as smart agriculture, smart city, smart home, smart transportation, etc. The IoT interconnectivity technologies are also increasing. Therefore, these are increasingly overwhelming the efforts of network administrators as they try to design, reconfigure and manage such networks. Relying on humans to manage such complex and dynamic networks is becoming unsustainable. Network automation promises to reduce the cost of administration and maintenance of network infrastructure, by offering networks the capability to manage themselves. Network automation is the ability of the network to manage itself. Various standardization organizations are taking the initiative in introducing network automation, such as European Telecommunication Standardization Institute (ETSI). ETSI is leading the standardization activities for network automation. It has provided different versions of reference architecture called generic autonomic network architecture (GANA), which describes a four-level abstraction for network-management decision elements (DEs), protocol level, function level, node level, and network level. In this article, we review and survey the existing works before and after the introduction of software-defined networking (SDN) and network-function-virtualization (NFV). We relate the main trending paradigms being followed, such as SDN, NFV, machine learning (ML), microservices, multiagent system (MAS), containerization, and cloudification, as a pivotal enabler of full network automation. We also discuss the autonomic architectures proposed in the literature. Finally, we presented possible future research directions and challenges that need to be tackled to progress in achieving full network automation.

The papers in this special section focus on softwarized networking for next generation industrial cyber-physical systems (CPSs). With the emergence of embedded and ubiquitous cyberphysical applications, the rationale of blending the physical and the virtual worlds has become ever promising. These papers examine several topics that are recently concerned in the community, including the software defined architectures and implementations, advanced machine learning and data analytics solutions, blockchain-based network services and applications, network function allocation, dependable and trustable solutions, energy efficient networks and services, and other enabling technologies for integrating softwarized networks into CPSs.

With the recent advances in wireless communications, the automotive industry is leading to evolution. To succeed in this emerging era of technology, the Internet of Connected Vehicles (IoCV) has emerged as one of the potential applications of the Internet of Things (IoT). It refers to the dynamic mobile communication systems that communicate between vehicles and public networks to enhance the connectivity between cars via technology. By offering a wide variety of infotainment services, fleet operations, and in-vehicle applications, IoCV has gained the tremendous capacity to provide a safer and sustainable transportation system to the society. According to Gartner Inc., “the connected car is already a reality, and in-vehicle wireless connectivity is expanding rapidly.” As a result, the evolution of cars into the IoT will keep on accelerating the global market which is expected to grow by 270% by 2022. Furthermore, the increasing deployment of sensors and ever-evolving cognitive technology opens up new opportunities for IoCV. Due to these significant developments, connected vehicles are receiving widespread attention from the major automotive giants such as Tesla, BMW, Waymo (Google), Uber, Volvo, and so on. Despite all the opportunities offered by the IoCV, their highly dynamic topology and the increasing number of vehicles pose challenges regarding delivering low-latency vehicle-to-everything (V2X) communications.

The advent of network softwarization is enabling multiple innovative solutions through software-defined networking (SDN) and network function virtualization (NFV). Specifically, network softwarization paves the way for autonomic and intelligent networking, which has gained popularity in the research community. Along with the arrival of 5G and beyond, which interconnects billions of devices, the complexity of network management is significantly increasing both investments and operational costs. Autonomic networking is the creation of self-organizing, self-managing, and self-protecting networks, to afford the network management complexes and heterogeneous networks. To achieve full network automation, various aspects of networking need to be addressed. So, this article proposes a novel architecture for the multi-agent-based network automation of the network management system (MANA-NMS). The architecture rely on network function atomization, which defines atomic decision-making units. Such units could represent virtual network functions. These atomic units are autonomous and adaptive. First, the article presents a theoretical discussion of the challenges arisen by automating the decision-making process. Next, the proposed multi-agent system is presented along with its mathematical modeling. Finally, MANA-NMS architecture is mathematically evaluated from functionality, reliability, latency, and resource consumption performance perspectives.

The recent pandemic of COVID-19 has changed the way people socially interact with each other. A huge increase in the usage of social media applications has been observed due to quarantine strategies enforced by many governments across the globe. This has put a great burden on already overloaded cellular networks. It is believed that direct Device-to-Device (D2D) communication can offload a significant amount of traffic from cellular networks, especially during scenarios when residents in a locality aim to share information among them. WiFi Direct is one of the enabling technologies of D2D communications, having a great potential to facilitate various proximity-based applications. In this work, we propose power saving schemes that aim at minimizing energy consumption of user devices across D2D based multi-hop networks. Further, we provide an analytical model to formulate energy consumption of such a network. The simulation results demonstrate that a small modification in the network configuration, such as group size and transmit power can provide considerable energy gains. The observed energy consumption is reduced by 5 times for a throughput loss of 12%. Additionally, we measure the energy per transmitted bit for different configurations of the network. Furthermore, we analyze the behavior of the network, in terms of its energy consumption and throughput, for different file sizes.

Adoption of wireless technologies is rising dramatically. As traffic load is continuously rising with the rapid growth of user scale and mobile services, today's Wireless Local Area Network (WLAN) enterprise is facing a series of crucial challenges such as packet loss, interference, poor bandwidth etc. This is because of the inherent design of the IEEE 802.11 architecture. Enabling Software Defined Networking (SDN) in Wi-Fi enterprise would solve the puzzle of current WLAN technology limitations and ensure high scalability and performance. While standardization is focusing on performance enhancement by evolving the Wi-Fi MAC/PHY protocols, in this work we focus on bandwidth optimization in IEEE S02.11 by using Software Defined Networking (SDN). We analyze various programming abstractions for WLANs and exploit the wireless link diversity in Software Defined Enterprise-WLAN with the support of the 5GEMPOWERSDN platform. A multiple uplink mechanism between user devices and Access Points (APs) is proposed that allows a remarkable performance improvement in the network in terms of throughput and packet delivery ratio.

5G and beyond need to meet stringent requirements of latency, reliability, and support for heterogeneous devices. However, the existing wireless network architecture is limited to fulfill these constraints. Cloud radio access network, along with network function virtualization, is suggested to provide flexibility and network agility. It decouples network functions, such as firewall and packet gateway, from hardware to software deployed in the cloud. Thus comprehensive end-to-end formulation of this architecture is required for virtual network function placement. Most of existing works focus on virtual functions placement with different objectives, addressing different service requirements separately. In this article, six 5G constraints are considered simultaneously to find optimal virtual network function placement with service differentiation. The selected six parameters reflect services' requirements, network constraints and computing constraints. We first model the overall cloud radio access network as a multi-layer loopless-random hypergraph and we provide the overall formulation of the system. Then, we reformulate such model considering backup virtual functions and CPU over-provisioning techniques to improve both virtual function's reliability and processing latency. Finally, we propose service differentiation to reduce CPU utilization and energy consumption, while using the above techniques. The results suggest that the application of service differentiation can significantly improve assignment of computing resources and energy efficiency.

Advances in virtualisation technology have reached the mobile networking domain. Network Functions Virtualisation Management and Orchestration (NFV MANO) as proposed by ETSI and realised via Opensource MANO (OSM) allows sharing or partitioning a fairly generic pool of hardware into virtual compute, network and storage resources among differentiated services. A direct consequence of this is a dramatic reduction in CAPEX/OPEX, but also the possibility of instantaneously deploy network services across one or several Mobile Network Operators' (MNO) infrastructure. This work demonstrates how the upcoming fifth generation (5G) of mobile communications envisions NFV MANO for instantiating network services, including Core Network components, and wireless SDN controllers for enforcing end-to-end QoS policies via network slices that span from the radio access segment to the backend packet network.

WiFi direct is a variant of Infrastructure mode WiFi, which is designed to enable direct Device-to-Device (D2D) communications between proximity devices. This new technology enables various proximity-based services such as social networking, multimedia content distribution, cellular traffic offloading, Internet of Things (IoT), and mission critical communications. However, energy consumption of battery-constrained devices remains a major concern in all the aforementioned applications. In this paper, we model energy consumption of the WiFi direct protocol, starting from device discovery to actual data transmissions for intra group D2D communications. We simulate a content distribution scenario in Matlab and analyze our model for the energy consumption of the devices. We argue that the energy spent in device discovery becomes significant in the case of small data sizes. In particular, we find that smaller data sizes, such as 100KB, cause the equal amount of energy to spend in both device discovery and data transmission phases, even when the device discovery time is very small.

The smartphones have evolved a lot during recent years. However, they are still limited in their battery time, computational power and storage space. Mobile Cloud Computing (MCC) has emerged as a promising solution that aims to augment smartphone's capabilities by providing a vast pool of computational power and storage space at cloud data center. In parallel to this, cooperation based computing is a recent concept in MCC that augments smartphone's capabilities by accumulating the computational resources of nearby devices to run a task. In this paper, we discuss different scenarios of computational offloading for a User Equipment (UE) and find an optimal option in terms of its energy consumption and task completion time. In particular, we compare the energy consumption and task completion time of a mobile application for local processing, offloading to a remote cloud and exploiting the cooperation based computing in the local Mobile Cloud (MC).We mark an offloading threshold for different offloading scenarios, so a UE can decide among offloading to a local MC or to a remote cloud, depending upon the size of the task it is offloading.

The next generation of cellular networks are expected to support multiple user-oriented services that have various quality of service (QoS) requirements, yet must be serviced by a single infrastructure. To achieve this, network virtualization can play an important role by partitioning/slicing a single physical network resource into multiple virtual networks such that each of the slices can support various services independently. The main contribution of this work is an implementation of static and dynamic resource allocation schemes for different slices supporting different services. We use this implementation to study the effects of increasing the number of network slices on the number of optimization trigger events. Moreover, we propose a non-uniform resource sharing agreement (policy) between the participating network slices and investigate how these sharing agreements affect the frequency of optimization trigger events.

The outcomes in terms of very high data rates, low latencies, almost full-time availability and wide coverages promised by the fifth generation (5G) networking technology should cope with the increasing customers demand for high quality multimedia content and services. The attempt to fulfill 5G pledges has seen the move to explore other groundbreaking technologies. These will include, among the others, the exploitation of millimeterwave (mmWave) bandwidths in both satellite and terrestrial communications, the novel paradigm of Software Defined Networking (SDN), the use of smart satellite gateways, etc. This paper aims at proposing an SDN-based seamless integration of ultra-broadband short-range wireless network segments, namely information showers with fixed fiber-based terrestrial network infrastructures for internetworking and mmWave geostationary satellite links in order to provide multi-gigabit multimedia content delivery to nomadic users. The impact of different parameters (including tropospheric conditions, latency and technical faults) on network performance have been fully appreciated by means of realistic emulations in Mininet environment. Preliminary results evidenced the clear advantages in terms of increased throughput and reduced latencies of the proposed SDN-based approach.

As the communication industry is progressing towards fifth generation (5G) of cellular networks, the traffic it carries is also shifting from high data rate traffic from cellular users to a mixture of high data rate and low data rate traffic from Internet of Things (IoT) applications. Moreover, the need to efficiently access Internet data is also increasing across 5G networks. Caching contents at the network edge is considered as a promising approach to reduce the delivery time. In this paper, we propose a marketplace for providing a number of caching options for a broad range of applications. In addition, we propose a security scheme to secure the caching contents with a simultaneous potential of reducing the duplicate contents from the caching server by dividing a file into smaller chunks. We model different caching scenarios in NS-3 and present the performance evaluation of our proposal in terms of latency and throughput gains for various chunk sizes.

Achieving the low‐latency constraints of public safety applications during disaster could be life‐saving. In the context of public safety scenarios, in this paper, we propose an efficient radio resource slicing algorithm that enables first responders to deliver their life‐saving activities effectively. We used the tool of stochastic geometry to model the base station distribution before and after a disaster. In addition, under this umbrella, we also proposed an example of public safety scenario, ultrareliable low‐latency file sharing, via in‐band device‐to‐device (D2D) communication. The example scenario is implemented in NS‐3. The simulation results show that radio resource slicing and prioritization of first responders resources can ensure ultrareliable low‐latency communication (URLLC) in emergency scenarios.

In this demo paper we present a dynamic spectrum-level slicing (DSLS) implementation for heterogeneous networks based on an open source software/hardware platform known as OpenAirInterface. Assuming the network traffic load changes every time interval, we mathematically formulate the DSLS as an optimization problem with the corresponding sets of constraints.

The performance gains from dynamic allocation of radio resources across multiple heterogeneous networks is studied. Through virtualization, the physical radio resources of the heterogeneous networks are first abstracted into a centralized pool of virtual radio resources. A dynamic spectrum-level slicing algorithm to share these radio resources across the different networks is then presented. This algorithm is responsive to changing user load and channel conditions. Simulation results show that for representative user arrival statistics, dynamic allocation of radio resources significantly lowers the percentage of dropped packets. In addition, they reveal that the triggers for dynamic allocation of resources across coexisting virtual networks occur every other time interval under the worst case traffic variation in the system (i.e., traffic varies every time interval). Our results suggest that performance benefits can be had even if dynamic spectrum-level slicing does not happen on time scales similar to that of the local resource schedulers residing in each virtual network.

WiFi Direct is a new technology that enables direct Device-to Device (D2D) communication. This technology has a great potential to enable various proximity-based applications such as multimedia content distribution, social networking, cellular traffic offloading, mission critical communications, and Internet of Things (IoT). However, in such applications, energy consumption of battery-constrained devices is a major concern. In this paper, we propose a novel power saving protocol that aims at optimizing energy consumption and throughput of user devices by controlling the WiFi Direct group size and transmit power of the devices. We model a content distribution scenario in NS-3 and present the performance evaluation. Our simulation results demonstrate that even a small modification in the network configuration can provide a considerable energy gain with a minor effect on throughput. The observed energy saving can be as high as 1000% for a throughput loss of 12%.

5G wireless communication envisions unprecedented changes in current mobile networks in order to guarantee significant higher performance mainly in terms of data rates, latencies and efficiency. This paper provides an analytical model based on stochastic geometry, in order to address the planning and the dimensioning of 5G Cloud RAN. The results shows the requirements in terms of number of virtual base-band units, and the energy gain achieved by virtualisation both in micro-cell and pico-cell scenarios. Thus, the proposed methodology represents a step forward to analyse and compare traditional RAN design with the emerging Cloud RAN paradigm.

Street lighting is an essential community service, but current implementations are not energy efficient and and require municipalities to spend up to 40% of their allocated budget. In this paper, we propose heuristics and devise a comparison methodology for new smart lighting solutions in next generation smart cities. The proposed smart lighting techniques make use of Internet of Things (IoT) augmented lampposts, which save energy by turning off or dimming the light in the absence of citizens nearby. Assessing costs and benefits in adopting the new smart lighting solutions is a pillar step for municipalities to foster real implementation. For evaluation purposes, we have developed a custom simulator which allows the deployment of lampposts in realistic urban environments. The citizens travel on foot along the streets and trigger activation of the lampposts according to the proposed heuristics. For the city of Luxembourg, the results highlight that replacing all existing lamps with LEDs and dimming light intensity in the absence of users in the vicinity of the lampposts is convenient and provides an economical return already after the first year of deployment.

Device-to-Device (D2D) communication has emerged as a new technology, which minimizes data transmission in radio access networks by leveraging direct interaction between nearby mobile devices. D2D communication has a great potential in solving the capacity bottleneck problem of cellular networks by offloading cellular traffic of proximity-based applications to D2D links. This provides several benefits including, but are not limited to, lower transfer delays, higher data rates, and better energy efficiency. However, security in D2D communication, which is equally essential for the success of D2D communication in future networks, is a less investigated topic in literature. In this paper, we propose the combination of the PGP and reputation-based model to bootstrap trust in D2D environments. Our proposal aims at minimizing any suspicious connection with selfish users. Offloading cellular traffic to trusted D2D links provides significant throughput gain over the conventional cellular network. Our results show that the capacity gain can be as high as 133%.

D2D communication and cognitive radio are among the key technologies that can solve the bandwidth bottleneck problem of cellular networks. These technologies help to maintain necessary communication between the user devices in case of natural disasters and traffic hotspot situations. In this paper, we efficiently employ the relay selection and cooperative beamforming (CBF) strategies for D2D communication and show that the user devices can securely communicate with each other without infrastructure support. The cloud heads in our proposal act as the primary users (PUs) and the cloud members are regarded as the secondary users (SUs) or the relay nodes. To investigate the efficiency of our proposal, we define the utilities of PUs employing relay selection and CBF, considering received signal power, latency, channel gain and security. We optimize relay selection and beamforming vector by solving mixed integer non-linear problem (MINLP). Numerical results show the efficiency and robustness of our approach.

This paper provides an analysis of energy efficiency of the Transmission Opportunity Power Save Mode (TXOP PSM) in IEEE 802.11ac Wireless Local Area Networks (WLANs). This mechanism allows a device to sleep during transmissions in the channel that are addressed to other devices. This operation is also referred to as microsleep and can significantly reduce the energy consumption of devices during overhearing periods. A key contribution of the analysis presented in this paper is the awareness of the non-negligible time and energy consumption that a device incurs when it switches between awake and sleep states. If the duration of such state transitions is longer than the transmission time, microsleep operation is not possible. This becomes a critical issue as transmission rates increase, thus reducing the transmission times. In this paper, we show that the performance dependence of TXOP PSM on the awake/sleep state transitions can be overcome by using burst transmission inherent to the TXOP operation. Results obtained through theoretical analysis and computer-based simulation show gains of up to 424% in energy efficiency when compared to legacy mechanisms.

This paper proposes a new Reverse Direction (RD) Medium Access Control (MAC) protocol to improve the throughput and energy efficiency of IEEE 802.11 Wireless Local Area Networks (WLANs). The proposed protocol allows a source device to transmit a burst of data frames to the intended destination device in a single channel access opportunity. After the successful reception of each data frame, the destination device may opportunistically respond with a data frame, thus being able to perform multiple RD transmissions. This operation can reduce the overall channel access overhead, hence increasing the efficiency of data transmission between two sender-receiver devices. The results obtained by means of theoretical analysis and computer-based simulation show that the novel RD protocol can outperform existing IEEE 802.11 protocols by yielding gains close to 60%.

Incorporating energy efficiency into the design of modern communication systems has become an important area of research. However, while most of the proposed solutions are devoted to making network hardware energy efficient, very few works focus on energy efficiency as a fundamental design parameter of network protocols. This paper proposes an analytical model for energy consumption of TCP which relates energy consumption to protocol operation cycles. Based on this model a number of optimization techniques are proposed to reduce energy consumption of TCP. The experiments, performed using NS2 simulations, demonstrate that energy savings can be as high as 93% for multiple TCP flows.

The surge of mobile data traffic has spurred academia and industries to begin developing 5G networks. 5G is meant to overcome limitations of 4G cellular technology relying on the dominant trend of mobile network densification with the deployment of small cell base stations. To accelerate this process, low complexity and inexpensive remote radio heads (RRHs) are deployed massively and connected to a centralized pool of resources. In this work, we study the problem of inter-cell interference (ICI) which arises in frequency reuse one multi-tier 5G networks. We entrust the management of RRHs to a software-defined network controller and we take advantage of network functions virtualization. Our contributions consist of proposing Dynamic Strict Fractional Frequency Reuse (DSFFR), a method to relieve ICI which dynamically divides the small cell area in a different number of sectors. Furthermore, we formulate a joint scheduling problem composed of two schedulers which operate at different time granularity to transmit downlink packets. Modeling the coverage area with the tool of stochastic geometry and solving with simulations the joint scheduling problem, we are able to show that DSFFR outperforms the static scheme. Performances are addressed in terms of spectral efficiency and packet blocking probability.

It has been shown in the literature that Network Coding (NC) can boost the performance of wireless networks. However, to really obtain the potential gain of NC, efficient Medium Access Control (MAC) protocols that operate with awareness of the NC functions are necessary. In this paper, we propose a novel NC-aware MAC protocol for IEEE 802.11 wireless networks that combines k-batch transmissions and multiple receiver-initiated reverse direction exchanges involving NC data to boost the overall network performance. The proposed protocol allows any node to transmit a burst of data packets in a single channel access invocation. Then, an intermediate node can transmit an NC data packet when receiving a valid data packet from a source node, without contending for channel access. Both analytical and simulation results presented in this paper show the high throughput and energy efficiency of the proposed protocol with gains ranging from 33% to 298% when compared to existing mechanisms based on the IEEE 802.11 Standard.

To cope up with the booming of data traffic and to accommodate new and emerging technologies such as machine-type communications, the 5th Generation (5G) of mobile networks must be empowered with efficient resource allocation schemes that benefit from the adoption of the Software-Defined networking (SDN) paradigm. In radio communications, allocation of resources is tightly connected with interference. In this paper, we revisit the way wireless interference is managed and avoided relying on the SDN paradigm for controlling the network. The SDN approach is exploited to expose the lower layers of the stack (e.g., Physical and Medium Access Control) to the controller and its applications by making system parameters available, such that it is possible to dynamically configure the network in a logically centralized fashion, by means of specifically designed algorithms. The contribution of this work is threefold. First, we show how to adapt the SDN paradigm to mobile networks. Second, we propose the interference graph as an abstraction that can be used to control interference. Last, we formulate a throughput optimization tool that uses the proposed interference graph as an input.

In this paper, we propose an energy-efficient solution for implementing Network Coding (NC) in wireless networks based on the IEEE 802.11 Standard. The proposed mechanism, called GreenCode, allows nodes to duty cycle by switching to a low-power (sleep) state when they overhear coded packet transmissions that will not provide any new information for them. To facilitate the sleep operation, bidirectional transmissions involving both coded and non-coded packets between pairs of sender-receiver nodes are integrated into the operation of GreenCode. Both analytical and simulation results presented in this paper show the high energy efficiency of GreenCode with gains of up to 360% when compared to the existing mechanisms based on the IEEE 802.11 Standard.

Wearable devices are becoming increasingly popular and are expected to become essential in our everyday life. Despite continuous improvement of hardware, the lifetime of mobile devices and their capabilities still remain a concern. Small size of batteries of smart watches, glasses, helmets and gloves limits the amount of computing, storage and communication resources. Mobile cloud computing can augment the capabilities of wearable devices by helping to execute some of the computing tasks in the cloud. Such computational offloading helps to preserve battery power at the cost of more intensive communications with the cloud. In this paper, we present a model and comprehensive analysis for computational offloading between wearable devices and clouds in realistic setups.

Spectrum sensing in cognitive radio technology consumes a significant amount of time and energy resources. Thus, it has a direct effect on the achievable throughput and consumed energy. In case of multi-channel systems, the problem becomes more effective since both the resources expenditure and the performance influence of spectrum sensing increase. Considering energy detection as the spectrum sensing method, a number of energy samples should be collected from each channel. Unlike the conventional scheme, the number of samples collected from each channel should be different due to the variant channel conditions. In this paper, the number of samples collected from each channel is optimized based on different setups, namely, throughput maximization setup, interference minimization setup, and sensing energy minimization setup.

Cloud computing is a computing model where users access ICT services and resources without regard to where the services are hosted. Communication resources often become a bottleneck in service provisioning for many cloud applications. Therefore, data replication which brings data (e.g., databases) closer to data consumers (e.g., cloud applications) is seen as a promising solution. In this paper, we present models for energy consumption and bandwidth demand of database access in cloud computing datacenter. In addition, we propose an energy efficient replication strategy based on the proposed models, which results in improved Quality of Service (QoS) with reduced communication delays. The evaluation results obtained with extensive simulations help to unveil performance and energy efficiency tradeoffs and guide the design of future data replication solutions.

Smart Grid (SG) is considered as the future of the electrical power distribution system, where Advanced Metering Infrastructure (AMI) will let utilities to acquire and analyze the consumption data and access and control several home appliances for power balancing purposes through special devices, known as Smart Meters (SMs). Long Term Evolution (LTE) appears as a promising solution to handle the SMs traffic due to its high bandwidth and flexibility. However, given that SMs need to periodically send measurements to the eNodeB (eNB), the network uplink could be a potential bottleneck, thus affecting the users running real-time applications, such as voice calls. To overcome this problem, in this paper, we present a scheduling policy that jointly considers the channel quality, the traffic prioritization and the AMI Packet Delay Budget (PDB) in order to provide the SMs with the required resources and reduce the impact on the real-time traffic. Extensive simulation experiments have been carried out, indicating the smooth coexistence between the SMs and the voice users, since the proposed scheduler achieves higher percentage of served users compared to widely employed schedulers.

This paper describes an experimental implementation of a variation of the Reverse Direction (RD) Medium Access Control (MAC) Protocol (RDP) defined in the IEEE 802.11n using the Wireless Open-Access Research Platform (WARP). The proposed approach, named Bidirectional MAC (BidMAC), allows the receiver of a valid data sequence to perform an RD transmission to the transmitter without contending for the channel. Whereas in RDP the RD transmission must be initiated by the transmitter, in BidMAC it can be dynamically initiated by the receiver according to its traffic requirements. Previous results based on mathematical analyses and computer-based simulations have shown that BidMAC can better balance downlink and uplink transmission opportunities in a Wireless Local Area Network (WLAN) where the Access Point (AP) handles bidirectional data flows for some of its wireless stations (STAs). This paper aims at going one step further and demonstrating that such superior performance can be attained in real environments. Towards this end, an implementation of BidMAC has been carried out in a reference design of WARP compatible with the IEEE 802.11a/g and tested in a proof-of-concept network formed by an AP and two STAs. Experimental results confirm the superior performance of BidMAC when compared to the legacy Distributed Coordination Function (DCF) of the IEEE 802.11 versus the traffic load, packet length, and data rate, yielding gains of up to 60%.*

This paper analyzes the performance of a duty-cycled polling-based access mechanism that exploits the Transmission Opportunity Power Save Mode (TXOP PSM) defined in the IEEE 802.11ac to improve the energy efficiency of Wireless Local Area Networks (WLANs) based on the IEEE 802.11. The basic idea behind the proposed approach, named GreenPoll, is to enable contention free periods, based on polling with beacons, during which wireless stations can save energy by turning off their radio transceivers after exchanging data with the access point. The closed expression of energy efficiency of GreenPoll is formulated in this paper and is used to evaluate the performance of GreenPoll considering important parameters like the traffic load, packet length, data rate, and number of stations in the network. Both analytical and simulation results show the high energy efficiency of GreenPoll with gains of up to 330% and 110% when compared to the legacy Distributed Coordination Function (DCF) and the Point Coordination Function (PCF) defined in the IEEE 802.11, respectively.

Abstract Cognitive radio is envisaged as a promising solution to cope with the problem of spectrum scarcity. In cognitive radio networks, users can sense the medium and opportunistically use available frequency bands. Users can cooperate in order to increase the reliability of the sensing process, which is called cooperative spectrum sensing (CSS). However, cooperative paradigms are threatened by the behavior of malicious users. Two types of attacks represent the main threats for CSS network operation, namely, spectrum sensing data falsification (SSDF) and incumbent emulation (IE). These two types of attacks have received a considerable amount of attention in the literature, but they have always been studied separately. In this paper, we propose a novel mechanism based on lightweight cryptography that considers both SSDF and IE attacks combined in a CSS network for the first time. Lightweight cryptography, in contrast to previous techniques used (such as intrusion detection or reputation systems), provides higher resilience to such attacks when a high number of mobile malicious users exist, providing better energy efficiency. Analytical and simulation results show the outperformance of the proposed algorithm compared with previous mechanisms, in terms of lower false alarm probability (i.e., higher chance of using the free frequency bands) and hence better energy ratings. Copyright © 2015 John Wiley & Sons, Ltd.

The implementation of Network Coding (NC) in IEEE 802.11-based wireless networks presents the important challenge of providing additional transmission priority for the relay nodes responsible for coding. These nodes are able to convey more information in each transmission than those that forward single packets, by combining several received packets in a single coded packet. To transmit data, the nodes execute the IEEE 802.11 Medium Access Control (MAC) protocol, called the Distributed Coordination Function (DCF). Thus, they compete for the access to the wireless channel and get equal transmission opportunities under high congestion. As a result, congested relay nodes will severely limit the performance of the network. In this paper, we investigate a backwards-compatible mechanism, called Reverse Direction DCF (RD-DCF), that allows relay nodes to transmit data upon successful reception of data. We analyze the performance limits of the proposed protocol with and without NC in terms of throughput and energy efficiency. The performance evaluation considers different traffic loads, packet lengths, and data rates. The results of this work show that the proposed RD-DCF+NC protocol can improve throughput and energy efficiency up to 335% when compared to legacy DCF.

The Distributed Coordination Function (DCF) is the mandatory access method for any compliant device in Wireless Local Area Networks (WLANs) based on the IEEE 802.11 Standard. WLAN Access Points (APs) and stations (STAs) contend for the access to the wireless channel in order to transmit data by using a variation of Carrier Sense Multiple Access (CSMA). In doing so, they consume a significant amount of energy for continuously monitoring the channel state. In this paper we investigate backwards-compatible mechanisms to increase throughput and energy efficiency in WLANs during contention periods based on DCF. The first mechanism is called Bi-Directional DCF (BD-DCF) because it allows for bidirectional transmissions between APs and STAs with a single channel access invocation. The second mechanism is called Bi-Directional Sleep DCF (BDSL-DCF) as it allows overhearing STAs to enter the sleep state, i.e. switch off the radio transceiver, during bidirectional transmissions. We analyze the performance limits of the proposed protocols in terms of throughput and energy efficiency considering different values for the data packet length and data rate. The results of this work show that the BD-DCF and BDSL-DCF protocols can improve throughput up to 60% and energy efficiency up to 360% when compared to legacy DCF.

An important challenge for the implementation of network coding in IEEE 802.11-based wireless networks is to give additional priority for channel access to the relay stations responsible for coding. These relay stations are able to provide more information in a single transmission than those that forward single packets, hence improving throughput and energy efficiency. The Distributed Coordination Function (DCF) of the IEEE 802.11 standard is a contention-based Medium Access Control (MAC) protocol that provides an equal distribution of channel access opportunities for all competing stations. However, the relay station represents a congestion point and additional transmission slots should be assigned to it to increase the overall network performance. To address this issue we investigate a coding-aware MAC protocol, called Reverse Direction DCF (RD-DCF), which enables bidirectional communications between the relay station and another station with a single channel access invocation. This simple and backwards compatible mechanism allows the relay station to transmit a coded packet together with the acknowledgement immediately after receiving a data packet. The simulation results show a gain of up to 130% in terms of both throughput and energy efficiency for RD-DCF with network coding when compared to DCF.

Cooperative Spectrum Sensing (CSS) was envisioned to improve the reliability of spectrum sensing process in cognitive radio networks. However, CSS is prone to security threats that degrade the overall performance. A popular attack in CSS is called spectrum sensing data falsification (SSDF) attack. In SSDF attack, a malicious user sends false spectrum sensing results to the fusion center, which significantly degrades detection accuracy and energy efficiency. In this paper, an attacker-punishment policy is proposed. The proposed policy is based on relating the scheduling probability for each user to its sensing performance, representing a punishment for attackers and a reward for honest users. The proposed policy includes identifying attackers, ignoring their reported results, and assigning a proper scheduling probability to each user. Two different approaches are presented to accomplish the proposed policy, namely, Majority-based Assessment and Delivery-based Assessment. Simulation results show that the proposed policy improves the individual energy efficiency of the honest CUs, and degrades the energy efficiency of the attackers.

Cooperative spectrum sensing (CSS) represents a key factor in the success of cognitive radio networks. CSS implies that users report their local sensing results to a fusion center in order to process them. The two popular reporting schemes are soft and hard schemes. In hard scheme, local sensing result is conveyed by a single bit, whereas the sensing result is reported as it is in the soft scheme. The more detection accuracy attained by soft scheme is confronted by more resource efficiency in hard scheme. This paper provides analytic comparison between both schemes in terms of throughput, energy consumption and energy efficiency. Our work includes deriving the sufficient conditions on the frame length by which the hard scheme outperforms soft scheme for each comparison aspect. Our results show that hard scheme always achieves higher throughput, while, at short frames and large number of users, it consumes less energy and attains higher energy efficiency.

One of the main challenges in cooperative spectrum sensing (CSS) for cognitive radio networks (CRN) is spectrum sensing falsification (SSDF) attack. A SSDF attack consists in a cognitive user providing false data about the spectrum status. SSDF attack can hugely degrade the achievable detection accuracy and energy efficiency of CRNs. In this paper, a robust CSS algorithm against SSDF attack is proposed. The proposed algorithm assigns a specific weight to each user, which is able to (i) completely eliminate the resulting effects on CSS caused by many types of SSDF attacks, (ii) convert some types of SSDF attacks to be honest users, and (iii) alleviate the influence of other honest users that suffer from poor sensing performance or/and very noisy reporting channels. Simulation results show that, compared to many previous works, a significant improvement in detection accuracy and energy efficiency can be attained by the proposed algorithm.

Although employing cooperation in spectrum sensing for cognitive radio (CR) systems improves the detection accuracy by mitigating the shadowing and multi-path fading faced by cognitive users, it increases the energy consumption especially because spectrum sensing is a periodic process. Therefore, for battery-powered terminals, energy efficiency represents a favorable metric in system design. One of the ways to improve the energy efficiency in CR is to optimize the fusion rule (FR) by which the individual results are processed. In this paper, we optimize the well known FR K-out-of-N for maximizing energy efficiency and detection accuracy. Mathematical expressions for the optimal N and K for both objectives are obtained. Simulation and analytical results show that significant improvement in energy efficiency can be achieved through FR optimization while satisfying a predefined threshold on the missed detection probability.

The IEEE 802.11 Distributed Coordination Function (DCF) is the fundamental access method providing asynchronous best-effort services in Wireless Local Area Networks (WLAN). In this standard, the currently employed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) and the Binary Exponential Backoff (BEB) mechanism represent major sources of energy consumption at both the access point and mobile stations of a WLAN. To improve energy efficiency in WLANs, this paper introduces an enhanced DCF protocol incorporating bidirectional transmissions in combination with sleep periods, called Bidirectional Sleep DCF (BDSL-DCF). By following this new scheme, every successfully established connection between a sender and its intended destination can be used to exchange data, hence reducing control overhead and channel contention. Furthermore, this functionality allows those mobile stations not participating in data transmission to activate the sleep mode to conserve energy. Simulation results show that BDSL-DCF can outperform DCF in terms of energy efficiency and throughput, with negligible impact on packet transmission delay.

Energy efficiency during spectrum sensing in cognitive radio has received a lot of attention during recent years. Such issue becomes challenging, especially with battery-powered terminals, because of its direct influence on achievable performance represented by detection accuracy. In this paper, we present a novel reporting scheme for spectrum sensing results, which significantly reduces the energy consumption without any effect on the detection accuracy. The proposed scheme is based on the observation that sensing results are consecutively reported to a Fusion Center (FC), which allows the FC to terminate the process whenever the received results are enough to make a decision according to the employed Fusion Rule (FR). Hence, the energy consumed in results' reporting is reduced as the number of reporting users is lower. Mathematical expressions for the average number of reporting users for several FRs are obtained. Simulation and analytical results show a significant reduction of the energy consumption.

The energy consumption in data centers is drastically increasing and becoming a significant portion in the data center operating expenses. Enabling a sleep mode in the idle computing servers and network hardware is the most efficient method to avoid unnecessary power consumption. However, changes in the power modes introduce considerable delays. Moreover, inability to wake up a sleeping server immediately requires an availability of a pool of idle servers able to accommodate incoming load in the short term to prevent QoS degradation. In this paper we investigate the amount of computing servers and network hardware needed to accommodate different incoming load patters in the data centers. Furthermore, we propose to build these servers on energy efficient hardware, which is costly but can scale its power consumption with the offered load levels. The evaluation results show that the proposed methodology can save up to $750 per server per year on average.

The Point Coordination Function (PCF) of the IEEE 802.11 standard represents a well-known Medium Access Control (MAC) protocol providing Quality-of Service guarantees in Wireless Local Area Networks (WLANs). However, with the currently employed polling mechanism WLANs consume a significant amount of the energy resources from battery-powered user devices. To provide energy saving, an improved MAC protocol is presented in this paper, where bidirectional transmissions of fixed duration are incorporated into PCF in order to enable dynamic scheduling of real-time traffic. Based on this new strategy, wireless access points (APs) can estimate the proper duration of the Contention Free Period (CFP), in order to allow mobile stations to acknowledge any received data packet with a data packet equal to the received packet in size. Having this information, a mobile station, following the data exchange with the AP, can determine its wake-up timer and activate the sleep mode for the rest of the CFP interval. Comprehensive computer-based simulations demonstrate the feasibility of the proposed MAC improvements to achieve energy efficiency with negligible impact on packet delivery delay.

Energy efficiency in cooperative spectrum sensing in cognitive radio is investigated in this paper, where a novel approach is proposed for reducing the energy consumed in spectrum sensing and improving the resultant energy efficiency of the cognitive transmission. The proposed approach is based on limiting the number of users that participate in the spectrum sensing task. The participation decision of each user is taken individually by the user itself, where each user estimates the expected amount of consumed energy based on its distance from the base station, and compares it to a predefined threshold. The user will participate only if the estimated energy is less than the threshold. Besides reducing energy consumption, our proposal increases the amount of successfully transmitted data as well. Moreover, an optimization of the threshold is carried out through simulation in order to optimize the energy efficiency. Our results show a considerable amount of reduction in energy consumption (up to 80%) compared to the conventional approach.

In order to increase the penetration of electric vehicles, a network of fast charging stations that can provide drivers with a certain level of quality of service (QoS) is needed. However, given the strain that such a network can exert on the power grid, and the mobility of loads represented by electric vehicles, operating it efficiently is a challenging and complex problem. In this paper, we examine a network of charging stations equipped with an energy storage device and propose a scheme that allocates power to them from the grid, as well as routes customers. We examine three scenarios, gradually increasing their complexity. In the first one, all stations have identical charging capabilities and energy storage devices, draw constant power from the grid and no routing decisions of customers are considered. It represents the current state of affairs and serves as a baseline for evaluating the performance of the proposed scheme. In the second scenario, power to the stations is allocated in an optimal manner from the grid and in addition a certain percentage of customers can be routed to nearby stations. In the final scenario, optimal allocation of both power from the grid and customers to stations is considered. The three scenarios are evaluated using real traffic traces corresponding to weekday rush hour from a large metropolitan area in the US. The results indicate that the proposed scheme offers substantial improvements of performance compared to the current mode of operation; namely, more customers can be served with the same amount of power, thus enabling the station operators to increase their profitability. Further, the scheme provides guarantees to customers in terms of the probability of being blocked (and hence not served) by the closest charging station to their location. Overall, the paper addresses key issues related to the efficient operation, both from the perspective of the power grid and the drivers satisfaction, of a network of charging stations.

Cognitive Radio provides a promising solution for the spectrum scarcity in wireless systems. The main stage of a successful cognitive transmission is the spectrum sensing stage, where the spectrum is sensed to detect and avoid interfering with licensed users. Unfortunately, regardless of the type of the spectrum sensing technique employed, the probability of missed detection can still be relevant. This paper investigates the effects of missed detection probability on the energy resources of the cognitive system, and provides a new algorithm for power allocation in OFDM systems, which reduces the loss in energy resources, and guarantees the target Quality-of-Service (QoS) of the served users. Unlike the current algorithms, the proposed algorithm is based on the Sensing Side Information (SSI) and the Channel State Information (CSI) as well. Simulation results underline a relevant reduction in terms of power loss (with gains up to 50%) and a consequent improvement in QoS satisfaction.

One of the main challenges in Cognitive Radio Networks (CRN) is the high energy consumption during the spectrum sensing stage, especially employing a cooperative approach. The algorithm proposed in this paper aims to reduce the energy consumption while maintaining the probability of detection and false alarm probability to the desired thresholds. The algorithm is based on decreasing the number of sensing users using a simple and practical approach. The performance of our approach is then compared in terms of energy efficiency with the different data fusion rules available in the literature. As a result, more than 95% energy saving can be achieved, as shown through mathematical equations and confirmed by simulation results.

In this paper we show how a cooperative Medium Access Control (MAC) protocol can help extend the lifetime of Machine to Machine (M2M) networks. A comparison between non-cooperative Automatic Repeat reQuest (ARQ) schemes (retransmissions performed only from the source) and a cooperative retransmission scheme (all the stations in the network are spontaneous helpers and assist the destination during the communication) in energy-constrained networks is also presented in this work. The results shown in this paper are discussed in terms of the trade off between the network lifetime and the amount of data delivered, leading to the conditions where it is possible to optimize the lifetime of this kind of networks.

The long propagation delays of the underwater acoustic channel make traditional Medium Access schemes impractical and inefficient under water. This paper introduces and studies Interference Avoidance and Network Coding for Medium Access protocol design aiming to cope with the underwater channel constraints and achieve efficient data transmission under water. Network Coding can exploit the broadcast channel to send different information to several receivers simultaneously. With Interference Avoidance the long propagation delay can be used to communicate in full-duplex mode. Alone and combined these concepts could increase channel utilisation as well as improve energy efficiency of the network nodes. The main goal is to investigate the potential benefits of new strategies for data dissemination over a string topology scenario. Comprehensive simulations prove the feasibility of Interference Avoidance and Network Coding improving the system efficiency when compared with CSMA/CA.

TCP congestion control has a critical impact on the Internet stability and performance. In relatively recent times, a number of novel TCP congestion control variants, such as TCP BIC, CUBIC and Compound, started to be deployed in modern operating systems. While risks and benefits of these recent developments are subject of debate, the research community needs to monitor the extent to which each of these novel TCP congestion control variants is actually used in regulating Internet traffic, and this information, due to the complex, diverse, and decentralized nature of the Internet, is very difficult to be obtained. As a first step towards the collection of this important piece of information, we have developed a tool, presented in this paper, which allows us to identify the TCP variants employed in monitored TCP sessions. Our approach consists in passively monitoring packets and ACKs exchanged in TCP sessions, estimating the evolution of congestion window, and matching a set of features against those typical of the various TCP variants considered. Our identification tool has been validated trough emulation and real Internet experiments, showing promising results in the identification of New Reno, BIC, Cubic and Compound TCP variants.

Rate adaptation represents a relevant issue in optimization of wireless local area network performance. The paper proposes to employ a cognitive approach to perform rate adaptation, which is able to learn cause-effect relationships without any a priori knowledge. Results demonstrate the potential of the proposed scheme.

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

We show how the TUNEGreen real-time energy consumption monitoring capabilities can be used to understand how and where power is consumed in a simple star-shaped network based on IEEE 802.11. Different traffic patterns will be generated in order to demonstrate the correlation between traffic and energy consumption.

Cognitive networks were recently proposed to cope with the complexity and the dynamics of network managements, exploiting reasoning to adapt the behavior of protocols. Among the reasoning formalisms that can be employed, Fuzzy Cognitive Maps seem to be very promising, as they potentially allow the cognitive process to consider cross-layer interactions in the characterization of the performance of a network node. However, when considering a high number of cross-layer interactions, reasoning schemes can be too time consuming and may not provide a suitable solution as environmental conditions change. In order to decrease the demand of reasoning time it is of utmost importance to discover which cross-layer relationships carry relevant information to the cognitive process. This paper discusses how to make such differentiation. Moreover, it proposes a metric to evaluate the influence a cross-layer interaction has on the cognitive process.

The evolution of network technologies brought increasing management complexity of networking infrastructure and protocols. Cognitive networking was introduced to deal with such complexity. This work presents a cognitive algorithm for cross-layer performance management which is the core of a decentralized framework for self-configuration of communication protocols. We illustrate the proposed solution for the joint reconfiguration of protocol parameters at different layers. The cognitive joint adaptation of TCP congestion window and MAC layer data rate is carried out as a proof of concept. Simulation results show performance improvement given by the proposed approach under changing network conditions.

Cognitive networks are becoming extremely popular in the network domain. This paper proposes a novel concept in cognitive network management and protocol configuration, where any protocol of the TCP/IP protocol reference model can be extended to dynamically tune its configuration parameters based on "immediate past" performance. The approach is focused on inter-node cognitive adaptation which is fostered by the proposed Cognitive Information Service (CIS). Performance evaluation results are obtained for cognitive adaptation of main TCP flow control parameters and show good agreement with design objectives.

In this paper, we introduce a novel queue management technique for the buffer space at the base station of infrastructure 802.11 networks that considers mobile user's receiving characteristics. The maximum amount of the base station buffer that can be used by a given flow is updated proportionally to RTT, measured at the mobile nodes and sent to the base station my the mean of link layer acknowledgements. In this way, the proposed scheme remains transparent to high-level protocols. The proposed approach makes possible the implementation of algorithms able to provide RTT-fairness in wired-cum-wireless networks. Results show the advantage of the proposed queue management scheme when compared to that of traditional drop-tail queue management.

Modeling computer networks is a complex task, as their behavior depends from several variables. Focusing on a single communication device, ISO/OSI and TCP/IP layered protocol stacks provide interoperability and fast deployment of networking solutions, but they limit the control on the interaction among protocols operating at different layers. As a consequence, the need is emerging to develop appropriate models to capture and evaluate the interaction of protocols within a single communication device in order to underline such forms of "indirect" interaction - since they may lead to unforeseen performance degradations. The proposed work aims at using the game theory for capturing the interactions within the protocol stack of a single node, with the goal of allowing to determine the "steady state" or the operating point of the system in a given scenario. As a result, a scalable and modular framework is presented, that enables characterization and analysis of cross-layer interactions starting from the protocols' specifications. Finally, as an example of application, the model is applied to a single-hop IEEE 802.11 wireless network.

Automatic repeat request (ARQ) techniques employed by leading wireless technologies aim at compensating the high error rates due to radio impairments, but do not offer any differentiated levels of protection. In this work, we propose to enable per-packet differentiation of link layer ARQ protection (in terms of no. of retransmissions) driven by requirements of the end applications as well as of communication protocols implemented on the mobile terminal. Experimental results demonstrate the potential benefits deriving from the proposed strategy, both on TCP data flows and MPEG-4 video streams.

There is an increasing acceptance for Wireless Mesh Networks (WMN) as the potential `last-mile' access technology running media-rich applications with stringent quality of service (QoS) requirements. As WMNs are envisioned to provide high bandwidth broadband services to a large community of users, the Internet Gateway which acts as a central point of attachment for the mesh routers is likely to be a potential bottleneck because of its limited wireless link capacity. We propose TBMGA (Tree-Based with Multi-gateway Association), a novel routing protocol that elegantly balances the load among the different Internet gateways in a WMN. With TBMGA, we combine the flexibility of layer-2 routing with the self-configuring and self-healing capabilities of MANET routing. TBMGA switches the point of attachment of an active source-serviced gateway depending on a global metric estimated based on the average queue length and expected availability at the Internet gateway. The protocol is evaluated using simulations and we observe that the proposed scheme is able to efficiently balance the traffic between multiple gateways.

WiMAX is one of the most promising emerging broadband wireless technologies. As a consequence, data transfer performance optimization represents a crucial issue due to TCP limitations in wireless environment. In this study, we focus on the overhead deriving from the multilayer ARQ employed at the link and transport layers. To the aim of reducing unnecessary burden on the wireless link, we propose a cross-layer ARQ approach, called ARQ proxy, which substitutes the transmission of TCP ACK packet with a short MAC layer request on the radio link. Packet identification is achieved through the use of hash functions applied to the packet headers. Performance of the ARQ proxy is evaluated using an IEEE 802.16e system level simulator which includes realistic physical layer implementation. The results demonstrate good agreement with the design objectives and achieve the expected levels of system capacity increase, reduction of round trip time, and higher error rate tolerance.

3G long-term evolution (LTE) is a recent effort taken by cellular industries to step into wireless broadband market. The key enhancements target an introduction of new all- IP architecture, enhanced link layer and radio access with OFDM modulation and multiple antenna techniques. In this study, we focus on the overhead deriving from the multilayer ARQ employed at the link and transport layers. To the aim of reducing unnecessary burden on the wireless link, we propose a cross-layer ARQ approach, called ARQ Proxy, which substitutes the transmission of TCP ACK packet with a short MAC layer request on the radio link. Packet identification is achieved through association of a hash function to the raw packet data. Performance of the ARQ Proxy is evaluated using EURAE extensions for ns2 simulator. Results demonstrate significant improvements in terms of system capacity, TCP throughput performance, and higher tolerance to transmission errors.

This paper introduces a procedure called traffic engineering for grids for enabling grid networks to self-adjust to resource availability. The proposal is based on monitoring the state of resources and on task migration. It involves several layers of the Internet architecture. Experiments executed in NS-2 are used to illustrate the efficacy of the procedure proposed.

Wireless Mesh Networks represent an interesting technology due to their reliability, broad coverage and relatively easy and inexpensive scalability. The architecture is mainly a hybrid solution between ad-hoc and infrastructure networking, where each node potentially acts as a relay, forwarding traffic generated by other nodes. Even if solutions are already available, many research topics have yet to be investigated, as no theoretical studies are available to determine the performance of such networking paradigm. In this work, we aim at providing mathematical expressions for connectivity and cumulative capacity of a mesh network on the basis of the network configuration, in order to provide guiding principles in mesh networks' design and development. An analytical approach is used in determining the connectivity probability, while the "bottleneck collision domain" concept is adopted to estimate capacity. The framework is further validated through comparison with simulation results.

In this paper, mechanisms to enhance the performance of TCP Westwood+ in the presence of a large Bandwidth-Delay Product are studied. In particular, the employment of a logarithmic function for congestion window increase in the absence of packet losses is proposed. Extensive numerical simulations, carried out by using ns-2, show that the use of such logarithmic function can lead to relevant performance improvements with respect to standard Westwood+ protocol.

This paper presents a novel solution of bidirectional high speed communications for IP traffic transport over WDM networks. Based on the observation of the bidirectional nature of traffic in the Internet bidirectional light-trails are proposed as an extension of the natively unidirectional light-trail concept. Specification of a synchronous protocol enables fairness in medium access and data delivery improving reliability of communications and enabling QoS support in WDM networks

The paper presents integrated mechanisms for differentiated service (DiffServ) architecture consisting of active rate management (ARM) at the edges of the network and two-color active queue management (AQM) controllers at the core of the network. The AQM controller is a robust linear quadratic regulator based proportional-integral-derivative controller for two-color marking at the core. The proposed mechanism is for DiffServ assured forwarding per hop behavior for guaranteeing minimum rates for exact and over-provisioned systems. The performance of the proposed scheme is evaluated and compared with other approaches using the NS-2 simulator.

In this paper the problem of transmitting embedded bitstreams over wireless packet networks is considered. In particular, the authors address the problem of joint usage of power control and forward error correction for optimizing video streams delivery, and analyze the performance in different scenarios. Experimental results, obtained for the transmission of MPEG-4 FGS bitstreams, show the impact of the proposed method in the case of different modulation schemes.

Wireless networks are becoming increasingly popular in the world of telecommunications. IEEE 802.11 standard provides reliable data delivery in wireless LANs. The cost for such reliability is the overhead related to the data transmission at the link layer. In this paper, the application of a packet concatenation algorithm at the IP layer is proposed to the purpose of overhead reduction together with increase of fairness in medium access at the link layer. The performance of the proposed algorithm is evaluated through simulations as well as real experiments. Results underline significant performance enhancements deriving from the use of the proposed packet concatenation algorithm in wireless LANs.

Mobility, made available by today's communication networks, imposes several limitations in the design of multimedia applications, due to high error rates, reduced bandwidth, strong variability, and mobile terminals lifetime. This paper investigates the use of an energy constrained cross-layer approach for the transmission of progressively coded images over packet-based wireless channels. We propose an optimum power allocation algorithm to enable unequal error protection of a pre-encoded image. Simulations are performed modeling transmission over a Rayleigh fading channel. The investigation focuses on JPEG2000, but it is applicable to other progressively coded bitstreams as well. Our experimental results demonstrate that it is possible to achieve a relevant performance enhancement with the proposed approach over uniform error protection. The optimal solution can also serve as a guideline for developing less computationally intensive empiric approaches.

A robust proportional-integral-derivative (PID) controller is proposed for active queue management (AQM). A linear quadratic regulator (LQR) method is used to design the controller, named LQR-PID. LQR is a robust design technique as compared to classical gain-and-phase margin and dominant pole placement methods. The LQR-PID controller marks the packets according to queue length with a probability and notifies congestion to sources; in turn, sources adjust their send rate, thus maintaining queue length at the desired level in bottleneck routers. By maintaining queue length at the desired level, delay can be predicted and quality of service can be provided. Simulation results demonstrate the robustness and superiority of LQR-PID AQM as compared with other AQM schemes in the literature.

In this paper, the problem of transmitting embedded bitstreams over wireless packet networks is considered. In particular, the authors address the problem of optimal energy allocation in video streams employing fixed-size packets, and analyze the performance under different modulation schemes. Results obtained for the transmission of MPEG-4 FGS bitstreams show the impact of the proposed method in different modulation schemes.

Multicarrier code division multiple access (MC-CDMA) techniques were originally proposed at mid of 90's for wideband multi-user communications in wireless environments characterised by hostile propagation characteristics. Problems still to be solved are related to the provision of efficient resource channel allocation in variable-bit-rate transmission. In this work, a strategy for medium access control in MC-CDMA systems for broadband WLAN indoor applications is considered. A great advantage of MC-CDMA lies in the capability of supporting asynchronous multi-user variable-bit-rate (VBR) transmission over multipath channel with conventional detection. This can be helpful in designing an efficient and real-time medium access control (MAC) strategy, since a significant number of VBR users can share the same bandwidth without relevant performance dropouts. Different classes of users will be labeled by the MAC level that has to plan a controlled access to the channel on the basis of the users' requests and to check the possibility of assuring to them a certain QoS degree. The paper presents some simulation results in order to discuss the feasibility of the proposed access control strategy.

A significant improvement of block-based motion estimation strategies is presented, which supports fast computation and VLBR coding. For each block, a spatio-temporal context is defined based on nearest neighbors in the current and previous frames, and a prediction list is built. Then, the best matching vector within the list is chosen as an estimation of the block motion. An additional correction vector can be sent when the prediction error exceeds a threshold. Bit rate saving is achieved through an adaptive sorting of the prediction list of each block, which allows one to reduce the entropy of the motion indexes. Tests demonstrate a speed up above 1:200 as compared to full search, and a coding gain above 2, with a negligible loss of accuracy.

Texture analysis based on the extraction of contrast features is very effective in terms of both computational complexity and discrimination capability. In this framework, max–min approaches have been proposed in the past as a simple and powerful tool to characterize a statistical texture. In the present work, a method is proposed that allows exploiting the potential of max–min approaches to efficiently solve the problem of detecting local alterations in a uniform statistical texture. Experimental results show a high defect discrimination capability, and a good attitude to real-time applications, which make it particularly attractive for the development of industrial visual inspection systems.

Current standards for video compression achieve good performances in terms of data compaction and signal-to-noise ratio of the decoded signal. Nevertheless, there are some known problems concerning the visual quality of reconstructed images, which can be partially solved using appropriate post-processing algorithms. The paper proposes a new adaptive anisotropic filter (AAF) that aims to unify the treatment of different sources of perceptive distortion in MPEG sequences. The process is driven by a local classification of blocks and single pixels of decoded frames, taking into account several parameters (distribution of DCT coefficient energy, presence of sharp variations, spatial position of DCT block boundaries). Experimental results show that the proposed algorithm outperforms existing enhancement approaches, in particular when constraints on complexity and real-time processing are compelling.

A significant improvement of block-based motion estimation strategies is presented, which provides fast computation and very low bitrate coding. For each block, a spatio-temporal context is defined based on nearest neighbors in the current and previous frames, and a prediction list is built. Then, the best matching vector within the list is chosen as an estimation of the block motion. Since coder and decoder are synchronous, only the index of the selected vector is needed at the decoder to reconstruct the motion field. To avoid the propagation of the error, an additional correction vector can be sent when prediction error exceeds a threshold. Furthermore, bitrate saving is achieved through an adaptive sorting of the prediction list of each block, which allows to reduce the entropy of the motion indexes. Tests demonstrate that the proposed method ensures a speed up over 1:200 as compared to full search, and a coding gain above 2, with a negligible loss of accuracy. This allows real-time implementation of VLBR software video coders on conventional PC platforms.

Most widespread video coding algorithms (such as MPEG, H.261, H.263) employ DCT coding for data compression but introduce annoying artefacts due mainly to the independent quantization of the coefficients in each block, that are especially visible at medium and low bitrates. Within this framework, post-processing appears to be a practical solution for visual enhancement of compressed video. In this paper, an adaptive anisotropic spatial-variant FIR filtering procedure is proposed. The filter kernels are selected on the basis of a pixel classification procedure that performs a block-DCT coefficients energy analysis and an edge extraction. The analysis of the transform coefficients matrix allows one to extract information about spatial characteristics, while the edge information provide the basis for the estimation of the position of the local visual artifact. Accurate filtering results were obtained during experiments that outperform those obtained with other existing approaches.

An effective real-time post-processing algorithm for error recovery in noise corrupted JPEG bit streams integrated into an existing remote video-surveillance system is presented. The algorithm exploits information extracted by the video-surveillance system in order to detect corrupted frames and to recover them, enhancing the performances of the system, without compromising the real-time behavior of the application. Results show the validity of the presented approach.

This work presents a real-time post-processing algorithm developed for enhancing the performances of remote JPEG-based video surveillance applications, seriously degraded by transmission over noisy channels. The aim of the algorithm is to distinguish between blocks changes due to variations in the observed scene and noise-altered blocks, which contain errors due to channel noise and can be corrected exploiting the strong spatio-temporal redundancy of the encoded digital source without any side information. Experimental results, obtained through JPEG transmission simulations performed in the context of a remote video-surveillance system devoted to detection of abandoned objects, show a good improvement both in terms of perceptive quality and of the performance of the overall video-surveillance system.