Today, Federated Learning (FL) stands out as the solution to addressing the challenges of distributed computing and empowering a wide range of edge devices with artificial intelligence capabilities. One variant of FL called semi-decentralized FL (SDFL) enables multiple server units to coordinate the learning task instead of relying on only one central server, hence preventing single-point failures. However, SDFL requires careful consideration regarding the coordination between server nodes, and dealing with the heterogeneous computing resources and data distributions across end devices (FL clients). Therefore, we propose TUNE-FL, an adapTive semi-synchronoUs semi-deceNtralizEd Federated Learning that addresses the clients' heterogeneity challenges. TUNE-FL alleviates these challenges by (i) ensuring consensus regardless of the network topology, and (ii) deploying an adaptive semi-synchronous mechanism for coordinating the learning process across all nodes while taking into consideration the heterogeneity presented by end devices. We evaluated TUNE-FL for the intrusion detection system (IDS) datasets and compared it with the three most representative baseline models. The experimental results demonstrate that TUNE-FL outperforms the baselines in accuracy while greatly reducing the duration of FL training by approximately 97 times.

In recent years, federated learning (FL) has gained significant attention as a privacy-preserving solution for distributed machine learning, particularly in cybersecurity applications such as intrusion and attack detection. However, traditional FL models often face challenges related to limited training data diversity, communication overhead, and the ability to adapt to novel or unforeseen attack patterns. At the same time, generative AI (GAI) models have emerged as powerful tools for synthesizing realistic data, enabling enhanced model generalization and robustness. In this work, we propose integrating GAI with FL to create a more effective and adaptive framework for cybersecurity called GAIA-FL (GAI-Augmented FL). GAI can augment FL by synthesizing diverse attack scenarios, enriching local datasets, and addressing data heterogeneity across distributed nodes. We analyze the unique capabilities of GAI, such as data generation, and highlight its potential to improve the performance of FL-based cybersecurity systems. Additionally, we explore the integration of generative models and FL, focusing on their combined ability to detect complex and evolving threats while maintaining data privacy. Unlike existing studies, our work emphasizes the fusion of GAI and FL to tackle the challenges of decentralized intrusion detection and attack prevention. To validate our approach, we present a case study where GAI is used to enhance FL-based network intrusion detection by generating synthetic attack data, improving detection accuracy and robustness. This work demonstrates how this integration can revolutionize cybersecurity in next-generation networks by providing scalable, privacy-preserving, and adaptive solutions for evolving cyber threats.

The widespread adoption of the Internet of Things (IoT) in our technology-driven society raises significant security and privacy concerns, highlighting the need for collaborative Intrusion Detection Systems (IDS) that leverage Deep Learning (DL) methods to detect suspicious network traffic. Using both the computing power and local data available at distributed end devices, Federated Learning (FL) provides a decentralized learning paradigm that preserves data privacy. However, the system heterogeneity and the Independent and Identically Distributed (non-IID) distribution of FL clients’ data introduce various challenges that impact training efficiency. To address these issues, we propose an adaptive approach named FLAIR, which leverages a semi-synchronous FL mechanism and a Decision Transformer (DT) to select clients that not only enhance the global model’s performance but also reduce communication and computation overhead. DT helps to make client selection decisions by considering both current and historical information, with the model trained offline using various client selection policies. The process begins by generating an offline database, which will be used to train the DT offline. Then, the DT is deployed for online client selection under a semi-synchronous protocol, enabling a fully adaptive FL system. Extensive experiments on IDS datasets show that FLAIR significantly reduces computation and communication time by up to 94% and 93%, respectively, while maintaining comparable classification performance to the baseline models.

Open Radio Access Network (O-RAN) architectures enable flexible, scalable, and cost-efficient mobile networks by disaggregating and virtualizing baseband functions. However, this flexibility introduces significant challenges for resource management, requiring joint optimization of functional split selection and virtualized unit placement under dynamic demands and complex topologies. Existing solutions often address these aspects separately or have limitations in large and real-world scenarios. In this work, we propose a novel Graph-Augmented Proximal Policy Optimization (GPPO) framework that leverages Graph Neural Networks (GNNs) for topology-aware feature extraction and integrates action masking to efficiently navigate the combinatorial decision space. Our approach jointly optimizes functional split and placement decisions, capturing the full complexity of O-RAN resource allocation. Extensive experiments on both small- and large-scale O-RAN scenarios demonstrate that GPPO consistently outperforms state-of-the-art baselines, achieving up to $18 \%$ lower deployment cost and $25 \%$ higher reward in multitopology evaluations, while maintaining perfect reliability. These results highlight the effectiveness and scalability of GPPO for practical O-RAN deployments.

Network Digital Twins (NDTs) are increasingly relying on data-driven approaches for modeling complex network dynamics. Traffic forecasting is crucial for NDTs to provide timely insights for automated network reconfiguration. Existing spatiotemporal forecasting methods, while effective, often rely on pre-constructed graphs, limiting their flexibility in dynamic network environments. This paper introduces Flex+ , an inductive graph-based learning model designed for traffic prediction in data-scarce scenarios. Flex+ focuses on individual eNodeB traffic prediction by extracting local spatial correlations from k-hop subgraphs, combined with temporal information. Its inductive design allows it to operate on unseen nodes during training, enabling adaptability to evolving network topologies. Empirical studies on a large-scale cellular traffic dataset demonstrate that Flex+ achieves a 5.9% improvement in accuracy in inductive settings and a 22% reduction in error in data-scarce scenarios when trained with only 3 days of traffic data. Notably, a Knowledge Distillation (KD) framework is introduced to reduce model size and accelerate inference time up to 10 times while maintaining prediction accuracy.

The rapid proliferation of Internet of Things (IoT) devices and the transition to distributed computing environments necessitate advanced intrusion detection systems (IDS) to safeguard the new paradigm known as Cloud-Edge-IoT (CEI) continuum. In this paper, we introduce a novel approach called SURFS, integrating Hierarchical Federated Learning (HFL) with Spiking Neural Networks (SNN) to propose a robust, sustainable, and energy-efficient IDS for this continuum. HFL, with its hierarchical learning strategy, keeps data where they are generated, thus preserving user privacy and reducing communication overhead through its combination of decentralized and centralized architecture. On the other hand, SNN, inspired by human neural mechanisms, offers significant computational efficiency. Our proposed IDS combines these strengths, facilitating localized and energy-efficient detection at the edge and IoT layers while enabling global model aggregation and updates at the cloud layer. Through extensive experiments using one of the most recent datasets (Edge-IIoTset), we demonstrate that our approach not only detects attacks with high accuracy but also substantially reduces energy consumption across the continuum. The SURFS model presents a slightly superior performance in classification accuracy, outstripping the FL+SNN and non-FL models by margins of 0.5% and 1.21 %; however, with a much faster convergence time (3 x and 17 x respectively). In terms of sustainability, it achieves a remarkable reduction in communication overhead 99% lower than FL+SNN and 97% lower than non-FL. Additionally, it showcases significant improvements in computational cost, being 62% more efficient than FL+SNN and 94% more efficient than the non-FL model.

Recent studies have explored the potential of Machine Learning (ML) for intrusion detection systems (IDS) in the Internet of Things (IoT) system. However, low latency and privacy requirements are important in emerging application scenarios. Furthermore, due to limited communication resources, sending the raw data to the central server for model training is no longer practical. It is difficult to get labeled data because data labeling is expensive in terms of time. In this paper, we develop a semi-supervised federated active learning for IDS, called (METALS). This model takes advantage of Federated Learning (FL) and Active Learning (AL) to reduce the need for a large number of labeled data by actively choosing the instances that should be labeled and keeping the data where it was generated. Specifically, FL trains the model locally and communicates the model parameters instead of the raw data. At the same time, AL allows the model located on the devices to automatically choose and label part of the traffic without involving manual inspection of each training sample. Our findings demonstrate that METALS not only achieve a high classification performance, comparable to the classical FL model in terms of accuracy but also with a small amount of labeled data.

From a telecommunication standpoint, the surge in users and services challenges next-generation networks with escalating traffic demands and limited resources. Accurate traffic prediction can offer network operators valuable insights into network conditions and suggest optimal allocation policies. Recently, spatio-temporal forecasting, employing Graph Neural Networks (GNNs), has emerged as a promising method for cellular traffic prediction. However, existing studies, inspired by road traffic forecasting formulations, overlook the dynamic deployment and removal of base stations, requiring the GNN-based forecaster to handle an evolving graph. This work introduces a novel inductive learning scheme and a generalizable GNN-based forecasting model that can process diverse graphs of cellular traffic with one-time training. We also demonstrate that this model can be easily leveraged by transfer learning with minimal effort, making it applicable to different areas. Experimental results show up to ${9. 8 \%}$ performance improvement compared to the state-of-the-art, especially in rare-data settings with training data reduced to below $20 \%$.

Security has become a critical issue for Industry4.0 due to different emerging cyber-security threats. Recently, many deep learning (DL) approaches have focused on intrusion detection. However, such approaches often require sending data to a central entity. This in turn raises concerns related to privacy, efficiency, and latency. Despite the huge amount of data generated by the Internet of Things (IoT) devices in Industry 4.0, it is difficult to get labeled data, because data labeling is costly and time-consuming. This poses many challenges for several DL approaches, which require labeled data. In order to deal with these issues, new approaches should be adopted. This article proposes a novel federated semisupervised learning scheme that takes advantage of both unlabeled and labeled data in a federated way. First, an autoencoder (AE) is trained on each device (using unlabeled local/private data) to learn the representative and low-dimensional features. Then, a cloud server aggregates these models into a global AE using federated learning (FL). Finally, the cloud server composes a supervised neural network, by adding fully connected layers (FCN) to the global encoder (the first part of the global AE) and trains the resulting model using publicly available labeled data. Extensive case studies on two real-world industrial datasets demonstrate that our model: (a) ensures that no local private data is exchanged; (b) detects attacks with high classification performance, (c) works even when only a few amounts of labeled data are available; and (d) haslow communication overhead.

Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

In this paper, we present FLUIDS, a Federated Learning with semi-sUpervised approach for Intrusion Detection System. FLUIDS formulates the intrusion detection into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined in a collaborative way. The combination of federated learning and semi-supervised Learning allows the solution to: better preserve the privacy, improve training and inference efficiency, achieve better accuracy, and be cheaper to deploy.

With the increasing development of 5G/Beyond 5G and network softwarization techniques, we have more flexibility and agility in the network. This can be exploited by Machine Learning (ML) to integrate intelligence in the network and improve network as well as service management in edge-cloud environment. Intrusion detection systems (IDS) is one of the challenging issues for managing network. However, traditional approaches in this domain require all data (and their associated labels) to be centralized at the same location. In this context, such approaches lead to: (i) a large bandwidth overhead, as raw data needs to be transmitted to the server, (ii) low incentives for devices to send their private data, and (iii) large computing and storage resources needed on the server side to label and treat all this data. In this paper, to cope with the above limitations, we propose a semi-supervised federated learning model for IDS. Moreover, we use network softwarisation for automation and deployment. Our model combines Federated Learning and Semi-Supervised Learning where the clients train unsupervised models (using unlabeled data) to learn the representative and low-dimensional features and the server conducts a supervised model (using labeled data). We evaluate this approach on the well-known UNSW-NB15 dataset and the experimental results demonstrate that our approach can achieve accuracy and detection rates up to 84.32% and 83.10%, respectively while keeping the data private with limited overhead.

Network traffic classification is a key component for network management, Quality-of-Service management, as well as for network security. Therefore, developing machine learning (ML) methods, which can successfully distinguish network applications from each other, is one of the most important tasks. However, among the classification methods applied to network traffic classification so far, there is no one method that outperforms all the others. They all have advantages and inconveniences depending on the application domain. Therefore,this paper proposes an intelligent traffic management model by using deep learning (DL) that incorporates multiple Decision Tree-based models. Our model deploys a blending ensemble learning method to combine tree-based classifiers in order to maximize generalization accuracy. Using two datasets, we show that our proposed ensemble model is efficient for network traffic classification. Furthermore, the proposed approach is also compared against other representative machine-learning and deep-learning models and the results demonstrate that our approach provides better performance compared to others.

Network Traffic Classification enables a number of practical applications ranging from network monitoring to resource management, with security implications as well. Nowadays, traffic classification has become a challenging task in order to distinguish among a variety of applications due to the huge amount of generated traffic. Therefore, developing Machine Learning (ML) models, which can successfully identify network applications, is one of the most important tasks. However, among the ML models applied to network traffic classification so far, no model outperforms all the others. To solve these issues, this paper proposes a novel Deep Learning (DL)-based approach that incorporates multiple Decision Tree based models. This approach employs a non-linear blending ensemble method by combining tree-based classifiers through DL in order to maximize generalization accuracy. This ensemble consists of two levels called base classifiers and meta-classifiers. In the first level, Decision Tree-based models are used as the base classifiers while in the second level, DL is used as a meta-model to combine the outputs of the base classifiers. Using two publicly available datasets, we show that our proposed ensemble is suitable for network traffic classification and outperforms the linear blending (using logistic regression as meta-model) as well as several well-known ML models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), AdaBoost, K-Nearest Neighbors (KNN), LightGBM, Catboost, and XGBoost.

Network traffic classification is an important task in modern communications. Several approaches have been proposed to improve the performance of differentiating among applications. However, most of them are based on supervised learning where only labeled data are used. In reality, a lot of datasets are partially labeled due to many reasons and unlabeled portions of the data, which can also provide informative characteristics, are ignored. To handle this issue, we propose a semi-supervised approach based on deep learning. We deployed deep learning because of its unique nature for solving problems, and its ability to take into account both labeled and unlabeled data. Moreover, it can also integrate feature extraction and classification into a single model. To achieve these goals, we propose an approach using stacked sparse autoencoder (SSAE) accompanied by de-noising and dropout techniques to improve the robustness of extracted features and prevent the over-fitting problem during the training process. The obtained results demonstrate a better performance than traditional models while keeping the whole procedure automated.

Nowadays, huge amount of data are generated and collected in many domains and from various sources. Most of the time, the collected data are processed as common data where simple calculations are applied for the analysis, such as measuring the average, the maximum, the deviation, etc. Exemplar selection has a finer meaning since its aim is to study a few exemplars from common data (the most representative ones). The objective of this paper is to propose a methodology able to extract these representative exemplars from a dataset. The proposed method has been tested against well-known simulated as well as real dataset. It is then experimented on dataset extracted from experimentations of connected vehicle traces.

Multimedia transfer in ad-hoc wireless networks is a challenging issue and attracts many researchers. In this paper, we propose a cross layer scheme to handle video transfer. Our staring point is to provide a multipath forwarding protocol able to transmit efficiently video streams and regular data from many sources to a unique base station (BS). This protocol will have a close cooperation with the application layer in order to consider with care different video frames and to assign high priority to the most important frames and lower priority for the least important ones. In the meantime, many paths could be selected, our aim is to guarantee independence between paths in terms of interference in order to be able to use more than one path in the same time. Experimental analysis has been undertaken, they show improvements of some performance indicators such as packet data rate, delay, loss packet rate and user experience feedback.

Relaying packets through multi-hop ad-hoc networks and the scarcity of wireless resources can severely deteriorate the quality of service. As a result, one of the major challenges in video streaming over ad-hoc networks is enhancing users experience and network utilization. An extension of H.264 standard, named scalable video coding (SVC), enables smooth adaptation of video quality to networks' status. In this paper, we formulate the problem of optimizing quality of experience (QoE) for SVC under time-constraints. The problem itself is a mixed integer linear programming which is NP-hard, and therefore we propose a heuristic algorithm to solve it. Simulation results show that the proposed algorithm can provide the similar video quality as optimal solutions with shorter calculation time.

This paper presents a QoE-based multipath routing algorithm for scalable video coding (SVC) over multichannel wireless mesh networks (WMN). The availability and quality of links in WMN significantly depends on not only the positions of neighbors but also the interference between links. In video streaming, the quality perceived by the users is the key to success. Consequently, enhancing experience of users and network utilization in these networks is an interesting challenge. In this paper, we utilize multicommodity model to formulate the problem of streaming SVC video over multichannel WMN. Then, we propose a heuristic algorithm to speed up searching solution procedure. Simulation results demonstrate that the proposed algorithm can obtain a near-optimal solution within a short calculation time.

Nowadays, a recent trend has emerged in the Wireless Multimedia Sensor Networks (WMSNs) field. This trend consists of ensuring the best Quality of Experience (QoE) while transferring the multimedia content, as well as keeping an acceptable Quality of Service (QoS) concerning the scalar data transmission. However, ensuring this request needs more flexible and robust protocols at different communication layers. Especially, the network layer must provide an efficient routing protocol with awareness of the interference phenomenon and the carrier sense range effect. In this paper we provide analysis of the carrier sense range effect on multimedia communication in the WMSNs. Therefore, we present a realistic solution at the routing layer to enhance the QoS/QoE while avoiding any type of multipath coupling effect. Our solution differs from the existing approaches that handle the problem at the MAC layer. Simulations conducted over the NS-2 simulator show promising results in terms of delay, Packet Delivery Ratio (PDR) and QoE.

The major challenge in ad-hoc network is multi-hop paradigm. Moreover, multimedia streaming over ad-hoc networks increasingly emerges since it can be useful in various practical cases. In video streaming, previous studies provided quality of service (QoS)-based methods that focused on the value of technical parameters such as bandwidth, jitter, delay, etc. It is however not perfectly correlated to users' experience. In this paper, we propose a routing mechanism based on optimized link state routing (OLSR) to guarantee a good quality of experience (QoE) of users. The pseudo-subjective quality assessment (PSQA) is adopted to estimate mean opinion scores (MOS), then this MOS value will be exploited by the source for selecting the appropriate path. In addition, an event-triggered based on the MOS value is exploited to provide more relevant information. The results show that the proposed scheme outperforms other OLSR-based routing protocols particularly in heavy load and moderate mobility scenario.

In-vehicle multimedia applications are gaining interest since recent years. However, the high loss rate caused by high mobility in vehicular ad-hoc networks (VANETs) imposes several challenges in multimedia transmission. Moreover, in the context of multimedia, the quality of service (QoS)-based approaches assess the quality of streaming services through network-oriented metrics while the concept of quality of experience (QoE) is built upon the perception of users. Consequently, adopting QoE metric to control multimedia streams in VANETs is interesting and relevant. In this paper, a QoE-based routing protocol for video streaming over VANETs is proposed. By taking the mean opinion score (MOS) into account for path selection, a better performance can be achieved. Simulation results demonstrate how the proposed scheme improves the performance of video streaming applications in VANETs.

Quality of Experience (QoE) is the key to success for multimedia applications and perceptual video quality is one of the important component of QoE. A recent video encoding scheme called Scalable Video Coding (SVC) provides the flexibility and the capability to adapt the video quality to varying network conditions and heterogeneous users. In this paper, we focus on SVC multicast over IEEE 802.11 networks. Traditionally, multicast uses the lowest modulation resulting in a video with only base quality even for users with good channel conditions. To optimize QoE, we propose to use multiple multicast sessions with different transmission rates for different SVC layers. The goal is to provide at least the multicast session with acceptable quality to users with bad channel conditions and to provide additional multicast sessions having SVC enhancement layers to users with better channel conditions. The selection of modulation rate for each SVC layer and for each multicast session is achieved with binary integer linear programming depending on network conditions with a goal to maximize global QoE. Results show that our algorithm maximizes global QoE by providing highest quality videos to users with good channel conditions and by guaranteeing at least acceptable QoE for all users.

The multimedia streams over the wireless access networks has increased dramatically over the past few years. Since the wireless link is featured with restricted bandwidth and limited resources, increasing demand of real-time applications imposes challenges on wireless networks to provide Quality of Service (QoS). Plenty of schemes were proposed to provide QoS, among which the admission control is a typical method. However, admission control mechanisms only consider the case at the network entering phase. After the admission, another mechanism is needed to continue to monitor the ongoing traffic and to react to fluctuation of resource consumption during the connection. Therefore, other than only using admission control schemes, which try to restrict access to the network, schemes to control on-going best-effort background are proposed in this paper. The proposed dynamical halting and resuming schemes of background traffic provide a finer way to allocate resources for video streams. It is shown via extensive simulations that proposed schemes can provide direct and solid enhancement of Quality-of-Experience (QoE) for video subscribers, and achieve good tradeoff between video quality and bandwidth utilization.

Deployment of next-generation network (4G) begins to spread throughout the world. With variety of network technologies and QoS restrictions on emerging applications; it becomes difficult for a user to select the best access network to request for connection. Even though many schemes have been proposed in the literature but very few of them take into account quality of experience (QoE) perceived by user for making decision. As QoE represents perception experienced by the user, it is thus an essential indicator for network evaluation, especially with multimedia communications nowadays. Therefore, in this paper we propose a novel network selection mechanism that takes quality of experience into consideration for decision making. It is a user-baaed and network-assisted approach thus a compromise solution between user and network benefit. The main idea is to use quality of experience of ongoing users in candidate networks as an indicator to select the best network for connection. We have implemented and tested our mechanism in network simulator NS-2. The obtained results illustrate that with a QoE-aware mechanism we can significantly improve user experience of mobile node and load balancing between networks.

With widespread use of multimedia communication, quality of experience (QoE) progressively becomes an important factor in networking today. Besides, multimedia applications can be supported under various technologies, including wired and wireless networks. Among them, Universal Mobile Telecommunications System (UMTS) is one of the most popular technology thanks to its support on mobility. Improved with a new access method (High Speed Downlink Packet Access or HSDPA), it can provide higher bandwidth and enable wider range of services including multimedia applications. In UMTS, different categories of traffic are specified along with their characteristics. Best effort traffic has been specified with less priority because it has fewer constraints. On the other hand, real-time multimedia traffic such as streaming video or VoIP are more sensitive to network condition changes, hence special treatment (e.g. QoS scheduler) is needed in order to achieve user satisfaction. According to the literature, most of scheduling mechanisms mainly take into account signal quality and fairness and do not consider user perception. In this paper, we propose a novel approach, QoE-aware scheduler that takes quality of experience into account when making scheduling decisions. Compared to other existing schedulers, QoE-aware approach has reached a profitable performance in terms of user satisfaction, throughput, and fairness.

Nowadays, wireless networks have been deploying everywhere, with IEEE 802.11 as the most popular standard. However, wireless resources are scarce and wireless condition varies often. These limitations are crucial for applications with tight QoS requirements such as video or voice over IP. To cope wi

The deployment of real-time and multimedia applications over wireless LAN has gained a growing interest in this last decade. These applications, e.g. video streaming, require tight guarantee on quality of service (QoS). Also, they use multicast communication in order to reduce the bandwidth consumption. However, in WLAN, multicast packets are sent with the basic rate (lowest rate); which results in capacity wasting because of longer channel occupancy. Moreover the lack of feedback mechanism makes it difficult to deal with reliability or service quality. In this paper, we propose to rely on the WLAN multi-rate capability, in order to transmit multicast packets with higher and dynamic rate than the basic rate. Unlike other existing protocols that use a static-threshold to decide when to change transmission rate, we propose a novel dynamic rate-adaptation mechanism based on quality of experience (QoE), namely Q-DRAM. According to the clients' feedback on QoE, we adapt the multicast rate: (i) when users had bad QoE we reduce the multicast transmission rate; (ii) when users had good QoE, we increase the multicast transmission rate. Simulation results show that Q-DRAM increases the wireless channel utilization and maximizes users' QoE, compared to existing solutions as well as to the IEEE 802.11 standard.

Widespread use of wireless networks nowadays raises many challenging issues to be explored. With increasing of multimedia traffic, quality of experience (QoE) needs to be satisfied at users while overall performance needs to be maintained at networks. In order to achieve these goals, the use of network selection mechanism is inevitable. When several access points are present, user should select the best available network while trying to keep load balanced between access networks. In this paper, we present a user-based and network-assisted scheme for network selection in wireless IEEE 802.11 technology. By providing users in decision making process with relevant information about the networks, the proposed solution keeps compromising advantage for both user and network. Our mechanism is based on a technique called pseudo-subjective quality assessment (PSQA), which is used basically to measure quality of experience perceived by users. We propose a new scheme where PSQA tool is used to assist in terminal-centric network selection. We explain the scheme and illustrate its efficient performance compared to a signal-based mechanism.

Widespread use of wireless networks nowadays raises many problems for service providers in managing their resources. These problems are caused mainly by restricted bandwidth and variable radio condition in this type of network. Moreover, with the emergence of multimedia traffic and its requirements in terms of quality, admission control is hence an inevitable choice to optimize network resources while maintaining high service quality at users. In this paper, we propose an admission control mechanism based on quality of experience (QoE) perceived by users. The human QoE is obtained by a tool called pseudo subjective quality assessment (PSQA), which is based on statistic learning using random neural network (RNN). Instead of relying on technical parameters such as bandwidth, loss, or latency, which do not correlate well with human perception, our scheme is based on mean opinion score (MOS) but without interaction from real humans. The simulation results demonstrate the better performance of our proposition compared to the loss-based approach regarding user satisfaction evaluated by achieved QoE at user and bandwidth utilization of the network evaluated by good put.