The ever-growing demand for video and data services poses a significant challenge for mobile network operators. This surge is fueled by the proliferation of high-definition applications, online gaming, and the rise of machine-type communications' entailing exceptional Quality of Experience (QoE) for users. To address these demands, Heterogeneous Networks (HetNets) have emerged as a promising solution within 5G networks. However, the coexistence of diverse small cells in multi-tier 5GHetNets introduces complex interference issues. This paper introduces a novel Machine Learning Enhanced Classification for Interference Management and Offloading (MLCIMO) scheme. It employs multi-binary classification to categorize users by interference types and levels, improving QoE for various services and enhancing resource utilization. A comprehensive performance comparison with state-of-the-art approaches was also conducted to showcase the advantages of MLCIMO in terms of Video Multimethod Assessment Fusion (VMAF), R-Factor, and RUM Speed Index (RUMSI) metrics.

The exponential growth of data-hungry apps has led to an enormous increase in global data traffic. The recent worldwide pandemic has also significantly contributed to changing traffic patterns due to overloaded macrocells in the suburban residential areas, creating load balancing issues. 5G technology along with Heterogeneous Networks (HetNets), consisting of small cells, are promising solutions to address this problem. Still, the deployment of these small cells, labelled femtocells, brings a new challenge known as femtocells' co-tier interference that can hinder the performance of various 5G applications. The introduction of Machine Learning (ML)/Artificial Intelligence (AI) in the 5G architecture is considered a promising solution to tackle this issue. In this paper, we propose a machine learning-based algorithm, Machine Learning Multi-Classification and Offloading Scheme (MLMCOS) to mitigate co-tier interference in 5G HetNets. MLMCOS classifies users into multiple classes and uses a service-based-priority system to offload users experiencing co-tier interference. The proposed algorithm is compared with the classic Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). Simulation results show that MLMCOS outperforms some existing solutions in terms of throughput, delay and Packet Loss Ratio (PLR).

Bluetooth Low Energy (BLE) is one of the technologies that can be used for short-range communications. BLE beacons are small wireless devices that can store URLs and real-world data, including GPS locations and identifiers. This article proposes BeTwin, an efficient solution that integrates BLE beacons with a digital twin environment in order to enhance and customise a virtual 3D platform, bridging the real and virtual worlds. The beacons are used to bring information from real objects into the virtual platform. We extend the Eddystone messaging protocol and investigate the impact of beacon distance, energy levels, and number of beacons on the system performance. We also describe a testbed consisting of a virtual environment, a beacon reader along with beacons, and assess its performance in terms of communications latency, Received Signal Strength Indicator (RSSI), and number of beacons.

In a time where data traffic is booming, Software-defined Networking (SDN) is becoming the most plausible solution to cope with the conventional networks’ shortcomings. The separation of the control (software) and the data (hardware) planes makes of SDN a technology-enabled to a multitude of new and promising applications and use cases for 5G network technology. Yet, initial SDN deployments considered having a single controller for the entire network. However, this arrangement is deemed to be counterproductive in large-scale and ultra-reliable networks based SDN as the controller might become the system's bottleneck. To address this issue, several multi-controller architectures were proposed. They are of three types: flat, hierarchical or hybrid. For theses architectures to be potent for 5G core networks, efficient path computation and flow tables update mechanisms are needed. In this paper, we propose HybCon, a hybrid SDN-based distributed Cloud architecture having the control plane distributed over different controller types (i.e., Fog, Edge, SDN). When a path computation request is received, HybCon involves some/all of the controllers and uses a multi-priority queueing model along with node parallelism to expedite the path computation and to cut down the synchronization overhead. Simulation results show that HybCon outperforms existing schemes in terms of path computation time, path setup latency, packet delivery ratio and communication overhead, making it a perfect solution for future 5G networks.

The exponential demand for multimedia services is one reason behind the substantial growth of mobile data traffic. Video traffic patterns have significantly changed in the past two years due to the coronavirus disease (COVID-19). The worldwide pandemic has caused many individuals to work from home and use various online video platforms (e.g., Zoom, Google Meet, and Microsoft Teams). As a result, overloaded macrocells are unable to ensure high Quality of Experience (QoE) to all users. Heterogeneous Networks (HetNets) consisting of small cells (femtocells) and macrocells are a promising solution to mitigate this problem. A critical challenge with the deployment of femtocells in HetNets is the interference management between Macro Base Stations (MBSs), Femto Base Stations (FBSs), and between FBS and FBS. Indeed, the dynamic deployment of femtocells can lead to co-tier interference. With the rolling out of the 5G mobile network, it becomes imperative for mobile operators to maintain network capacity and manage different types of interference. Machine Learning (ML) is considered a promising solution to many challenges in 5G HetNets. In this paper, we propose a Machine Learning Interference Classification and Offloading Scheme (MLICOS) to address the problem of co-tier interference between femtocells for video delivery. Two versions of MLICOS, namely, MLICOS1 and MLICOS2, are proposed. The former uses conventional ML classifiers while the latter employs advanced ML algorithms. Both versions of MLICOS are compared with the classic Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). The ML models are assessed based on the prediction accuracy, precision, recall and F-measure. Simulation results show that MLICOS outperforms the other schemes by providing the highest throughput and the lowest delay and packet loss ratio. A statistical analysis was also carried out to depict the degree of interference faced by users when different schemes are employed.

Achieving high Quality of Service (QoS) is one of the important goals in the latest 5G Heterogeneous Networks (HetNets) environments. However, ensuring fairness among users with Reduced Power Consumption (RPC) is a major challenge. Although several studies have examined the joint issue of User Association (UA), Resource Allocation (RA), and Power Allocation (PA), there is still no optimal solution that achieves QoS fairness and RPC with low complexity and processing time. This paper proposes the Power-Performance Efficient Adaptive Genetic Algorithm (P2EAGA) for solving the UA-RA-PA problem in HetNets. The UA-RA sub-problem is formulated as a ‘0/1’ Multiple Knapsack Problem (MKP) with constraints on the maximum capacity of Base Stations (BSs) along with the transport block size index. Its sub-optimal solution is given as input to the second MKP, formulated to solve the PA sub-problem with constraints on the total BS power capacity. Simulation results show that P2EAGA outperforms existing schemes in terms of variability, fairness, RPC, and QoS, including throughput, packet loss ratio, delay, and jitter. Simulation results also show that P2EAGA generates solutions that are very close to the optimal global solution compared to the Default Genetic Algorithm.

This paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows' performance by applying an Machine Learning (ML) approach to evaluate a set of features - such as latency and throughput - for every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a “low-level” application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient.

5G technology is expected to enable a plethora of new applications with distinct requirements. Provisioning resources to accommodate such applications implies having a flexible network infrastructure that can be tailored to the specific needs of each application. This can be achieved through network slicing. Still, several applications might request network slices, but their request may not be fulfilled due to lack of resources or lack of coverage and provisioning such resources is a cumbersome task. This paper describes an architecture that facilitates the dynamic leasing of resources among network operators to support cross-domain services. The cornerstone of this architecture is a brokering layer, called DBB, that relies on a blockchain-based bidding system to request resources and evaluate resource provisioning offers. The paper also presents a simulation-based use case scenario that illustrates the need for DBB and which was used to evaluate the performance of the proposed architecture.

Recently, the use of heterogeneous small-cell networks to offload traffic from existing cellular systems has attracted considerable attention. One of the significant challenges in heterogeneous networks (HetNet) is cross-tier interference, which becomes significant when macro-cell users (MUE) are in the vicinity of femtocell base stations (FBS). Indeed, the femtocell will cause significant interference to MUEs on the macrocell downlink (DL) while MUEs will induce hefty interference to the femtocell on the macrocell uplink (UL). Substantial work has focused on offloading and interference mitigation in HetNets; yet, none of them has considered the impact of cross-tier interference on quality of service (QoS), quality of experience (QoE). This paper proposes the Quality Efficient Femtocell Offloading Scheme (QEFOS) that selects the users most affected by the interference encountered and offloads them to nearby FBSs. QEFOS testing shows substantial improvements in terms of QoS and QoE perceived by users in heavy cross-tier interference scenarios in comparison with alternative approaches. In particular QEFOS's impact on throughput, packet loss ratio (PLR), peak-to-signal-noise ratio (PSNR), and structural similarity identity matrix (SSIM) was assessed.

Recently, fabrication laboratories (Fab Labs) have been shown to have a great impact on learners' academic and personal progress. As a result, an increasing effort is being put to integrate Fab Labs into schools' curricula. Yet, owing to the high cost of setting up and maintaining Fab Labs as well as the lack of sufficient funding for most schools and universities, only a limited number of institutions can afford them. In this article, we propose a new concept called Fabrication-as-a-Service (FaaS) that uses Internet of Things to democratize access to Fab Labs via enabling a wide learning community to remotely access these computer-controlled tools and equipment over the Internet. It employs a two-tier architecture consisting of a hub, deployed in the cloud, and a network of distributed Fab Labs. Each Fab Lab interacts with the hub and other digital labs via a Fab Lab Gateway. This is to support scalability and high availability of fabrication services as well as ensure the system's security. FaaS also adopts an innovative master-slave approach that uses inexpensive external hardware to monitor and control the activity of expensive fabrication equipment. This article also describes the FaaS deployment in the context of the European Union Horizon 2020 NEWTON project. Multiple scenarios have been deployed to fully illustrate the benefits of the FaaS architecture and to assess the performance of its communication protocol stack.

5G networks are anticipated to support a plethora of innovative and promising network services. These services have heterogeneous performance requirements (e.g., high-rate traffic, low latency, and high reliability). To meet them, 5G networks are entailed to endorse flexibility that can be fulfilled through the deployment of new emerging technologies, mainly software-defined networking (SDN), network functions virtualization (NFV), and network slicing. In this paper, we focus on an interesting automotive vertical use case: autonomous vehicles. Our aim is to enhance the quality of service of autonomous driving application. To this end, we design a framework that uses the aforementioned technologies to enhance the quality of service of the autonomous driving application. The framework is made of 1) a distributed and scalable SDN core network architecture that deploys fog, edge and cloud computing technologies; 2) a network slicing function that maps autonomous driving functionalities into service slices; and 3) a network and service slicing system model that promotes a four-layer logical architecture to improve the transmission efficiency and satisfy the low latency constraint. In addition, we present a theoretical analysis of the propagation delay and the handling latency based on GI/M/1 queuing system. Simulation results show that our framework meets the low-latency requirement of the autonomous driving application as it incurs low propagation delay and handling latency for autonomous driving traffic compared to best-effort traffic.

Given the shortcomings of traditional networks, software-defined networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability, and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. This paper proposes HiDCoP, a distributed high-performance control plane for path computation in large-scale SDNs along with its related solutions. HiDCoP employs a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that HiDCoP outperforms existing schemes in terms of path computation time, end-to-end delay, and transmission overhead.

Initial SDN deployments endorsed a centralized architecture with a single controller that is responsible for managing entire networks. Yet, this scheme has proven to be unscalable and unreliable. Though distributed control plane architectures have gained ground recently, they bring new concerns. A principal question that should be investigated thoroughly is how to efficiently perform path computation and update flow tables in very large SDNs. In this paper, we address this problem by proposing ScalCon, a scalable hybrid SDN architecture that distributes the control plane over different controller types (i.e., Fog, Edge, Cloud). ScalCon distributes the load of path computation among all controllers and uses a multi-priority queening model and node parallelism to cut down the synchronization overhead. Simulation results show that ScalCon outperforms existing schemes in terms of path computation time, path setup latency. end-to-end delay and communication overhead.

Given the shortcomings of traditional networks, Software-Defined Networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. In this paper, we propose DiSC, a distributed high-performance control plane for path computation in large SDNs. It endorses a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that DiSC outperforms existing schemes, including the most recent ones, in terms of path computation time, path setup latency and end-to-end delay.

Similar to IEEE 802.11e, the Wireless Access for Vehicular Environment (WAVE) uses the Enhanced Distributed Channel Access (EDCA) to provide service differentiation. Nevertheless, WAVE does not make use of the transmission opportunity (TXOP) parameter, i.e., only one packet can be transmitted per channel access. This fits well most safety applications as they usually transmit individual short messages. Yet, non-safety applications can witness a decline in their performance as they often transmit multiple long messages. In this paper, we propose an innovative scheme, called DTAS, that dynamically assigns TXOP limits to vehicles to improve non-safety applications' efficiency. DTAS targets vehicle-to-vehicle (V2V) communications and updates periodically its functionality to reflect changes in both network circumstances and mobility pattern between vehicles. To the best of our knowledge, no existing work has proposed something similar for V2V non-safety applications. Simulation results demonstrate that DTAS generates higher throughput compared to the conventional IEEE 802.11p.

The wireless access for vehicular environment (WAVE) mandates that data packets of non-safety applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels; yet, owing to vehicles' high mobility, there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to mitigate this problem. Nevertheless, they are either inefficient or cost-ineffective. In this paper, we propose a novel approach called altruistic service channel selection (ASSCH) that compels vehicles to cooperate in order to select the least congested service channels for vehicle-to-vehicle (V2V) non-safety applications. ASSCH has three phases: 1) identifying the channel's current state (i.e., free or occupied); 2) predicting channels that are likely to be free in the near future; and 3) selecting the least used channel among them. We then propose a stochastic analytical model for the throughput of V2V non-safety applications considering various factors, including the busy channel at zero, discarded by all existing IEEE 802.11p EDCA models. Simulation results demonstrate that ASSCH outperforms existing allocation-based schemes as it incurs low capture delay, low ratio of overlapping WBSS, and high throughput. Simulation results also show that our analytical model closely matches the throughput of EDCA access categories.

The Wireless Access for Vehicular Environment (WAVE) specifies that data packets of infotainment applications are to be sent within WAVE basic service sets (WBSS). These WBSS are to be established on the least congested service channels. WAVE proposes a mechanism to select such channels. Nevertheless, owing to high mobility in vehicular ad hoc networks (VANET), there is high chance of having overlapped WBSS, yielding unsatisfactory performance. Several approaches have been proposed to avoid such a scenario. Yet, they are either inefficient or cost-ineffective. In this paper, we propose ASSCH, an altruistic mechanism that impels vehicles to collaborate in order to select the least congested (i.e., used) service channel. ASSCH also includes a mechanism for WBSS termination, which is not specified in the WAVE standard. To the best of our knowledge, none of the existing works have proposed something similar. Simulation results demonstrate that our scheme handles the overlapping problem better and incurs high channel efficiency.

This paper proposes two Markovian models to analyze the performance of IEEE 802.11p EDCA mechanism for vehicle-to-vehicle (V2V) infotainment applications. The first model describes the backoff procedure and is used to compute the transmission probability of each access category (AC) while the second illustrates the contention phase after a busy channel and is used to derive the probability of collision. Both models consider backoff counter freezing as well as the internal and external collisions. In addition, they both take into account the case where the channel is sensed busy once the counter backoff reaches 0, which is not considered in almost all of existing works in the literature. Using both models, we derive an accurate formula for the normalized throughput for each AC. Simulation results are provided to demonstrate the accuracy of our analytical model.

Recently, Jung et al. [1] proposed a data access privilege scheme and claimed that their scheme addresses data and identity privacy as well as multi-authority, and provides data access privilege for attribute-based encryption. In this paper, we show that this scheme, and also its former and latest versions (i.e. [2] and [3] respectively) suffer from a number of weaknesses in terms of finegrained access control, users and authorities collusion attack, user authorization, and user anonymity protection. We then propose our new scheme that overcomes these shortcomings. We also prove the security of our scheme against user collusion attacks, authority collusion attacks and chosen plaintext attacks. Lastly, we show that the efficiency of our scheme is comparable with existing related schemes.

This paper uses two Markov chains to model IEEE 802.11p EDCA throughput over service channels. A 2-D Markov chain is constructed first to describe the backoff procedure of each access category and to infer its transmission probability. Then, a 1-D discrete Markov chain is used to describe the contention phase of an access category and to derive its collision probability. Both models consider the backoff counter freezing as well as the internal and external collisions. In addition, they both take into account the transmission opportunity (TXOP) parameter, unexploited by IEEE 802.11p, to enhance the performance of infotainment applications. Using both models, we derive an accurate model of the normalized throughput for each access category. Simulation results show that our model generates higher throughput for access categories with high priority compared to IEEE 802.11p standard.

This paper addresses the issue of selecting routing paths with minimum end-to-end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks (VANETs). Most existing schemes aim at reducing E2ED via greedy-based techniques (i.e., shortest path, connectivity, or number of hops), which make them prone to the local maximum problem and to data congestion, leading to higher E2ED. As a solution, we propose SCRP, which is a distributed routing protocol that computes E2ED for the entire routing path before sending data messages. To do so, SCRP builds stable backbones on road segments and connects them at intersections via bridge nodes. These nodes assign weights to road segments based on the collected information of delay and connectivity. Routes with the lowest aggregated weights are selected to forward data packets. Simulation results show that SCRP outperforms some of the well-known protocols in literature.

In recent years, many routing protocols have been proposed to enable infotainment applications for urban VANET. These schemes deploy greedy-based techniques that rely on various routing metrics (e.g., driving distance, road segment connectivity, or number of hops) to meet the requirements of the aforementioned applications, i.e., low end-to-end delay and high throughput. Yet, most of these protocols are exposed to the local maximum problem, inciting them to employ the carry-and-forward mechanism. This leads to high end-to-end delay and significant packet losses. To address this issue, we propose a Stable and Reliable CDS-based Routing Protocol (SRCP) that selects paths with high connectivity and low delivery delay. To accomplish this, SRCP builds backbones over road segments and estimates the delivery delay for transmitting data packets over them. It, then, assigns weights to the road segments and selects the routing path with the lowest total weight to forward data packets. Extensive simulations demonstrate that SRCP generates less end-to-end delay and provides better packet delivery ratio compared to existing schemes.

For safety applications, fast delivery is the key requirement that broadcasting protocols in VANET must fulfill. To achieve this goal, most of the existing schemes try to select the optimal next forwarder through a distributed contention phase. This latter adds few units of delay at each hop which is substantial in case of multihop transmission. A more pragmatic approach is to build a stable virtual infrastructure, ahead of time, which will be in charge of relaying messages. In this regard, this paper presents the Minimum Stable CDS-based Virtual Backbone, labeled CDS-SVB, which guarantees fast and efficient broadcasting. It selects vehicles with identical velocities to assure stability as well as low dissemination delay, and it deploys acknowledgments to ensure reliability. Simulation results show that CDS-SVB outperforms DBA-MAC in terms of dissemination delay, backbone's lifetime, and packets delivery ratio.