5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional “one-size-fits-all” network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and sub-categories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.

The performance of protocols and architectures for upcoming vehicular networks is commonly investigated by means of computer simulations, due to the excessive cost and complexity of large-scale experiments. Dependable and reproducible simulations are thus paramount to a proper evaluation of vehicular networking solutions. Yet, we lack today a reference dataset of vehicular mobility scenarios that are realistic, publicly available, heterogeneous, and that can be used for networking simulations straightaway. In this paper, we contribute to the endeavor of developing such a reference dataset, and present original synthetic traces that are generated from high-resolution real-world traffic counts. They describe road traffic in quasi-stationary state on three highways near Madrid, Spain, for different time-spans of several working days. To assess the potential impact of the traces on networking studies, we carry out a comprehensive analysis of the vehicular network topology they yield. Our results highlight the significant variability of the vehicular connectivity over time and space, and its invariant correlation with the vehicular density. We also underpin the dramatic influence of the communication range on the network fragmentation, availability, and stability, in all of the scenarios we consider.

Jamming attacks are becoming increasingly common and pose a significant threat to the security and reliability of wireless sensor networks (WSNs). These attacks can be difficult to detect, as they often operate in a stealthy manner, disrupting communication between sensors. Artificial intelligence (AI) techniques have the potential to be highly effective in detecting jamming attacks. However, the adoption of AI-based techniques for detecting jamming attacks has been limited due to the scarcity of up-to-date and accurate data of these attacks. Privacy-aware collaboration among agents is expected to be essential in building robust AI-based models for detecting jamming attacks in WSNs. In this context, we propose a novel framework that uses collaborative federated learning (FL) to enable privacy-aware distributed learning among multiple agents without sharing their sensitive data. This can be particularly important in jamming attack detection, where data may contain sensitive information that should not be shared. In addition, we design a novel secure aggregation scheme to protect the FL aggregation service from reverse-engineering attacks. The effectiveness of our proposed framework was tested using the public Wireless Sensor Networks Dataset (WSN-DS) that includes four types of well-known jamming attacks (i.e., constant jamming, reactive jamming, random jamming, and deceptive jamming). The results of a thorough experiment using the WSN-DS data set demonstrate the effectiveness and high accuracy/F1-score (99%) in detecting jamming attacks while also maintaining participant privacy.

The futuristic sixth-generation (6-G) networks will empower ultrareliable and low latency communications (URLLC), enabling a wide array of mission-critical applications such as mobile edge computing (MEC) systems, which are largely unsupported by fixed communication infrastructure. To remedy this issue, unmanned aerial vehicle (UAV) has recently come to the limelight to facilitate MEC for internet of things (IoT) devices as they provide desirable line-of-sight (LoS) communications compared to fixed terrestrial networks, thanks to their added flexibility and 3-D positioning. In this article, we consider UAV-enabled relaying for MEC systems for uplink transmissions in 6-G networks, and we aim to optimize mission completion time subject to the constraints of resource allocation, including UAV transmit power, UAV CPU frequency, decoding error rate, blocklength, communication bandwidth, and task partitioning as well as 3-D UAV positioning. Moreover, to solve the nonconvex optimization problem, we propose three different algorithms, including successive convex approximations, altered genetic algorithm (AGA), and smart exhaustive search. Thereafter, based on time-complexity, execution time, and convergence analysis, we select AGA to solve the given optimization problem. Simulation results demonstrate that the proposed algorithm can successfully minimize the mission completion time, perform power allocation at the UAV side to mitigate information leakage and eavesdropping as well as map a 3-D UAV positioning, yielding better results compared to the fixed benchmark submethods. Lastly, subject to 3-D UAV positioning, AGA can also effectively reduce the decoding error rate for supporting URLLC services.

Next-generation mobile networks are expected to meet the requirements of a wide range of new vertical services. Hence, the network slicing concept has been introduced, in which Mobile Virtual Network Operators (MVNOs) are allowed to provide various types of services over the same physical infrastructure, owned by an Infrastructure Provider (InP). To cope with an ever-changing traffic demand, MVNOs seek to pre-allocate/reconfigure the resources at the base stations in an anticipatory manner, based on traffic demand predictions. Ideally, conducting per-slice traffic forecasting requires information that is likely to disclose MVNO confidential information (i.e., business strategy or private user data). To secure data ownership while conducting traffic forecasting, we propose the Federated Proximal Long Short-Term Memory (FPLSTM) framework, which allows MVNOs to train their local models with their private dataset at each base station; subsequently, an associated InP global model can be updated through the aggregation of the local models. The results obtained by training the models on a real-world dataset indicate that the forecasting performance of our proposed approach is as accurate as state-of-the-art centralized solutions, while improving data privacy. To enable scalability, we further propose the Information-based Clustering FPLSTM (IC-FPLSTM) and Random Clustering FPLSTM (RC-FPLSTM) frameworks, dealing with large-scale cellular networks. These solutions demonstrate computation and communication cost efficiency significantly above the state-of-the-art.

Consumer-centric energy-efficient 6G networks for Industry 5.0 are essential with the emergence of artificial intelligence and internet of things (AIoT) devices, necessitating ultra-reliable low-latency communication (URLLC) services. Moreover, multi unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems offer a promising solution to provide URLLC services in dynamic environments. However, they face significant challenges such as communication and trajectory design, high energy consumption, and reliability issues. Our paper introduces a novel approach to address these challenges by developing low-complexity intelligent optimization algorithms based on successive convex approximations (SCA). Our method aims to minimize the weighted sum energy consumption of ground AIoT devices and UAVs while satisfying the quality-of-service (QoS) requirements of URLLC. Simulation results demonstrate the superior performance of our proposed algorithms compared to standard fixed benchmark algorithms, achieving reduced energy consumption and optimizing UAV trajectories. This innovation enhances the sustainability and efficiency of multi-UAV-assisted MEC systems, facilitating the deployment of URLLC services in AIoT environments within consumer-centric energy-efficient 6G networks.