Wireless local area networks (WLANs) empowered by IEEE 802.11 (Wi-Fi) hold a dominant position in providing Internet access thanks to their freedom of deployment and configuration as well as the existence of affordable and highly interoperable devices. The Wi-Fi community is currently deploying Wi-Fi 6 and developing Wi-Fi 7, which will bring higher data rates, better multi-user and multi-AP support, and, most importantly, improved configuration flexibility. These technical innovations, including the plethora of configuration parameters, are making next-generation WLANs exceedingly complex as the dependencies between parameters and their joint optimization usually have a non-linear impact on network performance. The complexity is further increased in the case of dense deployments and coexistence in shared bands. While classical optimization approaches fail in such conditions, machine learning (ML) is able to handle complexity. Much research has been published on using ML to improve Wi-Fi performance and solutions are slowly being adopted in existing deployments. In this survey, we adopt a structured approach to describe the various Wi-Fi areas where ML is applied. To this end, we analyze over 250 papers in the field, providing readers with an overview of the main trends. Based on this review, we identify specific open challenges and provide general future research directions.

In this survey we attempt to describe the Quality of Service (QoS) mechanisms employed by Medium Access Control (MAC) protocols designed for ad-hoc networks. We begin with background information: an overview of the related work, the definition of QoS and QoS-related metrics, a general description of contention-free and contention-based protocols for wireless networks, a discussion of issues affecting QoS provisioning in ad-hoc networks, as well as a novel classification of the QoS mechanisms. Then, each mechanism is briefly explained and implementation examples from different protocols are provided. Furthermore, a separate section is devoted to the completed and ongoing standardization work in the field. Afterwards, an extensive comparison of salient features, advantages and disadvantages of all described MAC mechanisms is given in order to guide future protocol designers. Finally, we comment on the most probable future research directions. Based on the presented survey, we observe that QoS provisioning is not only challenging but also a significant contemporary research problem. The protocol designs presented in the literature usually involve trade-offs between certain metrics, and currently there is no ideal solution which deals with all the issues affecting ad-hoc networks. Therefore, we trust that this survey will be of great help to designers of future QoS-aware protocols.

Research efforts at network design in the area of Autonomic Networking and Self-Managing Networks have reached a maturity level that forms a strong foundation toward standardization of architectural principles of the Self-Managing Future Internet. Therefore, an Industry Specification Group (ISG) on Autonomic network engineering for the self-managing Future Internet (AFI) has been established under the auspices of the European Telecommunications Standards Institute (ETSI). Upon its creation, the main stakeholders agreed to harmonize the previous developments and the most recent trends in the very vital field of autonomic and self-managing networks. Particularly, the life cycle of AFI is structured by Work Items providing the foundation for ETSI Group Specifications. So far AFI has been focusing on scenarios, use cases, and requirements for the autonomic/selfmanaging Future Internet, as well as on architectural reference model for autonomic networking and self-management. Most recently, AFI has continued with a new Work Item on requirements analysis and specification of implementation-oriented solutions for autonomics and self-management. At the same time, as a part of the global ecosystem, AFI is establishing strategic liaisons with the standards developing organizations and research community.

Coordination among multiple access points (APs) is integral to IEEE 802.11bn (Wi-Fi 8) for managing contention in dense networks. This letter explores the benefits of Coordinated Spatial Reuse (C-SR) and proposes the use of reinforcement learning to optimize C-SR group selection. We develop a hierarchical multi-armed bandit (MAB) framework that efficiently selects APs for simultaneous transmissions across various network topologies, demonstrating reinforcement learning’s promise in Wi-Fi settings. Among several MAB algorithms studied, we identify the upper confidence bound (UCB) as particularly effective, offering rapid convergence, adaptability to changes, and sustained performance.

The IEEE 802.11s amendment for wireless mesh networks does not provide incentives for stations to cooperate and is particularly vulnerable to selfish insider attacks in which a legitimate network participant hopes to increase its QoS at the expense of others. In this tutorial we describe various attacks that can be executed against 802.11s networks and also analyze existing attacks and identify new ones. We also discuss possible countermeasures and detection methods and attempt to quantify the threat of the attacks to determine which of the 802.11s vulnerabilities need to be secured with the highest priority.