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.

The purpose of this paper is to design and implement a complete system for monitoring and detecting attacks and anomalies in 5G wireless local area networks. Regrettably, the development of most open source systems has been stopped, making them unable to detect emerging forms of threats. The system provides a modular framework to create and add new detection rules as new attacks emerge. The system is based on packet analysis modules and rules and incorporates machine learning models to enhance its efficiency. The use of rule-based detection establishes a strong basis for the identification of recognized threats, whereas the additional implementation of machine learning models enables the detection of new and emerging attacks at an early stage. Therefore, the ultimate aim is to create a tool that constantly evolves by integrating novel attack detection techniques. The efficiency of the system is proven experimentally with accuracy levels up to 98.57% and precision as well as recall scores as high as 92%.

Current Internet use is evolving, users are becoming mobile and are expecting data services on the go. This fact presents big challenges and opportunities to operators, which see the increase in data services as a big market still to be exploited. However, current cellular technologies cannot accommodate the demand that will arise when the true mobile Internet evolves. Addressing these challenges, we present the CARMEN project, the vision of which is to extend operatorspsila infrastructure by providing carrier grade services through a heterogeneous wireless mesh. The CARMEN architecture will provide enough bandwidth to cope with userspsila expectations at a reduced cost, thereby generating major benefits to operators and users.

Wireless Local Area Networks (WLANs) have revolutionized modern communication by providing a user-friendly and cost-efficient solution for Internet access and network resources. However, the increasing popularity of WLANs has also led to a rise in security threats, including jamming, flooding attacks, unfair radio channel access, user disconnection from access points, and injection attacks, among others. In this paper, we propose a machine learning algorithm to detect Layer 2 threats in WLANs through network traffic analysis. Our approach uses a deep neural network to identify malicious activity patterns. We detail the dataset used, including data preparation steps, such as preprocessing and division. We demonstrate the effectiveness of our solution through series of experiments and show that it outperforms other methods in terms of precision. The proposed algorithm can be successfully applied in Wireless Intrusion Detection Systems (WIDS) to enhance the security of WLANs and protect against potential attacks.