Indoor positioning systems (IPS) use sensors and communication technologies to locate objects in indoor environments. IPS are attracting scientific and enterprise interest because there is a big market opportunity for applying these technologies. There are many previous surveys on indoor positioning systems; however, most of them lack a solid classification scheme that would structurally map a wide field such as IPS, or omit several key technologies or have a limited perspective; finally, surveys rapidly become obsolete in an area as dynamic as IPS. The goal of this paper is to provide a technological perspective of indoor positioning systems, comprising a wide range of technologies and approaches. Further, we classify the existing approaches in a structure in order to guide the review and discussion of the different approaches. Finally, we present a comparison of indoor positioning approaches and present the evolution and trends that we foresee.

Distributed Denial of Service (DDoS) attacks represent the most common and critical attacks targeting conventional and new generation networks, such as the Internet of Things (IoT), cloud computing, and fifth-generation (5G) communication networks. In recent years, DDoS attacks have become not only massive but also sophisticated. Software-Defined Networking (SDN) technology has demonstrated effectiveness in counter-measuring complex attacks since it provides flexibility on global network monitoring and inline network configuration. Although several works have proposed to detect DDoS attacks, most of them did not use up-to-date datasets that contain the newest threats. Furthermore, only a few previous works assessed their solutions using simulated scenarios, easing the migration to production networks. This document presents the implementation of a modular and flexible SDN-based architecture to detect transport and application layer DDoS attacks using multiple Machine Learning (ML) and Deep Learning (DL) models. Exploring diverse ML/DL methods allowed us to resolve which methods perform better under different attack types and conditions. We tested the ML/DL models using two up-to-date security datasets, namely CICDoS2017 and CICDDoS2019 datasets, and they showed accuracy above 99% on classifying unseen traffic (testing set). We also deployed a simulated environment using the network emulator Mininet and the Open Network Operating System (ONOS) SDN controller. In this experimental setup, we demonstrated high detection rates, above 98% for transport DDoS attacks and up to 95% for application-layer DDoS attacks.

Sequences and their properties have been studied widely in different research areas because they have useful characteristics which are used in many applications. Correlation Functions such as Golay Correlation functions (GCF) are a measure of similarity among sequences and, besides, they are a powerful property used extensively in many research areas. It is known that unitary sequence sets with ideal autocorrelation and cross-correlation functions do not exist, and this represents the main problem to construct orthogonal sequences given that are limited by bounds. In Wireless Communication Systems (WCS), orthogonal sequences are used as access codes which play a very important role to increase capacity and to provide higher robustness to interference. This survey describes the properties of these sequences and their relationships that make them attractive to be used in communication systems. We consider the correlation properties to classify and describe four main families of sequences: Traditional, Complementary, Interference Free Window, and Complex. Finally, a discussion on their advantages and disadvantages is given considering the WCS scenario, and the discussion leads to some challenges for future applications which offer some ideas to motivate research to improve those properties.

Intelligent transportation systems (ITSs) are currently under intense research and development for making transportation safer and more efficient. The development of such vehicular communication systems requires accurate models for the propagation channel. A key characteristic of these channels is their temporal variability and inherent time-changing statistics, which have a major impact on electromagnetic propagation prediction.

The evolution of 6G networks necessitates robust authentication and key agreement (AKA) protocols to counter emerging security challenges, particularly the threat posed by quantum computing. Existing 5G-AKA protocols remain vulnerable to user tracking, replay attacks, and false base station impersonation, while Elliptic Curve Cryptography (ECC) and RSA-based key exchanges face imminent obsolescence due to quantum attacks. This study examines the vulnerabilities of 5G-AKA and explores hybrid post-quantum cryptography (PQC) as a transitional security solution for 6G networks. We evaluate NIST-standardized PQC algorithms, focusing on their computational overhead, key size efficiency, and adaptability to heterogeneous network environments, including IoT and ultra-low latency applications. A comparative analysis of hybrid cryptographic schemes demonstrates that lattice-based (Kyber, Dilithium), hash-based (SPHINCS+), and code-based (BIKE, Classic McEliece) PQC techniques provide varying trade-offs between security, efficiency, and deployment feasibility. Furthermore, we propose a quantum-resistant 6G-AKA framework, integrating hybrid PQC, AI-driven trust mechanisms, and decentralized authentication to ensure scalability and interoperability. Experimental benchmarks highlight potential performance constraints, including latency in PQC-based key exchange and resource limitations in edge computing. Addressing these challenges requires the optimization of lightweight PQC implementations, formal security validation, and global standardization efforts. Our findings provide a roadmap for the secure transition to 6G authentication protocols, ensuring resilience against both classical and quantum threats.