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This study explores the process of creating avatars for Virtual Reality and metaverse environments using a variety of specialized applications, including VRChat, Ready Player Me, VRoidStudio, Mixamo, Convai, and MetaHuman. By evaluating these platforms, the research identifies the strengths and limitations of each tool in terms of customization, integration, and overall user experience. The practical implementation focuses on avatar creation within Unity and Unreal Engine, highlighting the technical aspects of rigging, animation, and real-time rendering. The study also delves into the broader implications of avatar use, particularly concerning privacy and security. Our findings reveal that while each platform offers unique advantages, the choice of tool significantly impacts the visual fidelity and performance of avatars in virtual environments. For the ease of use, Ready Player Me stands out with its intuitive interface. VRoidStudio is notable for its high degree of customizability, allowing for detailed avatar personalization. In terms of high-quality graphics, MetaHuman leads the way with its advanced graphical fidelity. At the same time, while some platforms excel in personalizing avatars or integrating with development environments (VRoidStudio, Ready Player Me), others are constrained by their limited flexibility or their platform-specific availability (MetaHuman, Mixamo). Understanding the technical intricacies of these tools is crucial for developing more immersive and secure metaverse experiences. [ABSTRACT FROM AUTHOR]

The future of Autonomous Vehicles (AVs) will experience a breakthrough when collective intelligence is employed through decentralized cooperative systems. A system capable of controlling all AVs crossing urban intersections, considering the state of all vehicles and users, will be able to improve vehicular flow and end accidents. This type of system is known as Autonomous Intersection Management (AIM). AIM has been discussed in different articles, but most of them have not considered the communication latency between the AV and the Intersection Manager (IM). Due to the lack of works studying the impact that the communication network can have on the decentralized control of AVs by AIMs, this paper presents a novel latency-aware deep reinforcement learning-based AIM for the 5G communication network, called AIM5LA. AIM5LA is the first AIM that considers the inherent latency of the 5G communication network to adapt the control of AVs using Multi-Agent Deep Reinforcement Learning (MADRL), thus obtaining a robust and resilient multi-agent control policy. Beyond considering the latency history experienced, AIM5LA predicts future latency behavior to provide enhanced security and improve traffic flow. The results demonstrate huge safety improvements compared to other AIMs, eliminating collisions (on average from 27 to 0). Further, AIM5LA provides comparable results in other metrics, such as travel time and intersection waiting time, while guaranteeing to be collision-free, unlike the other AIMs. Finally, compared to other traffic light-based control systems, AIM5LA can reduce waiting time by more than 99% and time loss by more than 95%.

Current e-learning forms are commonly based on improving the learning process through the enhancement of certain skills in students, such as collaborative, competitive or problem-based learning. However, it seems that there is still no e-learning "formula" that gathers the implementation of a number of more generic educational principles in a single e-learning system or platform. Therefore, this paper presents a web-based framework for the creation, development and implementation of heterogeneous learning environments called CADI. It offers a graphical user interface for students to interact with only the need for any generic WiFi compliant device. Its innovative design allows the instructor to select and combine the developed resources to create varied activities, providing content adapted to every single device. The usefulness of the system was tested by introducing a case study to a group of senior college students. The experience showed that the use of the system helped students to achieve better results in their evaluation. It also showed that the students had no problems interacting with it from the outset, promoting their active learning. Design, implementation and assessment results of the system are also presented and analyzed. (Contains 6 tables and 9 figures.)

WiFi-based passive methods are becoming a common tool to count, estimate, and/or locate people. One area of applicability is the development of intelligent control system for traffic management in urban areas, so that these systems are able to take into account not only vehicles’ behaviors but also pedestrians’, as important actors in the road scenario. In this work, we present the performance evaluation in terms of accuracy of a WiFi-based passive method used to identify pedestrians, classify them as moving pedestrians or static pedestrians, and for the latter, to locate them in a traffic intersection. The proposed algorithm is implemented in a low-cost development board and tested through several experiments in a real outdoor scenario. Our proposal is compared with several classic Machine Learning (ML) algorithms, specifically with Binary Logistic Regression, Support Vector Classification, Gaussian Naive Bayes, Random Forest, and k-Nearest Neighbors. Results show that despite the simplicity of our method, the outcomes are similar or better than most of the ML techniques, without the expected complexity or computational requirements that the latter required.

Summary: The deployment of Internet of Things (IoT) solutions in smart cities or industrial environments (Industrial IoT, IIoT) demands careful consideration in terms of user‐centric or system‐centric target metrics. A better monitoring system able to transform performance outputs into decision‐making and intelligent actions requires less restrictive performance evaluation methods. Classic approaches to performance evaluation in telecommunication networks rely on Quality of Service (QoS) and/or Quality of user Experience (QoE) assessment models. However, the new IoT paradigm establishes a completely different scenario, where, for instance, consumers might no longer be users but machines. In this paper, we propose the evaluation of the performance of IoT services and applications comprising the combination of four quality measures, namely, Quality of Data (QoD), Quality of Information (QoI), Quality of user Experience (QoE), and Quality Cost (QC). The proposal is analyzed using computing simulations. Specifically, we improve the simulation tool FLoRa (Framework for LoRa) incorporating additional LoRa Wide Area Network (LoRaWAN) features. LoRaWAN is a Low‐Power Wide Area Network (LPWAN) technology that operates on the LoRa modulation scheme. Long coverage ranges, low power consumption, and support for a massive number of IoT devices using a limited infrastructure are the main assets of LoRaWAN, establishing it as one of the top IoT technologies. Results show that it is easier and more efficient to disassociate metrics into different dimensions in order to provide a clear vision of the performance of IoT services. This performance evaluation method can be customized and applied to different IoT markets. [ABSTRACT FROM AUTHOR]