Abstract
Traditionally, there are two main market designs for user connected smart objects and smart appliances: cloud dependent and/or local centralized servers but both approaches bring concerns to the end-user side. The cloud-based approach raises concerns related with (apart from technical configuration and setup) security and privacy as user data may be exchanged with the cloud. Even in solutions that keep user data in the user side raises doubts and uncertainty to the final-user. On the other hand, the solutions based on local server may mitigate the security and privacy concerns but usually require end-user technical configuration and setup besides the fact that the local server becomes a single point of failure. Our aim is to address these concerns by the adoption of a peer-to-peer, self-contained and interoperable approach to ensure truly plug-and-play, to keep user data in the user side and to allow seamlessly interoperability among end-users' devices hence towards real Smart Environments. In this first paper we evaluate, for the first time, the oneM2M world wide IoT standard over peer-to-peer networking and the preliminary results are very promising, allowing us to move forward addressing other requirements such as IP provisioning, security and privacy, efficient peer discovery, etc. © 2025 Elsevier B.V., All rights reserved.

Abstract
Virtual reality (VR) offers an immersive experience by simulating realistic environments, reducing the need for physical spaces and equipment. This technology addresses the challenges of traditional testing and learning, which often involve high costs, hardware limitations, and health concerns. In this paper, we use Blender, an open-source 3D creation software, to model a virtual vineyard and a wine sensory analysis laboratory. The vineyard is populated with various plants across a large virtual space, and the sensory lab is designed to simulate wine-tasting processes. The virtual lab enables users to explore and understand the sensory characteristics of wine in a controlled, immersive environment, providing an effective alternative to physical labs. Our simulations demonstrate that VR can replicate real-world environments with high fidelity and improve user engagement. The results suggest that using VR for wine sensory analysis can enhance educational outcomes, reduce costs, and offer an innovative platform for research and learning. © 2025 Elsevier B.V., All rights reserved.

Abstract

Abstract

Abstract
We present a novel solution for automatic task allocation in multi-device environments, where configured robots compete for task assignment when announcing tasks, minimizing manual intervention. To this end, we propose the specification of a task assignment system and a task-oriented programming method aimed at automating processes and optimizing resource utilization in multiple controller environments. The proposed solution with its market-based algorithm and developed architecture improves the adaptability, scalability and overall efficiency of the system. The research discussion extends to broader implications that are consistent with the overall goal of improving robot capabilities in various deployment scenarios. © 2025 Elsevier B.V., All rights reserved.

Abstract
This paper proposes an Artificial Intelligence (AI) driven solution, Chatto, designed for emotional support among older adults. It integrates emotion recognition, Natural Language Processing (NLP), and human-computer interaction (HCI) to facilitate meaningful interactions and aid in self-emotion regulation while providing caregivers with tools to monitor and support the elder's emotional state remotely. The proposal includes an infrastructure to personalize the system through a human labeling approach and retraining of the deep learning models. The findings revealed the solution's impact on the emotional well-being of the elderly and identified potential improvements in emotion detection, conversational features, and user interface. These improvements were based on feedback from feasibility and usability tests conducted with caregivers and older adults subject to the influence of demographic variables, such as age, cultural background, and technological literacy. © 2025 Elsevier B.V., All rights reserved.