Personal information of online social networks (OSNs) is governed by the privacy policies chosen by users besides OSN’s policies. Users make these decisions using privacy mechanisms, but privacy problems and regrets are daily reported. This article reviews current privacy mechanisms and solutions. For this, we analyze all the sub-decisions and elements of online communication involved in the privacy decision-making process. However, the differences in users’ motivations and the disclosure of too sensitive information (among others) can lead to loss of privacy. In this work, we identify requirements such as automation, preference-centered, relationship-based, and multi-party privacy mechanisms, which have been more researched. But also other requirements (recently emerged), such as privacy preservation with risk metrics, explainability, and ephemeral messages. We explore all the advances made in the literature, and we have seen that most of these have been focused on matching the users’ preferences with their decision (which is not appropriate, because users cannot evaluate all of the potential privacy scenarios) instead of assessing privacy risk metrics, adaptation, and explainability. Therefore, we have identified open challenges, such as metrics for assessing privacy risks, explainable solutions for users, ephemeral communication solutions, and the application of these requirements to the multi-party privacy scenario.

One of the main current challenges of electric vehicles (EVs) is the creation of a reliable, accessible and comfortable charging infrastructure for citizens in order to enhance demand. In this paper, a multi-agent system (MAS) is proposed to facilitate the analysis of different placement configurations for EV charging stations. The proposed MAS integrates information from heterogeneous data sources as a starting point to characterize the areas where charging stations could potentially be placed. Through a genetic algorithm, the MAS is able to analyze a large number of possible configurations, taking into account a set of criteria to be optimized. Finally, the MAS returns a configuration with the areas of the city that are considered most appropriate for the establishment of charging stations according to the specified criteria.

Nowadays, systems are growing in power and in access to more resources and services. This situation makes it necessary to provide user-centered systems that act as intelligent assistants. These systems should be able to interact in a natural way with human users and the environment and also be able to take into account user goals and environment information and changes. In this paper, we present an architecture for the design and development of a goal-oriented, self-adaptive, smart-home environment. With this architecture, users are able to interact with the system by expressing their goals which are translated into a set of agent actions in a way that is transparent to the user. This is especially appropriate for environments where ambient intelligence and automatic control are integrated for the user’s welfare. In order to validate this proposal, we designed a prototype based on the proposed architecture for smart-home scenarios. We also performed a set of experiments that shows how the proposed architecture for human-agent interaction increases the number and quality of user goals achieved.