The gaming industry plays a crucial role in the realm of entertainment within our society. However, from Monopoly to Flight Simulators, serious games have also been appealing tools for learning a new language, conveying values, or training skills. The resurgence of Artificial Intelligence (AI) and data science in the last decade presents a unique window of opportunity for its integration into video games. This integration is of particular interest due to the vast amount of data that can be collected through a game, which is needed to feed the AI algorithms. This paper aims to identify relevant research paths in the intersection of serious games, AI, and computational social science, particularly in their utilization as novel research tools to comprehend human behavior and society. To provide a comprehensive context, we also present an overview of the serious game research field identifying the most prominent application areas and analyzing applications of AI in serious games that hold great potential for computational social science research. The goal of our work is to establish a valuable framework for researchers interested in utilizing serious games as a novel tool for AI-supported social research.

The increased use of wearables in recent years has fostered a great technological development in this area, although without the appropriate supervision usability may go first than security. In addition to this, the fact that wearables have been requiring more and more personal data from the user makes them attractive devices for an attacker. In this paper we propose a set of tests for evaluating the security and privacy of wearables and we apply them to analyse the security and privacy of a set of commercial wearables that are targeted at minors, who represent a group with especially high requirements in this regard. We define the testing scenario, expose the tools to support the research, and specify the testing process to be followed. Based on the obtained results, although the considered low-end devices are broadly speaking less secure than high-end ones, most of them present security and privacy flaws, which illustrates the necessity of regulation that ensures the fulfilment of appropriate security and privacy requirements.

Under the Internet of Things paradigm, the emergence and use of a wide variety of connected devices and personalized telematics services have proliferated recently. As a result, along with the penetration of these devices in our daily lives, the users’ security and privacy have been compromised due to some weaknesses in connected devices and underlying applications. This article focuses on analyzing the security and privacy of such devices to promote safe Internet use, especially by young people. First, the connected devices most used by the target group are classified, and an exhaustive analysis of the vulnerabilities that concern the user is performed. As a result, a set of differentiated security and privacy issues existing in the devices is identified. The study reveals that many of these vulnerabilities are related to the fact that device manufacturers often prioritize functionalities and services, leaving security aspects in the background. These companies even exploit the data linked to the use of these devices for various purposes, ignoring users’ privacy rights. This research aims to raise awareness of severe vulnerabilities in devices and to encourage users to use them correctly. Our results help other researchers address these issues with a more global perspective.

The demand of novel IoT and smart city applications is increasing significantly and it is expected that by 2020 the number of connected devices will reach 20.41 billion. Many of these applications and services manage real-time data analytics with high volumes of data, thus requiring an efficient computing infrastructure. Edge computing helps to enable this scenario improving service latency and reducing network saturation. This computing paradigm consists on the deployment of numerous smaller data centers located near the data sources. The energy efficiency is a key challenge to implement this scenario, and the management of federated edge data centers would benefit from the use of microgrid energy sources parameterized by user’s demands. In this research we propose an ANN predictive power model for GPU-based federated edge data centers based on data traffic demanded by the application. We validate our approach, using real traffic for a state-of-the-art driving assistance application, obtaining 1 hour ahead power predictions with a normalized root-mean-square deviation below 7.4% when compared with real measurements. Our research would help to optimize both resource management and sizing of edge federations.