Smart home energy management (SHEM) systems can introduce adjustments in the working period and operations of home appliances to allow for energy cost savings, which can however affect the quality of experience (QoE) perceived by the user. This paper analyses this issue and proposes a QoE-aware SHEM system, which relies on the knowledge of the annoyance suffered by users when the operations of appliances are changed with respect to the ideal user's preferences. Accordingly, a number of profiles describing different usages are created in the design phase. At the deployment stage, users behavior and annoyance are registered to assign one of these profiles per appliance. The assigned profile is then exploited by the QoE-aware cost saving appliance scheduling and the QoE-aware renewable source power allocation algorithms. The former is aimed at scheduling controlled loads based on users profile preferences and electricity prices. The latter re-allocates appliances' operations whenever a surplus of energy is available by renewable energy sources. Experimental results demonstrate that the annoyance perceived by users is severely diminished with respect to a QoE-unaware strategy, at the expenses of only a limited reduction in energy saving.

In this paper, we analyze the combination of Vehicular Ad-hoc NETworks (VANETs) with the Social Internet of Things (SIoT), i.e., the Social Internet of Vehicles (SIoV). In the SIoV every vehicle is capable of establishing social relationships with other vehicles in an autonomous way with the intent of creating an overlay social network that can be exploited for information search and dissemination in VANET applications. The contribution of this paper is two-fold: firstly, we define some relationships which can be established between the vehicles and between the vehicles and the road side units (RSUs); secondly, we propose a SIoV middleware which extends the functionalities of the Intelligent Transportation Systems Station Architecture (ITS SA), defined by ISO and ETSI standards, to take into account the elements needed to integrate VANETs in the SIoT. Additionally, we present results of software simulations analyzing realistic vehicular mobility trace in order to study the characteristics of the resulting social network structure.

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

Most Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases for an effective training. As these are not easily available, a good solution is to augment the databases with appropriate data augmentation (DA) techniques, which are typically based on either geometric transformation or oversampling augmentations (e.g., generative adversarial networks (GANs)). However, it is not always easy to understand which DA technique may be more convenient for FER systems because most state-of-the-art experiments use different settings which makes the impact of DA techniques not comparable. To advance in this respect, in this paper, we evaluate and compare the impact of using well-established DA techniques on the emotion recognition accuracy of a FER system based on the well-known VGG16 convolutional neural network (CNN). In particular, we consider both geometric transformations and GAN to increase the amount of training images. We performed cross-database evaluations: training with the "augmented" KDEF database and testing with two different databases (CK+ and ExpW). The best results were obtained combining horizontal reflection, translation and GAN, bringing an accuracy increase of approximately 30%. This outperforms alternative approaches, except for the one technique that could however rely on a quite bigger database.

This paper addresses the issue of evaluating the Quality of Experience (QoE) for Internet of Things (IoT) applications, with particular attention to the case where multimedia content is involved. A layered IoT architecture is firstly analyzed to understand which QoE influence factors have to be considered in relevant application scenarios. We then introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a use case related to the remote monitoring of vehicles during driving practices, which is used to validate the proposed layered model, and we discuss a second use case for smart surveillance, to emphasize the generality of the proposed framework. The effectiveness in evaluating classes of influence factors separately is demonstrated.

In this paper, a first approach for evaluating the Quality of Experience (QoE) for IoT (Internet of Things) applications is presented. Firstly, a layered IoT architecture is analysed to understand which QoE influence factors have to be considered in relevant application scenarios. Secondly, we introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a vehicular MIoT application that has been used to conduct subjective quality assessments to verify the performance of the proposed approach.