Our tools and appliances are becoming increasingly more intelligent and interconnected, giving birth to an “Internet of Things” that can be used to support new types of cyber-physical systems (CPSs). While many CPSs are human-centric applications where humans are an essential part of the system, unfortunately, most of these systems still consider the human as an external and unpredictable element to the control loop. In order for these systems to better serve human needs, future CPSs will need to bolster a closer tie with the human element, through Human-in-the-Loop controls that take into consideration human intents, psychological states, emotions and actions inferred through sensory data. This area is a natural confluence of multidisciplinary focus but currently lacks a general understanding of the underlying requirements, principles and theory. As far as we know, this survey is the first effort towards extending the field's knowledge through an in-depth research of the state-of-the-art and a critical overview of the current taxonomic efforts in the area of Human-in-the-Loop CPSs. On top of this research, a novel taxonomic exercise focused on the general roles of the human component together with a requirement analysis, are presented.

As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype.

Mobile sensing in Cyber-Physical Systems has been evolving proportionally with smartphones. In fact, we are witnessing a tremendous increase in systems that sense various facets of human beings and their surrounding environments. In particular, the detection of human emotions can lead to emotionally-aware applications that use this information to benefit people's daily lives. This work presents the implementation of a Human-inthe- loop emotionally-aware Cyber-Physical System that attempts to positively impact its user's mood through moderate walking exercise. Data from smartphone sensors, a smartshirt's electrocardiogram and weather information from a web API are processed through a machine learning algorithm to infer emotional states. When negative emotions are detected, the application timely suggests walking exercises, while providing real-time information regarding nearby points of interest. This information includes events, background music, attendance, agitation and general mood. In addition, the system also dynamically adapts privacy and networking configurations based on emotions. The sharing of the user's location on social networks and the device's networking interfaces are configured according to user-defined rules in order to reduce frustration and provide a better Quality of Experience.

This paper presents the development of a control system for learning environments specialized in special education. Amongst its various possible uses, the system focuses on managing the class attendance of special education students, teachers and other classroom users through the use of pervasive and ubiquitous technologies. For developing tests and obtaining results, electronic devices that monitor and control the environment were used, in particular, SunSpot devices. These nodes detect and store environmental data in an omnipresent distributed database. Another contribution present in this work is the use of a hybrid middleware for controlling the application and the devices used in these environments.

Smartphones have spawned a new generation of people-centric sensing applications, where their sensors gather information from users to achieve a wide range of objectives, from fitness monitoring to the urban-wide management of traffic. The connections between devices are of utmost importance for these systems, in fact, switching between the multitude of network interfaces available to smartphones (3G/4G, WiFi) can be beneficial to improve connectivity, the distribution of network traffic and save battery power. Current solutions in the literature for the management of network interfaces are limited in the sense that they only consider either application requirements or the system's status. In this paper, we propose a new model that considers not only these aspects but also the human-context, that is, the user's position and activity to intelligently manage which interfaces should be used. To support the switching between different interfaces without interrupting existing connections, we also discuss several handoff techniques. Finally, we present an experimental evaluation of our model based on Multipath TCP.