The increasing amount of human-related and/or human-originated data in current systems is both an opportunity and a challenge. Nevertheless, despite relying on the processing of large amounts of data, most of the so-called smart systems that we have nowadays merely consider humans as sources of data, not as system beneficiaries or even active “components.” For truly smart systems, we need to create systems that are able to understand human actions and emotions, and take them into account when deciding on the system behavior. Naturally, in order to achieve this, we first have to empower systems with human sensing capabilities, possibly in ways that are as inconspicuous as possible. In this context, in this article we survey existing approaches to unobtrusive monitorization of human beings, namely, of their activity, vital signs, and emotional states. After setting a taxonomy for human sensing, we proceed to present and analyze existing solutions for unobtrusive sensing. Subsequently, we identify and discuss open issues and challenges in this area. Although there are surveys that address some of the concerned fields of research, such as healthcare, human monitorization, or even the use-specific techniques like channel state information or image recognition, as far as we know this is the first comprehensive survey on unobtrusive sensing of human beings.

Use of wireless sensor networks (WSNs) in conjunction with mobile devices is growing on a daily basis. Mobile phones are not only personal devices that provide communication services, but also versatile and resourceful elements in the Internet of Things (IoT). Nevertheless, despite the referred growth, one key aspect for real-world IoT deployments is still largely overlooked: reliability. In this respect, there is a clear gap between theoretical models and real implementations, as no practical reliability mechanisms are in place for IoT. Proposing, studying, and providing IoT reliability solutions is, thus, fundamental for the massive deployment of IoT technology across all sectors of society. In this context, the main purpose of this paper is to propose, explore, and assess reliability mechanisms for IoT scenarios, based on the use of redundant routes and devices that take advantage of mobile devices, as key elements of IoT. These mechanisms were evaluated in a variety of failure scenarios, both theoretically and through implementations constructed with real, off-the-shelf equipment, showing their good capability to maintain effective communication even in the presence of link failures.

The next generation of mobile networks, widely known as 5G, was designed to respond to the next 10 years’ communication challenges. 5G will be an essential structure on which all types of electronic communications will rely. Built on the emerging 5G technology, we developed a vertical solution for blended learning environments within a Portuguese national project scope. The project aims to propose an integrated demonstration of a set of products capable of being part and providing services in the future 5G networks framework. To this end, we adopted a bottom-up approach to design and develop a product named5GOpenclasses, where we leveraged FIWARE middleware to manage all entities. This paper presents the architecture, technological platform, associated data structures, and end-user applications of5GOpenclasses. We also present the design of innovative location-based service for blended learning environments. This paper is the first step of the proposed product towards its quantitative evaluation when running Over-the-Top on both 4G and 5G networks. Although successful unit tests were carried out in what concerns the functional outcome, the integration tests for quantitative results depend on the availability of other project components.

The progressive incorporation of humans as active players within the Internet of Things domain, has positioned the smartphone as one of the main elements of this environment. The multiple functionalities that this device offers, along with its rapid evolution, have benefited usability, thus improving user experience, making this appliance a versatile pocket assistant. Nowadays, these devices comprise a large number of special-purpose sensors capable of collecting a variety of information, that along with the data from other mobile applications such as online social networks, is the fuel for new IoT systems. Much of this data is used to offer innovative services in several areas, making the smartphone an ideal medium for acquiring information. Unfortunately, the control of data flows between smartphones and new IoT systems is scarce, triggering concerns around users’ privacy. Also, we could add to the list the limited privacy preservation mechanisms in current mobile operating systems. It is for this reason that this article aims to propose a privacy-preserving framework that can be integrated into people-centric IoT systems. Besides introducing the architecture of our PACHA framework, the manuscript will present the new vision of our ISABELA platform derived from the integration with the privacy-preserving model.