Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people’s daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.

Smart Environments try to adapt their conditions focusing on the detection, localisation, and identification of people to improve their comfort. It is common to use different sensors, actuators, and analytic techniques in this kind of environments to process data from the surroundings and actuate accordingly. In this research, a solution to improve the user’s experience in Smart Environments based on information obtained from indoor areas, following a non-intrusive approach, is proposed. We used Machine Learning techniques to determine occupants and estimate the number of persons in a specific indoor space. The solution proposed was tested in a real scenario using a prototype system, integrated by nodes and sensors, specifically designed and developed to gather the environmental data of interest. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. Additionally, the analysis performed over the gathered data using Machine Learning and pattern recognition mechanisms shows that it is possible to determine the occupancy of indoor environments.

A new era of automated services has permeated user's daily lives thanks to paradigms such as Smart City and the Internet of Things. This shift from traditional applications is possible due to the massive amount of heterogeneous devices that constitute the Internet of Things. To provide newly improved characteristics to these services, such as mobility support, high resilience, and low latency, an extension to the Cloud computing paradigm was created, called Fog computing, which brings processing and storage resources towards the edge of the network, in the vicinity of the Internet of Things environment. This scenario implies a higher complexity level needed to coordinate available resources and how applications and services use them. Although some solutions have been proposed for the Cloud, several characteristics differentiate the Cloud from the Fog, creating the need for new mechanisms for the coordination of resources, applications, and services in the Fog. This paper explains the challenges present in the Fog that call for new mechanisms to later propose an architecture to manage resources in the Fog using a hybrid approach. In the Internet of Things and South-Bound Fog Levels, a distributed management of applications and services is proposed applying choreography techniques to enable automated fast decision making. A centralized approach to orchestrate applications and services taking advantage of a global knowledge of the resources available in the network is suggested for the North-Bound Fog and Cloud Levels.