Obtrusive sleep apnea (OSA) is one of the most important sleep disorders because of it has a direct adverse impact on the quality of life. Intellectual deterioration, decreased psychomotor performance, behaviour and personality disorders are some of the consequences of OSA. Therefore, a real-time monitoring of this disorder is a critical need in healthcare solutions. There are several systems for OSA detection. Nevertheless, despite their promising results, these systems not guiding their treatment. For these reasons, this research presents an innovative system for both to detect and support of treatment of OSA of elderly people by monitoring of multiple factors such as sleep environment, sleep status, physical activities, and physiological parameters as well as the use of open data available in smart cities. Our system architecture performs two types of processing. On the one hand, a pre-processing based on rules that enables the sending of real-time notifications to responsible for the care of elderly, in the event of an emergency situation. This pre-processing is essentially based on a Fog Computing approach implemented in a smart device operating at the edge of the network that additionally offers advanced interoperability services: technical, syntactic, and semantic. On the other hand, a batch data processing that enables a descriptive analysis that statistically details the behaviour of the data and a predictive analysis for the development of services, such as predicting the least polluted place to perform outdoor activities. This processing uses Big Data tools on Cloud Computing. The performed experiments show a 93.3 % of effectivity in the AQI prediction to guide the OSA treatment. The system’s performance has been evaluated in terms of latency. The achieved results clearly demonstrate that the pre-processing of data at the edge of the network improves the efficiency of the system.

The maritime industry expects several improvements to efficiently manage the operation processes by introducing Industry 4.0 enabling technologies. Seaports are the most critical point in the maritime logistics chain because of its multimodal and complex nature. Consequently, coordinated communication among any seaport stakeholders is vital to improving their operations. Currently, Electronic Data Interchange (EDI) and Port Community Systems (PCS), as primary enablers of digital seaports, have demonstrated their limitations to interchange information on time, accurately, efficiently, and securely, causing high operation costs, low resource management, and low performance. For these reasons, this contribution presents the Seaport Data Space (SDS) based on the Industrial Data Space (IDS) reference architecture model to enable a secure data sharing space and promote an intelligent transport multimodal terminal. Each seaport stakeholders implements the IDS connector to take part in the SDS and share their data. On top of SDS, a Big Data architecture is integrated to manage the massive data shared in the SDS and extract useful information to improve the decision-making. The architecture has been evaluated by enabling a port authority and a container terminal to share its data with a shipping company. As a result, several Key Performance Indicators (KPIs) have been developed by using the Big Data architecture functionalities. The KPIs have been shown in a dashboard to allow easy interpretability of results for planning vessel operations. The SDS environment may improve the communication between stakeholders by reducing the transaction costs, enhancing the quality of information, and exhibiting effectiveness.

The evolution of seaports to smart ports is the most important revolution they must face in order to meet the high requirements of efficiency, economy, and security. Achieving this evolution is a task full of challenges. One of these challenges is the interoperability of the stakeholders' information systems. Interoperability has been widely studied but there is still some reluctance to adopt the solutions because the stakeholders are afraid of sharing their data. Recently, the Industrial Data Space (IDS) reference architecture proposes several recommendations and specifications to solve this issue based on data sovereignty concept. This architecture has not been studied nor implemented for the seaport use case yet. For this reason, this paper presents a seaport data space based on IDS architecture to share information in a secure and interoperable manner. Along with this, a big data architecture is added to exploit the shared data. The seaport data space is implemented using the FIWARE IoT platform. The seaport data space is tested by sharing the data of the port authority to the port terminal and analyzing the large shared data through descriptive analysis. The results show that by using the seaport data space, the shared data allow improving the decision making in the planning operations of the seaport.

Applications and Services are located at the top of the IoT deployments and represent a big portion of the IoT stack. Each Platform has several IoT Services and these use to be domain-oriented and very heterogeneous. This heterogeneity hinders the interoperability between services and applications from different IoT Platforms. Flow Based Programming is a paradigm that allows the interconnection of services and the creation of an execution flow between them. There are several solutions that use this paradigm within the IoT platforms, but none is mainly focused in the aim of connect services from different IoT platforms. To address this, a methodology is described to develop functionalities to access to the services, and an architecture is provided with different components to offer a solution to this interoperability problem. This solution offers advantages in the registry, cataloging and discovery of services, and in the creation and management of composite IoT Services. Finally, this paper show a use case related with transport and logistic to validate this approach.