Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform.

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

The last decade has witnessed a significant reduction in prices and an increased performance of electronic components, coupled with the influence of the shift towards the generation of open resources, both in terms of knowledge (open access), programs (open-source software), and components (open hardware). This situation has produced different effects in today’s society, among which is the empowerment of citizens, called makers, who are themselves able to generate citizen science or build assembly developments. Situated in the context described above, the current study follows a Do-It-Yourself (DIY) approach. In this way, it attempts to define a conceptual design of an Internet of Things (IoT) node, which is reproducible at both physical and behavioral levels, to build IoT nodes which can cover any scenario. To test this conceptual design, this study proposes a sensorization node to monitor meteorological phenomena. The node is called SEnviro (node) and features different improvements such as: the possibility of remote updates using Over-the-Air (OTA) updates; autonomy, using 3G connectivity, a solar panel, and applied energy strategies to prolong its life; and replicability, because it is made up of open hardware and other elements such as 3D-printed pieces. The node is validated in the field of smart agriculture, with the aim of monitoring different meteorological phenomena, which will be used as input to disease detection models to detect possible diseases within vineyards.

In recent years, interest and investment in health and mental health smartphone apps have grown significantly. However, this growth has not been followed by an increase in quality and the incorporation of more advanced features in such applications. This can be explained by an expanding fragmentation of existing mobile platforms along with more restrictive privacy and battery consumption policies, with a consequent higher complexity of developing such smartphone applications. To help overcome these barriers, there is a need for robust, well-designed software development frameworks which are designed to be reliable, power-efficient and ethical with respect to data collection practices, and which support the sense-analyse-act paradigm typically employed in reactive mHealth applications. In this article, we present the AwarNS Framework, a context-aware modular software development framework for Android smartphones, which facilitates transparent, reliable, passive and active data sampling running in the background (sense), on-device and server-side data analysis (analyse), and context-aware just-in-time offline and online intervention capabilities (act). It is based on the principles of versatility, reliability, privacy, reusability, and testability. It offers built-in modules for capturing smartphone and associated wearable sensor data (e.g. IMU sensors, geolocation, Wi-Fi and Bluetooth scans, physical activity, battery level, heart rate), analysis modules for data transformation, selection and filtering, performing geofencing analysis and machine learning regression and classification, and act modules for persistence and various notification deliveries. We describe the framework's design principles and architecture design, explain its capabilities and implementation, and demonstrate its use at the hand of real-life case studies implementing various mobile interventions for different mental disorders used in clinical practice.

Weather conditions are one of the main threats that can lead to diseases in crops. Unfavourable conditions, such as rain or high humidity, can produce a risk of fungal diseases. Meteorological monitoring is vital to have some indication of a possible infection. The literature contains a wide variety of models for warning for this type of disease.These are capable of warning when an infection may be present. Devices (weather stations) able to measure weather conditions in real-time are needed to know precisely when an infection occurs in a smallholding. Besides, such models cannot be executed at the same time in which the observations are collected; in fact, these models are usually executed in batches at a rate of one per day. Therefore, these models need to be adapted to run at the same frequency as that at which observations are collected so that a possible disease can be dealt with as early as possible. The primary aim of this work is to adapt disease warning models to run in (near) real-time over meteorological variables generated by Internet of Things (IoT) devices, in order to inform farmers as quickly as possible if their crop is in danger of being infected by diseases, and to enable them to tackle the infection with the appropriate treatments. The work is centered on vineyards and has been tested in four different smallholdings in the province of Castellón (Spain).