The continuous evolution of the agricultural sector justifies the incorporation and adaptation of the latest technologies. Nowadays, managing crops is possible through Internet-based technologies. Their application allows for the exploitation of information and the development of isolated applications, which, although powerful, create challenges for obtaining scalable predictions throughout the useful life of farms. To address this problem, a data model was defined to improve the management of crop plots in irrigation communities and simultaneously monitor crop needs. Consequently, the objective of this study was to create an open and interoperable platform based on standard interfaces and protocols to enable the integration of heterogeneous sources of information, while ensuring interoperability with other third-party solutions for exchanging and exploiting such information. Standard and open interfaces and protocols form the basis of the platform, thereby unifying all information in a single data model, which facilitates the better use and dissemination of information. The system was fully instantiated in a real prototype in an irrigation community; the software improved water irrigation management for the farmers connected to the platform.

Although current estimates depict steady growth in Internet of Things (IoT), many works portray an as yet immature technology in terms of security. Attacks using low performance devices, the application of new technologies and data analysis to infer private data, lack of development in some aspects of security offer a wide field for improvement. The advent of Semantic Technologies for IoT offers a new set of possibilities and challenges, like data markets, aggregators, processors and search engines, which rise the need for security. New regulations, such as GDPR, also call for novel approaches on data-security, covering personal data. In this work, we present DS4IoT, a data-security ontology for IoT, which covers the representation of data-security concepts with the novel approach of doing so from the perspective of data and introducing some new concepts such as regulations, certifications and provenance, to classical concepts such as access control methods and authentication mechanisms. In the process we followed ontological methodologies, as well as semantic web best practices, resulting in an ontology to serve as a common vocabulary for data annotation that not only distinguishes itself from previous works by its bottom-up approach, but covers new, current and interesting concepts of data-security, favouring implicit over explicit knowledge representation. Finally, this work is validated by proof of concept, by mapping the DS4IoT ontology to the NGSI-LD data model, in the frame of the IoTCrawler EU project.

Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

The irrigation sector has undergone a remarkable transformation in recent decades due to the application of pressurized water distribution technologies, improving the management of limited water resources. As a result of this transformation, irrigation has become, together with agricultural machinery, the primary consumer of energy within the agri-food sector. Furthermore, the energy cost of operating pumping equipment during a farming season represents 30–40% of the crop’s total cost. For this reason, one of the most interesting challenges in this scope is that of improving energy efficiency and reducing economic costs so that productive work become more and more competitive. Energy audit makes possible to determine the efficiency of installations, and enables to determine energy saving protocols (requirements), for this reason the aim of this article is to carry out these by using IoT-based systems. The proposed system improves decision-making on agricultural pumping management by classifying wells’ efficiency and integrating the data sets that determine their efficiency into a single information model. The system monitors energy efficiency according to different parameters such as: infrastructure, energy consumption, electric rates, manometric height or type of installation, making it possible to enhance each pumping operation’s decisions. This solution has been deployed in an irrigation community in southeast Spain whose results have warned about the lack of efficiency in two of its wells: in one of them it is proposed that they be replaced, due to the high cost of pumping water, and in the other, hydraulic mechanisms are implemented to improve the water-energy binomial.