Climate change represents a critical threat to global food security, affecting agricultural production and exacerbating the food crisis projected by the FAO for 2050. Soil recovery and the adoption of sustainable agricultural practices, such as organic farming, are essential to address this challenge. Smart organic farming improves soil quality, crop productivity, and water retention capacity. In this context, vermiculture, which utilizes Eisenia Foetida (red worms), plays a fundamental role. This article highlights how humus production through vermiculture has been significantly optimized through an Edge AIoT platform that integrates an agricultural recommendation system based on bio-inspired algorithms, an LSTM network for predicting humus and worm populations, and a control system to regulate variables such as temperature, humidity, and pH. The results show an increase in humus production from 37.58% to 87.88% and in the worm population from 35.5% to 83%. Vermicompost, obtained through the non-thermophilic biodegradation of organic waste by worms, acts as a crucial biofertilizer that sustainably increases crop yields and helps farmers adapt to environmental stresses, contributing to the Sustainable Development Goals (SDGs). Finally, seven experiments were conducted in which the Edge AIoT-based agricultural recommendation platform optimized the vermicomposting process, improving efficiency and productivity in humus production. This technological approach not only mitigates the impact of climate change but also supports the recovery of degraded soils and promotes sustainable agricultural practices essential for ensuring future food security.

Abstract Human interaction has changed considerably with the emergence of the Internet. Today, a large percentage of daily communication takes place on instant messaging applications and social networks. In fact, there has been a considerable increase in the use of social networks because new social networks are being created for specific purposes, such as the search for employment or tourism. When the amount of content on a social network is large, it is necessary to help users find content of their interest. In this regard, artificial intelligence techniques can greatly facilitate the task of searching for relevant content. This paper presents a recommender system for a business and employment oriented social network, on which users are recommended job offers and other user profiles to follow. The presented system is based on virtual organizations of agents, and uses artificial neural networks to determine whether job offers and users should be recommended or not. The system has been evaluated on a real social network; its recommendations regarding job offers and user profiles have had a high acceptance rate.

About a decade after the introduction of Industry 4.0 (I4.0) as a paradigm oriented towards the digitization of industrial environments, centered on the concept of industrial Cyber-physical Systems (CPS) to enable the development of intelligent and distributed industrial systems, many companies around the world are still not immersed in this digital transformation era. This transition is not straightforward and requires the aligned with the novel technologies, architectures and standards to migrate entire traditional systems into I4.0 systems. In this context, this paper presents an approach to perform the digitization of non-I4.0 components/systems into I4.0 through an approach based on the Asset Administration Shell (AAS), which is a standardized digital representation of an asset. This approach enables to hold the asset information throughout its lifecycle, provides a standard communication interface with the asset, and is based on a set of modules that are combined with the AAS to provide novel functionalities for the asset, e.g., monitoring, diagnosis and optimization. Moreover, this approach adopts Multi-agent Systems (MAS) to provide mainly autonomy and collaborative capabilities to the system. The agents are able to get information from the AASs, making intelligent decisions and perform distributed tasks following interaction strategies, e.g., collaboration, negotiation and self-organization. The feasibility of the proposed approach was tested by digitizing a small-scale production system comprising several assets.

The detection and measurement of gas levels have become very important in both domestic and industrial fields. This is a result of the realization that certain gases are toxic and may have a harmful or even lethal effect on humans (and/or the environment), depending on exposure time or concentration. For this reason, gas sensors play a crucial role, especially in industries, where they help to prevent risks by detecting and measuring the presence of harmful gases in the workers’ environment. There is a wide variety of sensors and techniques used to detect gases and determine their characteristics. The case study presented in this paper analyzes the presence of gases in a train hangar. Using a range of low-cost sensors, it is possible to measure the concentration of different gases over time and identify the moments where these concentrations are higher. In this study, it has been determined that these periods are usually related to moments when maintenance works are taking place in the hangar and their relationship with the measurements of a high-accuracy CO2 sensor. Detecting and predicting when these events take place allows to alert employees in case gas levels are considered dangerous for human health, or to take different actions to counteract this threat. An analysis of how to improve the energetic efficiency of the hangar is also carried out, estimating the losses caused by an inefficient ventilation system. Finally, some ideas are given to help improve the well-being of the workers, and also reduce energy costs.

Cyber-Physical Systems (CPS) are devoted to be the main participants in Industry 4.0 (I4.0) solutions. In recent years, many authors have focused their efforts on making proposals for the design and implementation of CPS based on different digital technologies. However, the comparative evaluation of these I4.0 solutions is complex, since there is no uniform criterion when it comes to defining the test scenarios and the metrics to assess them. This paper presents a technology-independent CPS demonstrator for benchmarking I4.0 solutions. To that end, a set of testing scenarios, Key Performance Indicators and services were defined considering the available automation cells setup. The proposed demonstrator has been used to test an I4.0 solution based on a Multi-agent Systems (MAS) approach.

Industry 4.0 (I4.0) is promoting the digitization of traditional manufacturing systems towards flexible, reconfigurable and intelligent factories based on Cyber-Physical Systems (CPS). In this context, the Reference Architecture Model Industrie 4.0 (RAMI4.0) provides guidelines to develop I4.0 compliant solutions based on industrial standards. As the main RAMI4.0 specification, the Asset Administration Shell (AAS) is a standard digital representation of an industrial asset that plays a pivotal role in enabling interoperable communication among I4.0 components across the value chain. This paper provides an analysis of the current state-of-the-art of implementing AAS, discussing, amongst others, the key enabling technologies used to implement the AAS and the alignment of the research works found in the literature with the I4.0 components criteria.

The development and integration of technologies such as the Internet of Things (IoT) or edge computing devices is contributing to the formation of an increasingly digital, intelligent and connected world. As a result, there is a massive flow of data in different sectors of human activity. One example is intelligent buildings, where thousands of components, devices, systems and suppliers interact. In this context, failures in control and monitoring systems are frequent. To analyze this situation, this paper presents as a case study the problem of fault-tolerant robust adaptive monitoring control with state prediction performance for a class of IoT temperature systems subject to uncertainties of precision states and external disturbances. The authors propose a new control strategy based on consensus game theory and prediction of future precision states to reduce tracking error and improve algorithm efficiency. The authors present the development of a new algorithm that improves the functioning of monitoring and control of parcel networks. This has the purpose of increasing the energy efficiency of the same and ensure the effectiveness of our adaptive temperature control algorithm, compared to existing results. With the simulation presented in this research, it is possible to conclude that a new fault tolerant error tracking algorithm ensures robust monitoring of the reference model. It was shown that the predicted temperature signal is limited by a small range close to the collected temperature data. A case study result is provided to demonstrate the effectiveness of the proposed fault-tolerant adaptive monitoring control algorithm.

In smart buildings there are many different types of IoT devices that collect measurements of the environment. These sensors can vary in their characteristics and can also influence the topology of the smart building. For this reason, IoT devices collect heterogeneous measurements. Using complex network and clustering techniques we have designed a new technique that allows to transform heterogeneous data into homogeneous data, this technique is called IoT slicing. This technique consists of creating a graph with the measurements of the IoT network, and virtualizing layers based on the clustering of the graph. To validate the efficiency of this new technique we present the results of a case study using a smart building temperature control algorithm.

Nowadays, circa 10% of 7-11 olds turn out to be poor comprehenders: they demonstrate text comprehension difficulties, related to inference making, despite proficiency in low-level cognitive skills like word reading. To improve the reading comprehension of these children, TERENCE, a technology enhanced learning project, aims at stimulating inference-making about stories. In order to design and develop the TERENCE system, we use a user centred design approach that requires an in depth study of the system's main end-users, namely, its learners and educators. This paper reports on the specification of the user classes for the TERENCE learners by means of user-centred design field studies, the resulting global system architecture, and an example use case of the system, with few related GUI's snapshots.

Organizations of agents based on Virtual Organizations needs to be supported by a coordinated effort that explicitly determines how the agents should be organized and carry out the actions and tasks assigned to them. The interactions of a multi-agent system cannot be only related to the agent and the communication skills, but also to the concepts of organizational engineering. Moreover, nowadays there is a clear trend towards using methods and tools that can help to develop and simulate virtual organizations by means of multiagent systems (MAS). Simulation is used for several purposes ranging from work flow to system's procedures representation. The contribution from agent based computing to the field of computer simulation mediated by ABS (Agent Based Simulation) is a new paradigm for the simulation of complex systems that require a high level of interaction between the entities of the system. The main goal of this work is a new global coordination model for an agent organization in a simulation context. The innovation of this work consists of the dynamic and adaptive planning capability to distribute tasks among the agent of the organization. The middleware used for simulation makes it possible to visualize the emergent agent behaviour and the entity agent.

The significance that Ambient Intelligence (AmI) has acquired in recent years requires the development of innovative solutions. Nonetheless, the development of AmI-based systems requires the creation of increasingly complex and flexible applications. In this regard, the use of context-aware technologies is an essential aspect in these developments to perceive stimuli from the context and react upon it autonomously. This work presents a novel platform that defines a method for integrating dynamic and self-adaptable heterogeneous Wireless Sensor Networks (WSNs). This approach facilitates the inclusion of context-aware capabilities when developing intelligent ubiquitous systems, where functionalities can communicate in a distributed way. Furthermore, the information obtained must be managed by intelligent and self-adaptable technologies to provide an adequate interaction between the users and their environment. Agents and Multi-Agent Systems are one of these technologies. The agents have characteristics such as autonomy, reasoning, reactivity, social abilities and pro-activity which make them appropriate for developing dynamic and distributed systems based on AmI. This way, the integration of the platform with a Service-Oriented Multi-Agent architecture is proposed. Finally, conclusions and future work are presented.