The widespread embrace of the Internet of Things (IoT) and complementary technologies such as Wireless Sensor Networks (WSNs) has significantly driven down the costs associated with IoT devices. Despite this, seamlessly integrating these advancements into the management of sensors in practical settings remains a formidable task. This research endeavors to confront these implementation challenges by introducing an architectural model for an automated monitoring system dedicated to embedded devices, complemented by creating a user-friendly interface. To validate the efficacy of the proposed model, a water quality monitoring system is implemented, utilizing readily available commercial off-the-shelf components.

The literature on the development of autonomous sailboats lacks reporting the challenges experimented in experiments in real-world settings such as the risk of failure, the complexity of missions, and the absence of established operational procedures. Simulators also lack real-world data, which limits their fidelity. Towards alleviating these problems, this work adopts an exploratory and applied approach based on a longitudinal case study. The operational challenges faced during five years of development and real missions with a robotic sailboat are introduced, proposing operational procedures to reduce risks, increase the agility of field missions, and enable the collection of navigation data. The main contribution is a set of innovative operational practices in missions with robotic sailboats that enables the transition of algorithms from simulation to practical application. An analysis was conducted using data from these practices on real missions, complemented by an evaluation with questionnaires. These new operational procedures increased the safety and effectiveness of field missions.

Current Unmanned Surface Vessel (USV) systems follow the USV-1.0 paradigm (traditional modular perception-planning-control stacks) or more recently the USV-2.0 approach (end-to-end neural policies requiring large training datasets). In contrast to being effective in constrained settings, both paradigms fail in leveraging accumulated human nautical expertise and struggle with data efficiency and interpretability. In this paper we propose a cognitive maritime navigation paradigm that integrates Vision-Language Models (VLMs), Human-in-the-Loop (HITL) learning, and Spatio-Temporal Navigation Memory (STNM) to enable autonomous surface vessels to learn and reuse visuomotor behavior from human demonstrations, which we nominate as USV-3.0. This novel approach integrates three components into a unified cognitive framework: (1) language-conditioned maritime vision-language models (VLMs) that interpret 360 degrees visual scenes and ground perception in the intent conveyed by natural-language mission commands; (2) a confidence-based HITL mechanism that triggers operator intervention when system confidence drops below 90%, storing validated human decisions together with their linguistic and visual context; and (3) an STNM system that records trajectories, visual embeddings, and textual reasoning, enabling hybrid GPS-and CLIP-based retrieval of past behaviors conditioned on mission semantics. We validate USV-3.0 in VisualSimBoat using four real-world navigation scenarios that represent realistic conditions. Considering trained configurations (normal visibility), the system achieves 100% mission success, 98.2% autonomous functioning, and memory retrieval precision within 8m, learning new routes from 23 to 61 HITL episodes-orders of magnitude fewer than typically required by USV-2.0 approaches. Zero-shot transfer experiments under adverse visibility conditions reveal limitations in decision execution, motivating further investigation into safety mechanisms for unseen environments. These results position language-conditioned cognitive navigation as a data-efficient alternative to existing USV paradigms, while clearly delineating its current boundaries and future research directions.

The responsible management of water resources is essential for promoting sustainable development and supporting economic growth. To ensure long-term water security, society must adopt more conscious consumption practices aimed at preserving natural reserves. In this context, we present the development of an affordable computational system based on Internet of Things (IoT) technologies and Commercial Off-The-Shelf (COTS) components for the detection of water leakages in residential settings. The proposed system integrates modules for data acquisition, transmission, and processing, providing users with timely information on potential leakages. Our methodology includes empirical evaluation of the data transmission process and a comparative analysis of leakage detection techniques. A functional prototype was successfully implemented and tested in a real-world environment, representing a tangible step toward a more sustainable and water-efficient future.

Overall, a big challenge in building a sailboat USV relies on the development of an autonomous system for guidance, navigation, and control (GNC) because both sail and rudder angle must be cooperatively adjusted to correct the navigation direction — traditional propelled boats can be more easily controlled with a straightforward control task to set the rudder angle. Moreover, sailing upwind requires special maneuvers to reach a given target in that unfeasible direction. Reinforcement learning emerges as a promising technique for building autonomous GNCs for sailing robots, but training the neural network with a real sailboat is impractical due to long periods of training and safety reasons. Even traditional control-based approaches are mainly tested in simulated environments due to the difficulties in building and operating a real sailboat. The issue that arises is the fidelity of these simulated environments. In this context, we propose Yara, an oceanic virtual environment with a reliable physics simulation for developing, training, and evaluating autonomous agents to operate digital twins of sailing robots in reinforcement learning and other paradigms. An autonomous sailing robot digital twin is available within the virtual environment, with the foil dynamics constructed based on a real sailing robot. We coupled these foil dynamics in Gazebo’s physics engine to compute the lift and drag forces acting on the sail, rudder, and keel. The simulated world feeds sensors such as cameras, wind sensors, and GPS. The Robot Operating System communicates these sensors’ data through topics, facilitating users’ implementation and testing of new GNC solutions. Yara provides a reliable solution for foil dynamic simulated physics that achieves a simulation speedup of 300 times on an i7 laptop with 8 GB of RAM, powered by a Nvidia RTX 3060 and running Ubuntu 20.04. With this speedup, it is possible to complete a million time steps of deep reinforcement learning training in approximately eight hours. Evaluation scenarios were presented to highlight specific features of the simulator, like the maneuverability of the sailing robot digital twin and applications to train, evaluate, and compare reinforcement learning agents and other control solutions.

This review explores the recent advancements in AI-driven autonomous sailboat navigation, underscoring its pivotal role in ocean monitoring and real-time maritime data collection. Drawing on an extensive range of primary and secondary sources, the study critically evaluates current challenges, innovative control algorithms, and path planning strategies, with a particular emphasis on AI techniques. A major contribution of this study is the comparative analysis of these AI methods to assess their efficacy in achieving robust autonomy amid dynamic and uncertain maritime environments. The review also addresses notable gaps in the literature, highlighting the limited adoption of AI-specific methodologies in sailboat control systems. It explores hybrid and adaptive approaches that integrates advanced sensing and obstacle avoidance technologies to improve real-time decision-making and navigation accuracy. Furthermore, the paper traces the evolution of path planning from traditional graph-based methods to state-of-the-art learning algorithms, identifying future research directions focused on enhancing robustness, adaptability, and the practical deployment of autonomous sailboats beyond simulations. Ultimately, this review serves a foundational resource for researchers and practitioners aiming to advance sustainable, efficient, and reliable autonomous sailboat technologies for marine exploration and environmental Management.

Coastal environments, such as beaches, canals, and estuaries, are ecologically vital, supporting diverse species and regulating the interface between continental and oceanic systems. Despite their importance, these ecosystems face growing threats from anthropogenic activities that compromise their environmental balance. In this context, data-driven approaches such as machine learning offer promising tools for monitoring and managing water quality. However, many existing studies focus on limited indicators and often overlook temporal and spatial dynamics. This study addresses these gaps by applying machine-learning models to assess water quality in the Jacarepaguá Lagoon System, a complex of four interconnected lagoons in Rio de Janeiro, Brazil. While the analysis centered on biochemical oxygen demand (BOD), future work will incorporate additional indicators such as turbidity and dissolved oxygen, and adopt temporal modeling strategies. The models developed achieved a mean squared error of 17(mg/L) ^2 , constrained by the sparsity of monitoring data. These findings underscore both the potential and limitations of current predictive approaches and highlight the need for more comprehensive and continuous data collection to support evidence-based environmental management.

The demand for autonomous Unmanned Surface Vehicles (USVs) is growing across maritime industries, yet there remains a lack of accessible platforms for generating high-quality synthetic datasets needed for AI model development. Real-world data collection is costly, time-consuming, and often impractical in dynamic maritime environments. This work introduces VisualSimBoat, an open-source, high-fidelity simulator built on Unreal Engine that addresses this gap. The simulator offers physics-based water interaction, multi-sensor integration, and tools for creating extensive datasets with synchronized camera imagery, GPS data, and control commands. Experimental results validate its effectiveness in generating data frames for diverse maritime conditions and facilitating streamlined dataset creation. The simulator has proven instrumental in debugging waypoints and leveraging datasets from camera and GPS sensors to train AI models for autonomous navigation.

This work focuses on the main challenges and problems in developing a virtual oceanic environment reproducing real experiments using Unmanned Surface Vehicles (USV) digital twins. We introduce the key features for building virtual worlds, considering using Reinforcement Learning (RL) agents for autonomous navigation and control. With this in mind, the main problems concern the definition of the simulation equations (physics and mathematics), their effective implementation, and how to include strategies for simulated control and perception (sensors) to be used with RL. We present the modeling, implementation steps, and challenges required to create a functional digital twin based on a real robotic sailing vessel. The application is immediate for developing navigation algorithms based on RL to be applied on real boats.

Unmanned vehicles keep growing attention as they facilitate innovative commercial and civil applications within the Internet of Things (IoT) realm. In this context, autonomous sailing boats are becoming important marine platforms for performing different tasks, such as surveillance, water, and environmental monitoring. Most of these tasks heavily depend on artificial intelligence (AI) technologies, such as visual navigation and path planning, and comprise the so-called AI of Things (AIoT). In this article, we propose 1) the OpenBoat, an automating system architecture for AIoT-enabled sailboats with application-agnostic autonomous environment monitoring capability and 2) the F-Boat, a fully functional prototype of OpenBoat built with commercial off-the-shelf (COTS) components on a real sailboat. F-Boat includes low-level control strategies for autonomous path following, communication infrastructure for remote operation and cooperation with other systems, edge computing with AI accelerator, modular support for application-specific monitoring systems, and navigation aspects. F-Boat is also designed and built for robustness situations to guarantee its operation under extreme events, such as high temperatures and bad weather, through extended periods of time. We show the results of field experiments running in Guanabara Bay, an important aquatic ecosystem in Brazil, that demonstrate the functionalities of the prototype and demonstrate the AIoT capability of the proposed architecture.

Unmanned surface vessels of sailboat type are becoming important platforms for tasks such as surveillance and environmental monitoring. However, making them fully autonomous remains a challenge due to the complexity of their operating environments. In this paper, we propose a unified control architecture for autonomous sailboats that integrates low-level control strategies, such as PID for rudder control and fuzzy logic for sail adjustment. These traditional control techniques are adapted to the sailboat’s dynamics and have been experimentally validated in real-world scenarios. Future work will focus on developing a hierarchical reinforcement control (HRC) system, integrating Artificial Potential Fields (APF) and A* for high-level path planning and obstacle avoidance and Proximal Policy Optimization (PPO) for low-level control.

Os Veículos Aéreos Não Tripulados (ou UAV, do inglês Unmanned Aerial Vehicles) tiveram um crescimento significativo devido a seus preços acessíveis, disponibilidade e facilidade de operação. Esses benefícios de operabilidade levaram a sua introdução em muitas aplicações civis, incluindo vigilância, monitoramento remoto, operações de socorro, além do uso recreativo. Para aplicações críticas, uma plataforma realista de simulação de missão de UAV, incluindo modelos precisos para prever o alcance e a qualidade da comunicação entre o UAV e a estação terrestre, é de grande valia para garantir a eficácia e a segurança do voo. Nossos estudos mostraram que as plataformas de simulação são, em geral, simplificadas quanto aos modelos de comunicação. Neste artigo, propomos uma arquitetura de simulação baseada no SITL ArduPilot que introduz modelos de comunicação mais precisos.

Os últimos anos têm sido marcados pela inserção de tecnologias renováveis na matriz energética mundial, como a energia eólica e solar, que são energias limpas e de baixo impacto ambiental. As turbinas eólicas, responsáveis pelo processo de conversão energética, se constituem por equipamentos complexos de alto custo e suscetíveis a inúmeras falhas. O monitoramento dos componentes das turbinas pode auxiliar na detecção de falhas antes que elas ocorram, reduzindo os custos de manutenção do equipamento. Este trabalho compara duas técnicas para determinação de hiperparâmetros de modelos centrados em dados na detecção de falhas em turbinas eólicas. Resultados mostram a importância da seleção e otimização de dados para o problema.

We introduce a study about the possibility of using currently developed sailboat robots as tools for motivating learning skills by undergraduate students, including programming abilities, among other disciplines. By way of a survey done with several (former) undergraduate students that worked on autonomous sailboat projects in two different institutions, we demonstrate that it can be an interesting learning resource. These students used it both as a final course project or even only as project participants. Ultimately, we figured out that the platforms can be used as supporting artifacts for Problem-Based Learning in several disciplines. Their use could be restricted due to the space necessary. However, smaller versions have been pointed out as a solution. Another positive point is the motivation that it gives to the students.

Drones find several usages in the modern world, including smart agriculture, providing Internet connectivity, delivering goods, and entertainment. They also find applicability in smart cities, enabling or improving activities such as surveillance, city planning, disaster management, and traffic monitoring. The usefulness of drones stems from their ability to access difficult regions, but also from the possibility of customization by means of different sensors and actuators: depending on what kind of accessories they carry, drones can perform a number of different tasks, broadening their applicability. However, this potential can be hindered by a number of hurdles, including technical, regulatory or even social issues. This paper describes the work of the DroNit project, which aims to improve the applicability and efficacy of drones in the context of the activities of the Civil Defense Office of the city of Niterói, Brazil. We describe how the office currently uses drones and discuss the main issues they faced throughout the years. We also discuss other possible applications of drones that could help the work done by the Office and study what are the currently standing obstacles. Finally, we discuss technical solutions that are currently under development by the DroNit project and report on preliminary results.

Drones have become usual among the civilian population and are enabling cities to be more interconnected and intelligent. However, few studies about the public's opinion on drones have been conducted and those are usually particular for a certain country or region. Knowledge of public opinion is crucial for guiding government policies, particularly on new technologies. This work aims to conduct a systematic review to identify the civilian public attitude toward drones and to assess the main factors that influence opinion. A total of 30 studies were included in the review, analyzing public opinion regarding drones in 15 countries. The review shows that public opinion is influenced by socio-cultural aspects, the main perceived risks and the main expected benefits. Sociocultural factors were related to demographic aspects, such as geographic location, technological expertise, and familiarity with drone-related terminology. The main perceived risks included drone misuse, privacy disrespect, malfunction, damage, safety, noise and legal liability. The main expected benefits, such as application flexibility, emergency response and monitoring, cost reduction and safety, are also described. This review may orient future qualitative and quantitative research on public acceptance of drones and guide researchers and decision makers on drone use by describing the influencing factors on the population's perceived benefits and risks.

Diversos segmentos da sociedade ainda carecem de integração de pessoas com deficiência visual, como o mercado de entretenimento, onde os jogos são predominantes. Uma das formas de entretenimento favoritas para os jogadores são os jogos de cartas, que não são compatíveis com as necessidades deste público. As adaptações existentes são limitadas, devido ao alto custo, baixa disponibilidade e ao fato de que ainda não é possível aplicá-las em todos os jogos. Entre os jogos de cartas, Magic: The Gathering é o mais jogado do mundo. Este artigo apresenta os resultados de uma pesquisa qualitativa e um processo de co-design com um usuário cego, onde buscamos uma solução para tornar o jogo acessível e inclusivo.

Deploying monitoring systems with wireless sensor networks (WSNs) is a very challenging task: physical components are highly heterogeneous, suffer damage, replaced; data is generated massively and must be managed, stored, and made available to other systems. In this chapter, we propose OSIRIS, a framework for building monitoring systems based on WSNs. This framework provides resources for monitoring the WSN, collecting, processing, and storing data, and an interface for providing data to other applications and/or systems. OSIRIS uses a set of abstractions to offer flexibility for the creation of various monitoring systems and to decouple network physical sensors from data consuming applications. In our tests, we use an implementation of OSIRIS to show that our architectural decisions allow OSIRIS to handle commercial and industrial-sized networks.

Our society has become ever more dependent on large datacenters. Search engines, e-commerce and cloud computing are just some of the broadly used services that rely on large scale datacenters. Datacenter managers are reluctant to non-functional changes on the facilities of a perfectly operational installation as failures can be very expensive. Therefore, one of the big challenges of green computing is how to reduce the energy consumption and environmental impact of such systems without compromising the business. In this work, we propose a thermal monitoring tool for datacenters which is based on a WSN composed of ready-to-use modules. This tool provides a better understanding of the thermal behavior of datacenters and can help datacenter managers, for example, to manually adjust the cooling system in order to avoid energy waste and reduce cost. There is very low intrusiveness to the server facilities, as the tool is 100% independent of the server operability and requires only the setup of small wireless and battery powered sensors. Our tool was implemented and tested on a real datacenter in order to demonstrate the feasibility of our approach.