Edjair Mota

Com o avanço da Internet das Coisas (IoT) e de tecnologias de transmissão de baixa potência como LoRa (Long Range), emergiu uma gama de soluções para monitoramento ambiental remoto. Nestes cenários, além da seleção adequada dos parâmetros de transmissão LoRa, destaca-se também o desafio de estender a vida útil da bateria dos nós da rede. A estratégia padrão do LoRa consiste no ajuste dinâmico dos parâmetros de transmissão por meio do algoritmo de Taxa de Dados Adaptativa (ADR). Contudo, o ADR, baseado em SNR (Relação sinal-ruído), apresenta limitações em ambientes ruidosos e florestais. Neste trabalho, apresenta-se o LoRaBB, um algoritmo de seleção automática de parâmetros voltado a cenários florestais ruidosos e à redução do consumo energético, que incorpora três métricas do LoRa (RSSI, SNR e PDR) e se fundamenta na caracterização prévia do ambiente de implantação. Experimentos práticos em fragmentos de floresta demonstraram que o LoRaBB convergiu para configurações com redução do Time on Air (ToA) e menor consumo energético. Por fim, simulações conduzidas com 15 e 40 nós, com alcances de 800 a 2200 metros para o gateway, confirmaram melhoria média de 27,6% no PDR do LoRaBB em relação ao ADR e redução de até 54,2% no consumo energético por pacote, além de diminuição de 78,1% nas colisões de pacotes.

Este trabalho apresenta uma arquitetura IoT de baixo custo e longa distância para monitoramento da qualidade da água na Amazônia, integrando nós sensores com comunicação LoRa, banco em nuvem e um agente de IA baseado em LLM, fundamentado em normas brasileiras de qualidade da água. A arquitetura foi validada em experimento de campo, em lago no INPA - AM, com coleta de pH, temperatura, turbidez e TDS ao longo de três dias, totalizando 9.769 leituras. Os resultados demonstraram a viabilidade operacional do sistema e a efetividade do agente de IA na geração de diagnósticos regulatórios e recomendações pedagógicas alinhadas aos ODS 6 e 15.

Accurate environmental monitoring of aquatic ecosystems is often compromised by field-measurement biases introduced through human presence. Autonomous robotic platforms can mitigate these biases by enabling consistent, long-term data collection under a wide range of environmental conditions. We present the Modular Aquatic Robotic Platform–Freiberg (MARP-FG), an autonomous, catamaran-based system engineered for flexible deployment with customizable sensor payloads to facilitate surface water monitoring. This paper outlines the design criteria, engineering challenges, and solutions implemented to ensure reliable, autonomous operation. The platform supports modular catamaran floaters sized between 1.2 and 2.5 m, selected according to payload mass and mission requirements. To demonstrate versatility, three payload configurations were developed and validated: (i) hydrographic profiling via multi-parameter probes, (ii) sonar-based three-dimensional mapping of complex basin morphologies, and (iii) dynamic closed-chamber measurements of greenhouse gas fluxes equipped with onboard CO₂ quantification using infrared spectrometry and automated gas sampling for subsequent gas chromatographic analysis. Focusing on the greenhouse gas flux measurement configuration, we provide a detailed account of the system's autonomous navigation, operational workflow, and performance metrics. The MARP-FG maintained positioning accuracy within ±2 m under challenging conditions including wave heights up to ±40 cm and wind speeds up to 7 m s⁻¹. Deployments in diverse aquatic environments, such as Amazonian lakes and Central European water bodies, confirmed the platform's robustness and data quality under adverse weather and nocturnal conditions. These innovations substantially improve automated aquatic monitoring capabilities, offering a versatile tool for long-term geoscientific data acquisition.

The Amazon River basin, home to one of the world’s largest freshwater reserves and unparalleled biodiversity, silently suffers from a vast environmental disaster caused by illegal mining, during which mercury is discharged into its waters. This contamination threatens aquatic ecosystems and poses serious risks to highly vulnerable populations. In response, this paper provides clues for a resilient and scalable system architecture for real-time water quality monitoring, tailored to the environmental and infrastructural challenges of the Amazon region. A detailed systematic literature review assesses state-of-the-art Internet of Things (IoT)-based monitoring techniques, focusing on key variables such as mercury concentration, temperature, turbidity, and pH. Special emphasis is given on communication technologies suitable for diverse settings—from Wi-Fi-enabled urban areas to remote rainforest regions where LoRa, NB-IoT, and WiLD (Wi-Fi over Long Distance) present viable alternatives. The study also highlights the role of the application layer in enabling data analysis, real-time alerts, and remote visualization of environmental conditions. This research contributes to developing time-efficient and sustainable monitoring strategies to support public health initiatives and ecological conservation by bridging technological innovation with the urgent environmental needs of one of the planet’s most critical biomes.

The interference of human activities in water bodies has contributed to a deterioration in water quality. With the advancement of the Internet of Things (IoT), aided by transmission technologies such as LoRa (Long Range), low-cost solutions have emerged for long-distance environment monitoring scenarios. One key challenge in such IoT-based systems is selecting LoRa transmission parameters to ensure efficient data exchange among nodes, adapting to varying network conditions. Well-known strategies adapt transmission parameters according to network context through information exchange among nodes and LoRa gateway(s). In this work, we introduce a novel LoRa parameter selection algorithm by incorporating three major LoRa metrics (RSSI, SNR, and PDR) and conducting a comprehensive characterization and validation in the forest environment to build a set of reference values of transmission quality, which are employed in a binary search methodology, utilizing the R-array, representing the transmission quality according to LoRa parameters. The experimental results indicate that the proposed algorithm achieves a 16.20% reduction in Time on Air (ToA). Furthermore, our algorithm optimized the transmission power (TP) selection, achieving at least 38% lower energy consumption than ADR TP parameters. These results highlight that our proposed algorithm can enhance the transmissions in a rainforest environment.

Ecosystems, like almost any environmental entity, are often highly sensitive to the presence of humans when measuring field characteristics. Robotic solutions deserve attention to avoid or greatly reduce related bias. Constant availability of robotic solutions, independent of the time of day and most weather conditions, is an additional advantage. Here, we present an autonomous, Modular Aquatic Robotic Platform (MARP-FG) designed to collect relevant environmental information from surface waters. We define the demands, describe the encountered obstacles and how to overcome them. MARP-FG implements autonomous navigation and data collection capability across various floating-body configurations and sensor setups. Depending on the weight of the measurement system (payload), catamaran floaters with a length ranging from 1.2 meters to 2.5 meters are used. We realized and evaluated three different payloads based on the MARP-FG concept: i) Hydrographic profiling with a multi-parameter probe, ii) Sonar-based 3D mapping of complex basins, and iii) Dynamic closed chamber-based greenhouse gas exchange determination with on-board CO₂ quantification (IR spectrometry) and gas sampling (Exetainers^®) for subsequent gas-chromatographic analysis. This work focuses on option iii) as a practical example to describe our design process and operational modes, thus minimizing faults and errors, especially in harsh environments. Full operation was possible to wave heights of ±40 cm and wind speeds to 7 m sec^-1. Positioning accuracy during measurement cycles was on average better than ±2 m in xy directions. The platform has demonstrated its capabilities in field campaigns on lakes in the Amazon basin (Brazil) and on waterbodies in temperate climate regions of Europe. Largely improved and reproducible positioning on a waterbody, full functionality also under adverse weather conditions and during nighttime significantly enhanced high-quality data acquisition and opens new applications.

Ecosystems, like almost any environmental entity, are often highly sensitive to the presence of humans when measuring field characteristics. Robotic solutions deserve attention to avoid or greatly reduce related bias. Constant availability of robotic solutions, independent of the time of day and most weather conditions, is an additional advantage. Here, we present an autonomous, Modular Aquatic Robotic Platform (MARP-FG) designed to collect relevant environmental information from surface waters. We define the demands, describe the encountered obstacles and how to overcome them. MARP-FG implements autonomous navigation and data collection capability across various floating-body configurations and sensor setups. Depending on the weight of the measurement system (payload), catamaran floaters with a length ranging from 1.2 meters to 2.5 meters are used. We realized and evaluated three different payloads based on the MARP-FG concept: i) Hydrographic profiling with a multi-parameter probe, ii) Sonar-based 3D mapping of complex basins, and iii) Dynamic closed chamber-based greenhouse gas exchange determination with on-board CO₂ quantification (IR spectrometry) and gas sampling (Exetainers^®) for subsequent gas-chromatographic analysis. This work focuses on option iii) as a practical example to describe our design process and operational modes, thus minimizing faults and errors, especially in harsh environments. Full operation was possible to wave heights of ±40 cm and wind speeds to 7 m sec^-1. Positioning accuracy during measurement cycles was on average better than ±2 m in xy directions. The platform has demonstrated its capabilities in field campaigns on lakes in the Amazon basin (Brazil) and on waterbodies in temperate climate regions of Europe. Largely improved and reproducible positioning on a waterbody, full functionality also under adverse weather conditions and during nighttime significantly enhanced high-quality data acquisition and opens new applications.

Ecosystems, like almost any environmental entity, are often highly sensitive to the presence of humans when measuring field characteristics. Robotic solutions deserve attention to avoid or greatly reduce related bias. Constant availability of robotic solutions, independent of the time of day and most weather conditions, is an additional advantage. Here, we present an autonomous, Modular Aquatic Robotic Platform (MARP-FG) designed to collect relevant environmental information from surface waters. We define the demands, describe the encountered obstacles and how to overcome them. MARP-FG implements autonomous navigation and data collection capability across various floating-body configurations and sensor setups. Depending on the weight of the measurement system (payload), catamaran floaters with a length ranging from 1.2 meters to 2.5 meters are used. We realized and evaluated three different payloads based on the MARP-FG concept: i) Hydrographic profiling with a multi-parameter probe, ii) Sonar-based 3D mapping of complex basins, and iii) Dynamic closed chamber-based greenhouse gas exchange determination with on-board CO₂ quantification (IR spectrometry) and gas sampling (Exetainers^®) for subsequent gas-chromatographic analysis. This work focuses on option iii) as a practical example to describe our design process and operational modes, thus minimizing faults and errors, especially in harsh environments. Full operation was possible to wave heights of ±40 cm and wind speeds to 7 m sec^-1. Positioning accuracy during measurement cycles was on average better than ±2 m in xy directions. The platform has demonstrated its capabilities in field campaigns on lakes in the Amazon basin (Brazil) and on waterbodies in temperate climate regions of Europe. Largely improved and reproducible positioning on a waterbody, full functionality also under adverse weather conditions and during nighttime significantly enhanced high-quality data acquisition and opens new applications.

Human activities’ interference in water bodies has contributed to the deterioration of the water quality. With the advancement of the Internet of Things (IoT), aided by transmission technologies such as LoRa (Long Range), low-cost solutions have emerged for long-distance environment monitoring scenarios. One key challenge in such IoT-based systems is selecting LoRa transmission parameters to ensure efficient data exchange among nodes, adapting to varying network conditions. Well-known strategies adapting transmission parameters according to network context through information exchange among nodes and LoRa gateway(s). In this work, we introduce a novel LoRa parameter selection algorithm by incorporating three major LoRa metrics (RSSI, SNR, and PDR) and conducting a comprehensive characterization and validation in the forest environment to build a set of reference values of transmission quality, which are employed in a binary search methodology, utilizing the R-array, representing the transmission quality according of LoRa parameters. Experimental results indicated that the proposed algorithm achieves a 16.20% reduction in Time on Air (ToA). Furthermore, our algorithm optimized the Transmission Power (TP) selection, achieving at least 38% lower energy consumption than ADR TP parameters. These results highlight that our proposed algorithm can enhance the transmissions in a rainforest environment.

Ecosystems, like almost any environmental entity, are often highly sensitive to the presence of humans when measuring field characteristics. Robotic solutions deserve attention to avoid or greatly reduce related bias. Constant availability of robotic solutions, independent of the time of day and most weather conditions, is an additional advantage. Here, we present an autonomous, Modular Aquatic Robotic Platform (MARP-FG) designed to collect relevant environmental information from surface waters. We define the demands, describe the encountered obstacles and how to overcome them. MARP-FG implements autonomous navigation and data collection capability across various floating-body configurations and sensor setups. Depending on the weight of the measurement system (payload), catamaran floaters with a length ranging from 1.2 meters to 2.5 meters are used. We realized and evaluated three different payloads based on the MARP-FG concept: i) Hydrographic profiling with a multi-parameter probe, ii) Sonar-based 3D mapping of complex basins, and iii) Dynamic closed chamber-based greenhouse gas exchange determination with on-board CO₂ quantification (IR spectrometry) and gas sampling (Exetainers^®) for subsequent gas-chromatographic analysis. This work focuses on option iii) as a practical example to describe our design process and operational modes, thus minimizing faults and errors, especially in harsh environments. Full operation was possible to wave heights of ±40 cm and wind speeds to 7 m sec^-1. Positioning accuracy during measurement cycles was on average better than ±2 m in xy directions. The platform has demonstrated its capabilities in field campaigns on lakes in the Amazon basin (Brazil) and on waterbodies in temperate climate regions of Europe. Largely improved and reproducible positioning on a waterbody, full functionality also under adverse weather conditions and during nighttime significantly enhanced high-quality data acquisition and opens new applications.