In Opportunistic Networks (OppNets), mobility of and contact between nodes are explored to create communication opportunities and exchange messages and information. A basic premise for a better performance of these networks is a collaboration of the nodes during communication. However, due to energy restriction factors, nodes may eventually fail to collaborate with message exchanges. In this work, we propose a routing mechanism called eGPDMI to improve message probability of delivery while optimizing nodes’ energy consumption. Unlike other algorithms proposed in OppNets literature, eGPDMI groups nodes by energy level and nodes interests using clustering techniques. Our major assumption is that retaining messages in nodes with the highest energy levels can improve network performance, thus overcoming the problem of nodes’ disconnection due to unwillingness to cooperate due to low energy values. Through questionnaire application and factorial design experiments, we characterize the impacts of energy levels in OppNets. Further, we apply performance evaluation of the eGPDMI mechanism in terms of effectiveness using mobility from real-world scenarios. The results show that our mechanism effectively reduces the degradation of the probability of delivery when the minimum energy level used for nodes to cooperate with communication increases.

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.

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.

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.

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.