Opportunistic networks are becoming a solution to provide communication support in areas with overloaded cellular networks, and in scenarios where a fixed infrastructure is not available, as in remote and developing regions. A critical issue, which still requires a satisfactory solution, is the design of an efficient data delivery solution trading off delivery efficiency, delay, and cost. To tackle this problem, most researchers have used either the network state or node mobility as a forwarding criterion. Solutions based on social behaviour have recently been considered as a promising alternative. Following the philosophy from this new category of protocols, in this work, we present our “FriendShip and Acquaintanceship Forwarding” (FSF) protocol, a routing protocol that makes its routing decisions considering the social ties between the nodes and both the selfishness and the device resources levels of the candidate node for message relaying. When a contact opportunity arises, FSF first classifies the social ties between the message destination and the candidate to relay. Then, by using logistic functions, FSF assesses the relay node selfishness to consider those cases in which the relay node is socially selfish. To consider those cases in which the relay node does not accept receipt of the message because its device has resource constraints at that moment, FSF looks at the resource levels of the relay node. By using the ONE simulator to carry out trace-driven simulation experiments, we find that, when accounting for selfishness on routing decisions, our FSF algorithm outperforms previously proposed schemes, by increasing the delivery ratio up to 20%, with the additional advantage of introducing a lower number of forwarding events. We also find that the chosen buffer management algorithm can become a critical element to improve network performance in scenarios with selfish nodes.

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.

Contacts are essential to guarantee the performance of opportunistic networks, but due to resource constraints, some nodes may not cooperate. In reputation systems, the perception of an agent depends on past observations to classify its actual behavior. Few studies have investigated the effectiveness of robust learning models for classifying selfish nodes in opportunistic networks. In this paper, we propose a distributed reputation algorithm based on the game theory to achieve reliable information dissemination in opportunistic networks. A contact is modeled as a game, and the nodes can cooperate or not. By using statistical inference methods, we derive the reputation of a node based on learning from past observations. We applied the proposed algorithm to a set of traces to obtain a distributed forecasting base for future action when selfish nodes are involved in the communication. We evaluate the conditions in which the accuracy of data collection becomes reliable.

Este estudo explora a produção de bebidas artesanais a partir de frutas amazônicas como açaí (Euterpe oleracea), cupuaçu (Theobroma grandiflorum) e cumaru (Dipteryx odorata), com foco em processos sustentáveis e inovação tecnológica. O açaí exige sistemas de extração eficientes para preservar suas propriedades nutricionais, enquanto o cupuaçu demanda equipamentos especializados para otimizar a fermentação e prensagem, realçando suas qualidades sensoriais. Já o cumaru apresenta desafios no processamento de suas sementes aromáticas, necessitando de tecnologias específicas para infusões e destilados. A metodologia adotada incluiu revisão bibliométrica e análise do estado da arte sobre os frutos selecionados, bem como os processos de automação e energias renováveis, visando otimizar os processos produtivos. Os resultados indicam que a automação e o uso de tecnologias emergentes, como sensores e Inteligência Artificial (IA), são essenciais para melhorar a produção, aumentar a qualidade e reduzir os impactos ambientais. Além disso, o aproveitamento de resíduos para geração de bioenergia fortalece a circularidade produtiva, contribuindo para a bioeconomia regional. A pesquisa reforça a importância da sustentabilidade na transformação da indústria de bebidas na Amazônia.