Um modelo de rede de sensores sem fio auto-organizada e tolerante a falhas para detecção de incêndios

• Autor(a) principal: Giuntini, Felipe Taliar
• Data de Publicação: 2016
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFSCAR
• Texto Completo: https://repositorio.ufscar.br/handle/ufscar/8296
• topic: Redes de sensores sem fio; Auto-organização; Tolerância a falhas; Detecção de fogo; Wireless sensor networks; Sel-organization; Fault tolerance; Fire detection; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

The wildfires caused by human occupation is one of the factors that most contributes to deforestation of conservation areas, resulting in a number of issues for ecological systems. Premature fire detection lead to the elimination or minimize the damage that will be caused by a fire incident. Wireless Sensor Networks (WSNs) has been shown to be a good alternative for environmental monitoring applications, as they can collect and send the information in real-time, such as humidity, wind and temperature of various parts of the forest. Due to problems such as power limitation, communication failure and loss of nodes, the network topology is constantly changing, requiring mechanisms to achieve self-organization and fault tolerance. This paper proposes the development of a model and application in selforganizing and fault-tolerant WSNs for fire detection in conservation areas. To achieve self-organization and fault tolerance is encouraged local interactions between neighboring nodes that monitor the same region and the coordination of tasks, through a supervisor node, equipped with a framework for developing fault-tolerant applications based on components. Using a component redundancy technique with adaptive approach, the fire detection solution was implemented. Each component, ie, different implementations of the same specification, it is loaded and unloaded from runtime memory while the node assumes the role of coordinator. The results are stored and after execution of all components is achieved a consensus. For analysis and validation of the model and the application was simulated to 60 events in the sensor network in a real scenario, using the Sinalgo simulator. The results were classified as True (partial or absolute) or False (partial or absolute). In 45% of consensus identified a possible fault in the application and in only 35% there was absolute consensus.