In recent years, one of the main global challenges is associated with energy efficiency, being energy waste one of the factors to be highlighted. Such waste can be overcome by using the Home Automation System (HAS). However, HASs are heavily dependent on its internal network, since this is the basis of a smart home. In this scenario, the Wireless Sensor and Actor Networks (WSAN) is a promising solution, ubiquitous and of easy deployment to be used as infrastructure in a home automation system. However, the use of the WSAN as infrastructure to monitor and act in the decision-making within the context of a home automation system provides challenges. Such challenges refer back not only the lack of a method to act in the decision-making process within the network to reduce energy consumption of the residents, but also in the absence to investigate a trade-off between accuracy of the information and energy consumption of the sensors to prolong the useful life of the infrastructure. Moreover, the lack of a fully distributed infrastructure, flexible and that addresses the needs of the residents are some of the new problems to be explored. In this context, this research project aims to propose a distributed home automation system using wireless sensors and actuators. For this purpose, methods-based solutions on machine learning to act in the decision process within own network, as well as solutions of data aggregation will be proposed. Thus, the system will be aware not just in the lifetime of the network, but also the energy consumption of the residents. Still, through distributed data, will be reused the physical largeness of the environment, such as presence, temperature and humidity, to correlate the applications (for instance, air conditioning and/or lighting) in the decision process. For instance, the air conditioning system can turn off or increase the temperature when the lighting system detects absence of residents in the environment.
News published in Agência FAPESP Newsletter about the scholarship: