Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Creating an Ambient-Intelligence Environment Using Embedded Agents
IEEE Intelligent Systems
Intelligent light control using sensor networks
Proceedings of the 3rd international conference on Embedded networked sensor systems
A survey of middleware for sensor networks: state-of-the-art and future directions
Proceedings of the international workshop on Middleware for sensor networks
How smart are our environments? An updated look at the state of the art
Pervasive and Mobile Computing
Enabling applicability of energy saving applications on the appliances of the home environment
IEEE Network: The Magazine of Global Internetworking
Occupancy-driven energy management for smart building automation
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
TinyEARS: spying on house appliances with audio sensor nodes
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Granger causality analysis on IP traffic and circuit-level energy monitoring
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
SmartTecO: context-based ambient sensing and monitoring for optimizing energy consumption
Proceedings of the 8th ACM international conference on Autonomic computing
Evolution for the sustainability of internetware
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
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The high energy required by home appliances (like white goods, audio/video devices and communication equipments) and air conditioning systems (heating and cooling), makes our homes one of the most critical areas for the impact of energy consumption on natural environment. In this paper we present a work in progress within the European project AIM for the design of a system that can minimize energy waste in home environments efficiently managing devices operation modes. In our architecture we use a wireless sensor network to monitor physical parameters (like light and temperature) as well as the presence of users at home and in each of its rooms. With gathered data our system creates profiles of the behavior of house inhabitants and through a prediction algorithm is able to automatically set system parameters in order to optimize energy consumption and cost while guaranteeing the required comfort level. When users change their habits due to unpredictable events, the system is able to detect wrong predictions analyzing in real time information from sensors and to modify system behavior accordingly. By the automatic control of energy management system it is possible to avoid complex manual settings of system parameters that would prevent the introduction of home automation systems for energy saving into the mass market.