As our society becomes more and more digital, the corresponding demand for electric energy is increasing. Despite the power efficient design of devices, this rising trend of energy consumption does not weaken because of more and more devices used in our daily life. Collaboration strategies between devices can reduce their overall electrical consumption. Consolidation -i.e., migrating tasks among devices to place into low power state or to switch off a maximum of unused devices -is a mean of optimizing the consumption of a group of devices. So far, consolidation is mainly used in datacenters. Here, we propose a model to extend this approach to Digital Home. This model takes into account properties, such as the unforeseeable appearance of devices or restrictions due to task nature. Its implementation in a Digital Home environment saves around 25% of the energy consumption in a scenario based on the daily life of a family of four persons.

The number of connected devices in the home is growing dramatically, increasing the part of the Digital Home in the electric power demand. Reducing the overall energy consumption of the Digital Home becomes a concern in everyday life. Moving applications to the smaller set of devices enables to increase the number of devices that can be put into low power state, and thus reduce energy consumption. However, the application deployment constraints and the Digital Home heterogeneity limit the choice in deployment solutions onto available devices. We propose to consider distributed component-based applications to overcome this limitation. The distribution of applications constraints over its components improves their mobility, i.e., increasing the number of devices on which a component can be deployed. This approach is optimized to reduce the set of processed solutions. Moreover, the proposed architecture reacts continuously to relevant modifications in the Digital Home software architecture (connection and disconnection of devices, start and stop of applications) to always meet energy efficiency. The architecture is also designed to limit its own energy consumption impact. The feasibility of the approach is assessed with Digital Home applications and migration policies between devices.