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The definition of a virtual router layer allows the application of energy saving techniques based on the migration of virtual routers within the physical infrastructure as traffic conditions change. As traffic decreases, physical resources can be vacated through virtual router migration, thus, saving energy. Such resources can be reclaimed if needed because of traffic increase. Unfortunately, the migration of virtual routers involves reconfiguration costs depending on implementation details and characterized, for instance, by the revenue loss due to quality-of-service (QoS) degradation during the migration process. In this paper, we propose a methodology based on the Markov decision processes for the evaluation of optimal migration policies taking into account both the operation (in terms of energy consumption) and reconfiguration costs.

A new vision-based obstacle avoidance technique for indoor navigation of Micro Aerial Vehicles (MAVs) is presented in this paper. The vehicle trajectory is modified according to the obstacles detected through the Depth Map of the surrounding environment, which is computed online using the Optical Flow provided by a single onboard omnidirectional camera. An existing closed-form solution for the absolute-scale velocity estimation based on visual correspondences and inertial measurements is generalized and here employed for the Depth Map estimation. Moreover, a dynamic region-of-interest for image features extraction and a self-limitation control for the navigation velocity are proposed to improve safety in view of the estimated vehicle velocity. The proposed solutions are validated by means of simulations.