We show that even though mobile networks are highly unpredictable when viewed at the individual node scale, the end-to-end quality-of-service (QoS) metrics can be stationary when the mobile network is viewed in the aggregate. Finally, we show how energy maps can be utilized by an application that aims to minimize a node's total energy consumption over its near-future trajectory.
Energy planning and optimization constitutes one of the most significant challenges for high-mobility networks. A novel framework to share, retain and refine end- to-end energy metrics in the joint memory of the nodes, over time scales over which this information can be spread to the network and utilized for energy planning decisions. We construct maps of end-to-end energy metrics that enable energy optimization in high-mobility networks. We show how to (1) compute the spatial derivatives of energy potentials in high-mobility networks, distribute, share, fuse, and refine energy maps over time by information exchange during encounters, (2) allow the nodes to use energy maps for energy planning and optimization in delay- tolerant, high-mobility networks
We define the coherence time as the maximum duration for which the end-to-end QoS metric remains roughly constant, and the spreading period as the minimum duration required to spread QoS information to all the nodes.
We show that if the coherence time is greater than the spreading period, the end-to-end QoS metric can be tracked. We focus on the energy consumption as the end-to-end QoS metric, and describe a novel method by which an energy map can be constructed and refined in the joint memory of the mobile nodes.
- Networking Module
- Dynamic Random Module
- Connectivity Period Module
|| Visual Studio Dot Net 2005