With more and more wireless sensor networks being deployed, how to design and manage these large-scale sensor networks, especially for energy efficiency, becomes a very important issue. The current sensor network management tools only offer limited management data such as energy level and location of each sensor. However, these data are isolated over time, not enough to reflect the dynamic nature of the sensor networks. In this paper, we propose a new framework for the network management of a large-scale sensor network, called Energy Map. Based on nonlinear manifold learning algorithms, we will be able to not only visualize the energy level and location of each sensor in a network but also to find the dynamic patterns from a large volume of sensor network data such as which set of sensors has been significantly consuming its energy or how far the cluster members are from the current cluster head. All these information is usually very important to develop a good sensor network protocol stack such as clustering algorithms and routing protocols. Our contribution made in this paper is to introduce nonlinear manifold learning methods to derive the energy distribution of a wireless sensor network. We also show several interesting results and discuss their significance to the energy-efficient designs of wireless sensor networks.
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