Training a Wireless Sensor Network

The networks considered in this paper consist of tiny energy-constrained commodity sensors massively deployed, along with one or more sink nodes providing interface to the outside world. Our contribution is to propose a scalable energy-efficient training protocol for nodes that are initially anonymous, asynchronous and unaware of their location. Our training protocol imposes a flexible and intuitive coordinate system onto the deployment area and partitions the anonymous nodes into clusters where data can be gathered from the environment and synthesized under local control. An important by-product of the training protocol is a simple and natural data fusion protocol as well as an energy-efficient protocol for routing data from clusters to the sink node. Being energy-efficient, our training protocol can be run on either a scheduled or ad-hoc basis to provide robustness and dynamic reconfiguration. We also outline a way of making the training protocol secure by using a parameterized variant of frequency hopping.

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