Data Persistence for Zero-Configuration Sensor Networks

Sensor networks are especially useful in catastrophic or emergency scenarios such as floods, fires, terrorist attacks or earthquakes where human participation may be too dangerous. However, such disaster scenarios pose an interesting design challenge since the sensor nodes used to collect and communicate data may themselves fail suddenly and unpredictably, resulting in the loss of valuable data. Furthermore, because these networks are often expected to be deployed in response to a disaster, or because of sudden configuration changes due to failure, these networks are often expected to operate in a “zeroconfiguration” paradigm, where data collection and transmission must be initiated immediately, before the nodes have a chance to assess the current network topology. In this paper, we design and analyze techniques to increase “persistence” of sensed data, so that data is more likely to reach a data sink, even as network nodes fail. This is done by replicating data compactly at neighboring nodes using novel growth codes that increase in efficiency as data accumulates at the sink. We show via simulations that in typical sensor network topologies, our novel protocol can preserve about 10-20% more data than when no coding is done. We also design a dynamically changing codeword degree distribution based on our results and show that it delivers data at a much faster rate compared to other well known degree distributions such as Soliton and Robust-Soliton.