Center for Embedded Network Sensing

Recently, several studies have analyzed the statistical properties of low power wireless links in real environments, clearly demonstrating the differences between experimentally observed communication properties and widely used simulation models. However, most of these studies have not performed in depth analysis of the temporal properties of wireless links. These properties have high impact on the performance of routing algorithms.

Our first goal is to study the statistical temporal properties of links in low power wireless communications. We study short term temporal issues, like lagged autocorrelation of individual links, lagged correlation of reverse links, and consecutive same path links. We also study long term temporal aspects, gaining insight on the length of time the channel needs to be measured and how often we should update our models.

Our second objective is to explore how statistical temporal properties impact routing protocols. We studied one-to-one routing schemes and developed new routing algorithms that consider autocorrelation, and reverse link and consecutive same path link lagged correlations. We have developed two new routing algorithms for the cost link model: (i) a generalized Dijkstra algorithm with centralized execution, and (ii)a localized distributed probabilistic algorithm.