Skip to Main Content
Unlike current models for radio channel shadowing indicate, real-world shadowing losses on different links in a network are not independent. The correlations have both detrimental and beneficial impacts on sensor, ad hoc, and mesh networks. First, the probability of network connectivity reduces when link shadowing correlations are considered. Next, the variance bounds for sensor self-localization change, and provide the insight that algorithms must infer localization information from link correlations in order to avoid significant degradation from correlated shadowing. Finally, a major benefit is that shadowing correlations between links enable the tomographic imaging of an environment from pairwise RSS measurements. This paper applies measurement-based models, and measurements themselves, to analyze and to verify both the benefits and drawbacks of correlated link shadowing.