Communication-aware motion planning in fading environments

In this paper we create a framework to model and characterize the impact of time-varying fading communication links on the performance of a mobile sensor network. We propose communication-aware motion-planning strategies, where each node incorporates statistical learning of communication link qualities, such as Signal to Noise Ratio (SNR) and correlation characteristics, into its motion-planning function. We show that while uncorrelated fading channels can ruin the overall performance, the introduced natural randomization can potentially help the nodes leave deep fade spots. We furthermore show that highly correlated deep fades, on the other hand, can degrade the performance drastically for a long period of time. We then propose a randomizing motion-planning strategy that can help the nodes leave highly correlated deep fades.