Stochastic location-aided routing for mobile ad-hoc networks

We study location-aided routing under mobility in wireless ad-hoc networks. Due to node mobility, the network topology changes continuously, and clearly there exists an intricate tradeoff between the message passing overhead and the latency in the rout discovery process. Aiming to obtain a clear understanding of this tradeoff, we use Stochastic Semidefinite Programming (SSDP), a newly developed optimization model, to deal with location uncertainty associated with node mobility. In particular, we model both the speed and the direction of node movement by random variables and construct random ellipses accordingly to better capture the location uncertainty and the heterogeneity across different nodes. Based on SSDP, we propose a stochastic location-aided routing (SLAR) strategy to optimize the tradeoff between the message passing overhead and the latency. Our results reveal that in general SLAR can significantly reduce the overall overhead than existing deterministic algorithms, simply because the location uncertainty in the routing problem is better captured by the SSDP model.