Low Expected Latency Routing in Dynamic Networks

Timely and efficient message transmission through intermittently and sparsely connected networks is a problem of significant interest to the mobile networking community. Although the long-term statistics describing the time-varying connectivity in such networks can be characterized systematically and can be used for selecting good routes, it may be possible to achieve better performance by intelligently using the actual link states at the time of routing, in conjunction with these statistical dynamics models. In this paper, we investigate a family of minimum expected latency routing methods for such dynamic networks, spanning purely model-based and state-oblivious source routing, state-based source routing, and various flavors of dynamic (or hop-by-hop) routing, with increasing amounts of current link state knowledge around the source. First, we give a heuristic and an approximation scheme for the model-assisted source routing problem, as well as heuristics for the dynamic routing problem. Then we show using extensive simulations on both synthetic and real time-varying connectivity traces that although dynamically sampling link states helps to improve expected routing latency compared to source routing, the marginal improvements decline rapidly for knowledge of current link states beyond 2 hops. To the best of our knowledge, this is the first thorough characterization of the performance of the entire spectrum of model-assisted routing algorithms ranging from little knowledge to complete knowledge of link dynamics.