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Random walks have been proposed as a simple method of efficiently searching,or disseminating information throughout, communication and sensor networks. Innature, animals (such as ants) tend to follow correlated random walks, i.e.,random walks that are biased towards their current heading. In this paper, weinvestigate whether or not complementing random walks with directional bias candecrease the expected discovery and coverage times in networks.  To do so, we develop a macro-level model of a directionally biased randomwalk based on Markov chains. By focussing on regular, connected networks, themodel allows us to efficiently calculate expected coverage times for differentnetwork sizes and biases. Our analysis shows that directional bias cansignificantly reduce coverage time, but only when the bias is below a certainvalue which is dependent on the network size.

A macro-level model for investigating the effect of directional bias on network coverage