Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...
Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...
Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...
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10 H. Dubois-Ferrière, M. Grossglauser, and M. VetterliIn GREP rout<strong>in</strong>g state never times out: a rout<strong>in</strong>g entry can only be deletedwhen a newer entry overrides it. This is because a past route, which was oftenacquired at a high flood<strong>in</strong>g cost, can still carry noisy, but useful <strong>in</strong>formationabout the present topology.We consider two simple scenarios to illustrate this. The first scenario isstraightforward and concerns a short-term timescale. Consider a route whichhas been left unused for some time. In this time, one of the nodes <strong>in</strong> the routehas moved. Clearly, tim<strong>in</strong>g out the whole route at this po<strong>in</strong>t would impose acostly re-discovery if the route is needed aga<strong>in</strong>; if we keep all rout<strong>in</strong>g entriesonly a small, local repair is necessary.In the second scenario we consider a long-term timescale, on the order ofthe time required for nodes to traverse the whole area that they <strong>in</strong>habit. One<strong>in</strong>tuition might be that rout<strong>in</strong>g state has no value at this timescale, s<strong>in</strong>ce thecurrent topology has no relationship with the topology at the time when theroute was established. However, this timescale is precisely the one considered <strong>in</strong>FRESH [3] where we have shown that one-hop rout<strong>in</strong>g entries, however old, canbe used to constra<strong>in</strong> new route discoveries and significantly decrease the flood<strong>in</strong>goverhead.Hop-<strong>by</strong>-Hop Rout<strong>in</strong>g. Rout<strong>in</strong>g protocols typically view a route as a consistentend-to-end structure. In this model a route must be set up and converged fromsource to dest<strong>in</strong>ation before data can flow across it. Clearly this is well-suited(and has been proven) to wired networks, where topology changes are rare events.For more dynamic networks, where change is the norm rather than exception,the brittle nature of this model can degrade performance. For example, a s<strong>in</strong>glel<strong>in</strong>k break can br<strong>in</strong>g down an entire route, even when most of the route rema<strong>in</strong>svalid. As networks grow larger and routes get longer, the probability of a l<strong>in</strong>kbreak along a route <strong>in</strong>creases, and the amount of time when a route is availablecorrespond<strong>in</strong>gly goes down. This reduces the overall throughput available to anapplication.GREP does away with the notion of end-to-end routes and views routesas distributed structures which cont<strong>in</strong>uously adapt to change rather than beentirely torn down and rebuild from scratch at each topology change. In thishop-<strong>by</strong>-hop rout<strong>in</strong>g approach a source does not have to stop send<strong>in</strong>g when a l<strong>in</strong>kchanges along the route to the dest<strong>in</strong>ation; <strong>in</strong> fact it is <strong>in</strong> most cases not evenaware that a local repair happened further along the route.We should also note that the exclusive use of local repairs has one drawback,namely this may result <strong>in</strong> suboptimal routes that are longer than the shortestpossible path. Though our simulation results show that this is effect is not severeenough to damage GREP’s performance, we believe that a worthwhile extensionto GREP will allow a node to progressively ‘shorten’ a suboptimal route as asession goes on.5 ConclusionsIn this paper we have <strong>in</strong>troduced a new approach to rout<strong>in</strong>g <strong>in</strong> mobile ad hocnetworks, which we call space-time rout<strong>in</strong>g (STR). This approach uses both