Mattia D'Emidio

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We propose a simple and practical speed-up technique, which can be combined with every distance vector routing algorithm based on shortest paths, allowing to reduce the total number of messages sent by that algorithm. We combine the new technique with two algorithms known in the literature: DUAL, which is part of CISCO's widely used EIGRP protocol, and the(More)
We present LFR (Loop Free Routing), a new loop-free distance vector routing algorithm, which is able to update the shortest paths of a distributed network with n nodes in fully dynamic scenarios. If Φ is the total number of nodes affected by a set of updates to the network, and φ is the maximum number of destinations for which a node is affected , then LFR(More)
Let G = (V, E) be an n-nodes non-negatively real-weighted undirected graph. In this paper we show how to enrich a single-source shortest-path tree (SPT) of G with a sparse set of auxiliary edges selected from E, in order to create a structure which tolerates effectively a path failure in the SPT. This consists of a simultaneous fault of a set F of at most f(More)
The problem of finding best routes in road networks can be solved by applying Dijkstra's shortest paths algorithm. Unfortunately, road networks deriving from real-world applications are huge yielding unsustainable times to compute shortest paths. For this reason, great research efforts have been done to accelerate Dijkstra's algorithm on road networks.(More)
The problem of finding and updating shortest paths in distributed networks is considered crucial in today's practical applications. In the recent past, there has been a renewed interest in devising new efficient distance-vector algorithms as an attractive alternative to link-state solutions for large-scale Ethernet networks, in which scalability and(More)
The distributed setting of computational mobile entities, called robots, thathave to perform tasks without global coordination has been extensively studied in the literature. A well-known scenario is that in which robots operate in Look-Compute-Move (LCM) cycles. During each cycle, a robot acquires asnapshot of the surrounding environment (Look phase), then(More)