Multicasting, the transmission of a packet to a group of hosts, is an important service for improving the efficiency and robustness of distributed systems and applications. Although multicast capability is available and widely used in local area networks, when those LANs are interconnected by store-and-forward routers, the multicast service is usually not offered across the resulting internetwork. To address this limitation, we specify extensions to two common internetwork routing algorithms-distance-vector routing and link-state routing-to support low-delay datagram multicasting beyond a single LAN. We also describe modifications to the single-spanning-tree routing algorithm commonly used by link-layer bridges, to reduce the costs of multicasting in large extended LANs. Finally, we discuss how the use of multicast scope control and hierarchical multicast routing allows the multicast service to scale up to large internetworks.

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Abstract—We present a simple theory for maximizable routing metrics. First, we give a formal definition of routing metrics and identify two important properties: boundedness and monotonicity. We show that these two properties are both necessary and sufficient for a routing metric to be maximizable in any network. We show how to combine two (or more) routing metrics into a single composite metric such that if the original metrics are both bounded and monotonic (and, hence, maximizable), then the composite metric is also bounded and monotonic (and, hence, maximizable). We present several applications of our theory. We show that the composite routing metric used in the Inter-Gateway Routing Protocol (IGRP) is not maximizable and we show that Enhanced IGRP (EIGRP) does not behave as expected for nonmonotonic metrics. We also show that a technique for scalable link-state routing does not work correctly when applied to composite metrics. A common theme throughout our paper is that the intuitions generated by using distance metrics to produce shortest paths do not carry over to other routing metrics. Index Terms—Communication system routing, communication system signaling, computer networks, distance vector, distributed

In this paper, we study inverse optimization problems defined as follows. Let S denote the set of feasible solutions of an optimization problem P, let c be a specified cost vector, and x 0 be a given feasible solution. The solution x 0 may or may not be an optimal solution of P with respect to the cost vector c. The inverse optimization problem is to perturb the cost vector c to d so that x 0 is an optimal solution of P with respect to d and �d − c � p is minimum, where �d − c � p is some selected L p norm. In this paper, we consider the inverse linear programming problem under L 1 norm (where �d − c � p = ∑ i∈J w j�d j − c j�, with J denoting the index set of variables x j and w j denoting the weight of the variable j) and under L � norm (where �d −c � p = max j∈J �w j�d j −c j���. We prove the following results: (i) If the problem P is a linear programming problem, then its inverse problem under the L 1 as well as L � norm is also a linear programming problem. (ii) If the problem P is a shortest path, assignment or minimum cut problem, then its inverse problem under the L 1 norm and unit weights can be solved by solving a problem of the same kind. For the nonunit weight case, the inverse problem reduces to solving a minimum cost flow problem. (iii) If the problem P is a minimum cost flowproblem, then its inverse problem under the L 1 norm and unit weights reduces to solving a unit-capacity minimum cost flowproblem. For the nonunit weight case, the inverse problem reduces to solving a minimum cost flowproblem. (iv) If the problem P is a minimum cost flowproblem, then its inverse problem under the L � norm and unit weights reduces to solving a minimum mean cycle problem. For the nonunit weight case, the inverse problem reduces to solving a minimum cost-to-time ratio cycle problem. (v) If the problem P is polynomially solvable for linear cost functions, then inverse versions of P under the L 1 and L � norms are also polynomially solvable.

We have developed an on-line handwriting recognition system. Our approach integrates local bottom-up constructs with a global top-down measure into a modular recognition engine. The bottom-up process uses local point features for hypothesizing character segmentations and the top-down part performs shape matching for evaluating the segmentations. The shape comparison, called Fisher segmental matching, is based on Fisher's linear discriminant analysis. The component character recognizer of the system uses two kinds of Fisher matching based on different representations and combines the information to form the multiple experts paradigm.

In this paper we introduce a method for use state machines as topological maps for remembering the landmarks used to navigate the robot in the static environment. Also we explain how to generalize the state machine model to simplify the search and localization. This generalization process also allows us to remove redundancy from our topological map. We find the shortest path between landmarks, represented as states in a state machine, using breadth-first method for the search. For localizing the robot, we use a simple statistical method. For learning and generalizing the topological map, we use the RPNI (Regular Positive and Negative Inference) algorithm, which learns regular finite automata. We present the experimental results and explain how the environment was modelled.

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Abstract – In this paper we describe FreeSim, which is a fully-customizable macroscopic and microscopic free-flow traffic simulator. FreeSim allows for multiple freeway systems to be easily represented and loaded into the simulator as a graph data structure with edge weights determined by the current speeds. Traffic and graph algorithms can be created and executed for the entire freeway system or for individual vehicles, and the traffic data used by the simulator can be user-generated or be converted from real-time data gathered by a transportation organization. The vehicles in FreeSim can communicate with the system monitoring the traffic on the freeways, which makes FreeSim ideal for ITS simulation. FreeSim is licensed under the GNU General Public License, and the source code is available for download from

We present the BTLA (Base Tree Link Augmentation) multicast routing infrastructure for heterogeneous wireless/wireline networks. BTLA has two goals: (a) to reduce the latency and overhead of joins, leaves, and handoffs in regions that experience high group dynamics and mobility, and (b) to optimize the multicast tree for stable regions that do not experience frequent group dynamics. BTLA consists of two components: (a) pre-computed minimum height spanning trees of the multicast network called base trees that are used to generate and update multicast trees with low latency and overhead under dynamic network conditions, and (b) link augmentation-based optimization of the multicast tree using additional links in the multicast network that are not a part of the base tree, in order to reduce the size of the multicast tree. BTLA uses the base tree approach in dynamic regions and refines it with the link augmentation approach in stable regions. Initial performance evaluation shows that BTLA ...

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