22,141 research outputs found

    A Comparison of Meshes With Static Buses and Unidirectional Wrap-Arounds

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    We investigate the relative computational powers of a mesh with static buses and a mesh with unidirectional wrap-mounds. A mesh with unidirectional wraparounds is a torus with the restriction that any wraparoundlink of the architecture can only transmit data in one of the two directions at any clock tick. We show that the problem of packet routing can be solved as efficiently on a linear array with unidirectional wrap-around link as on a linear array with a broadcast bus. We also present a routing algorithm for a twcdimensional torus with unidirectional wraparound links whose run time is close to that of the best known algorithm for routing on a mesh with broadcast buses in each dimension. In addition, we show that on a mesh with broadcast buses, sorting can be done in time that is essentially the same as the time needed for packet routing

    Broadcasting in Noisy Radio Networks

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    The widely-studied radio network model [Chlamtac and Kutten, 1985] is a graph-based description that captures the inherent impact of collisions in wireless communication. In this model, the strong assumption is made that node vv receives a message from a neighbor if and only if exactly one of its neighbors broadcasts. We relax this assumption by introducing a new noisy radio network model in which random faults occur at senders or receivers. Specifically, for a constant noise parameter p[0,1)p \in [0,1), either every sender has probability pp of transmitting noise or every receiver of a single transmission in its neighborhood has probability pp of receiving noise. We first study single-message broadcast algorithms in noisy radio networks and show that the Decay algorithm [Bar-Yehuda et al., 1992] remains robust in the noisy model while the diameter-linear algorithm of Gasieniec et al., 2007 does not. We give a modified version of the algorithm of Gasieniec et al., 2007 that is robust to sender and receiver faults, and extend both this modified algorithm and the Decay algorithm to robust multi-message broadcast algorithms. We next investigate the extent to which (network) coding improves throughput in noisy radio networks. We address the previously perplexing result of Alon et al. 2014 that worst case coding throughput is no better than worst case routing throughput up to constants: we show that the worst case throughput performance of coding is, in fact, superior to that of routing -- by a Θ(log(n))\Theta(\log(n)) gap -- provided receiver faults are introduced. However, we show that any coding or routing scheme for the noiseless setting can be transformed to be robust to sender faults with only a constant throughput overhead. These transformations imply that the results of Alon et al., 2014 carry over to noisy radio networks with sender faults.Comment: Principles of Distributed Computing 201

    Wireless Broadcast with Network Coding in Mobile Ad-Hoc Networks: DRAGONCAST

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    Network coding is a recently proposed method for transmitting data, which has been shown to have potential to improve wireless network performance. We study network coding for one specific case of multicast, broadcasting, from one source to all nodes of the network. We use network coding as a loss tolerant, energy-efficient, method for broadcast. Our emphasis is on mobile networks. Our contribution is the proposal of DRAGONCAST, a protocol to perform network coding in such a dynamically evolving environment. It is based on three building blocks: a method to permit real-time decoding of network coding, a method to adjust the network coding transmission rates, and a method for ensuring the termination of the broadcast. The performance and behavior of the method are explored experimentally by simulations; they illustrate the excellent performance of the protocol

    Transform-based Distributed Data Gathering

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    A general class of unidirectional transforms is presented that can be computed in a distributed manner along an arbitrary routing tree. Additionally, we provide a set of conditions under which these transforms are invertible. These transforms can be computed as data is routed towards the collection (or sink) node in the tree and exploit data correlation between nodes in the tree. Moreover, when used in wireless sensor networks, these transforms can also leverage data received at nodes via broadcast wireless communications. Various constructions of unidirectional transforms are also provided for use in data gathering in wireless sensor networks. New wavelet transforms are also proposed which provide significant improvements over existing unidirectional transforms
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