Decomposing broadcast algorithms using abstract MAC layers

Khabbazian, Majid; Kühn, Fabian; Kowalski, Dariusz R.; Lynch, Nancy

doi:10.1145/1860684.1860690

Cited by 23 publications

(39 citation statements)

References 21 publications

(25 reference statements)

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“…The algorithms mentioned above are all for global broadcast of one message from a known source. For multiple messages, a deterministic algorithm for k messages with complexity O(k log 3 n + n log 4 n) appears in [7], while randomized global broadcast of multiple messages was studied in [5,22,23]. We refer the reader to an excellent survey on broadcasting in radio networks in [37].…”

Section: Related Workmentioning

confidence: 99%

Bounded-Contention Coding for the additive network model

Censor-Hillel

Haeupler²,

Lynch³

et al. 2015

Distrib. Comput.

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Efficient communication in wireless networks is typically challenged by the possibility of interference among several transmitting nodes. Much important research has been invested in decreasing the number of collisions in order to obtain faster algorithms for communication in such networks.This paper proposes a novel approach for wireless communication, which embraces collisions rather than avoiding them, over an additive channel. It introduces a coding technique called Bounded-Contention Coding (BCC) that allows collisions to be successfully decoded by the receiving nodes into the original transmissions and whose complexity depends on a bound on the contention among the transmitters.BCC enables deterministic local broadcast in a network with n nodes and at most a transmitters with information of bits each within O(a log n + a ) bits of communication with full-duplex radios, and O((a log n + a )(log n)) bits, with high probability, with half-duplex radios. When combined with random linear network coding, BCC gives global broadcast within O((D + a + log n)(a log n + )) bits, with high probability. This also holds in dynamic networks that can change arbitrarily over time by a worst-case adversary. When no bound on the contention is given, it is shown how to probabilistically estimate it and obtain global broadcast that is adaptive to the true contention in the network.

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Section: Related Workmentioning

confidence: 99%

Bounded-Contention Coding for the additive network model

Censor-Hillel

Haeupler²,

Lynch³

et al. 2015

Distrib. Comput.

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“…We say an algorithm solves the multihop broadcast problem in r rounds if and only if every process outputs m by round r, w.h.p. The Local Broadcast Problem.In this paper, following the approach of [17], we decompose multihop broadcast into first solving local broadcast, and then using the construction presented in [17] to transform this local solution into a global one.…”

Section: Modelmentioning

confidence: 99%

Leveraging Channel Diversity to Gain Efficiency and Robustness for Wireless Broadcast

Gilbert

Khabbazian

Newport³

2011

Lecture Notes in Computer Science

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This paper addresses two primary questions: (i) How much faster can we disseminate information in a large wireless network if we have multiple communication channels available (as compared to relying on only a single communication channel)? (ii) Can we still disseminate information reliably, even if some subset of the channels are disrupted? In answer to the first question, we reduce the cost of broadcast to O(log log n) rounds/hop, approximately, for sufficiently many channels. We answer the second question in the affirmative, presenting two different algorithms, while at the same time proving a lower bound showing that disrupted channels have unavoidable costs.In more detail, we present upper and lower bounds for the multihop broadcast problem in the tdisrupted radio network model. This model assumes that in every round: each processes can participate on 1 out of C available communication channels, of which up to t < C might be locally disrupted, preventing communication. This model captures the unpredictable message loss that plagues real radio networks. We begin by studying the case with no disruption (t = 0) and present a randomized algorithm that solves broadcast in O((D + log n)(log C + log n C ) rounds, w.h.p., where D is the network diameter and n is the network size. Notice, for a single channel (C = 1), our algorithm has the same running time as the canonical Bar-Yehuda et al. algorithm [3], but as the number of channels increases so does our algorithm's performance advantage. We then prove that for a sufficiently large number of channels, our algorithm is within a O(log log n) factor of optimal.Having shown that multiple channels yield efficiency, we next turn our attention to showing that they also yield robustness. We now consider a setting with adversarial disruption (t > 0) and a common source of randomness. We present a randomized algorithm that solves broadcast in O((D + log n)( C log C log log C C−t + log n C−t )) rounds. For t up to a constant factor of C, this algorithm performs only a factor of O(log log C) slower than the no disruption case: that is, even with significant disruption, our multi-channel algorithm still outperforms solutions that assume a single, perfectly reliable channel. For completeness, we conclude by considering the case with disruption and no common randomness. We demonstrate a clear separation with the common randomness case by proving a lower bound of Ω((D +log n) Ct C−t ) rounds, and then presenting an almost matching randomized upper bound that solves broadcast in O((D + log n) Ct C−t log ( n t )) rounds, w.h.p.

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“…However, it is possible to provide similar guarantees with a high probability (cf. [12]). The MAC layer assumes well-formedness conditions for upper layers.…”

Section: The Abstract Mac Layermentioning

confidence: 99%

“…We follow the specification of an abstract MAC layer presented in [13] (with implementation details provided in [12]). This modular approach makes the algorithm easier to design, understand and verify.…”

Section: Introductionmentioning

confidence: 99%

Reliable neighbor discovery for mobile ad hoc networks

2014

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We define a reliable neighbor discovery layer for mobile adhoc networks and present two algorithms that implement this layer as a service with varying progress guarantees. Our algorithms are implemented atop an abstract MAC layer [13], which deals with the lower level details of collision detection and contention. Specifically, we first describe a basic region-based neighbor discovery protocol with weak progress guarantees. Informally speaking, this protocol does not guarantee communication links when nodes move quickly across region boundaries. To overcome this limitation, we describe a technique that uses a basic neighbor discovery protocol as a black box and boosts its progress guarantees. The key idea behind this technique is to use multiple partitions overlayed in a specific way, and associate with each partition an instance of the basic neighbor discovery protocol. We show the output of these instances can be composed in a way that provides stronger progress guarantees. In the last section of the paper we discuss different user layer algorithms which would benefit from the reliable neighbor discovery layer described in this paper.

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Decomposing broadcast algorithms using abstract MAC layers

Cited by 23 publications

References 21 publications

Bounded-Contention Coding for the additive network model

Bounded-Contention Coding for the additive network model

Leveraging Channel Diversity to Gain Efficiency and Robustness for Wireless Broadcast

Reliable neighbor discovery for mobile ad hoc networks

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