Distributed Randomized Broadcasting in Wireless Networks under the SINR Model

Jurdziński, Tomasz; Kowalski, Dariusz R.

doi:10.1007/978-1-4939-2864-4_604

Cited by 21 publications

(48 citation statements)

References 5 publications

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“…An overview of works regarding transmission scheduling in the SINR model can be found in a survey by Goussevskaia, Pignolet and Wattenhofer [19]. Other results cover Broadcasting [20,21], Local Broadcasting [1][2][3][4] and backbone construction [22]. Regarding distributed node coloring in the SINR model Derbel and Talbi [5] show that a distributed node coloring algorithm due to Moscibroda and Wattenhofer [23,24] can be adapted to the SINR model.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

Simple Distributed Δ + 1 Coloring in the SINR Model

Fuchs

Prutkin

2015

Lecture Notes in Computer Science

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In wireless ad hoc or sensor networks, distributed node coloring is a fundamental problem closely related to establishing efficient communication through TDMA schedules. For networks with maximum degree ∆, a ∆ + 1 coloring is the ultimate goal in the distributed setting as this is always possible. In this work we propose ∆ + 1 coloring algorithms for the synchronous and asynchronous setting. All algorithms have a runtime of O(∆ log n) time slots. This improves on the previous algorithms for the SINR model either in terms of the number of required colors or the runtime and matches the runtime of local broadcasting in the SINR model (which can be seen as a lower bound).

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Section: Related Work and Contributionsmentioning

confidence: 99%

Simple Distributed Δ + 1 Coloring in the SINR Model

Fuchs

Prutkin

2015

Lecture Notes in Computer Science

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“…In [14] an O(D log n + log 2 n) randomized algorithm and in [15] an O(D log 2 n) deterministic algorithm for networks deployed in the Euclidean space are presented, where stations know their own positions (e.g., thanks to GPS devices). Finally, Daum et al [5] designed an algorithm working in O((D log n) log α+1 R s ) rounds, provided stations know only granularity R s of the network (i.e., the maximum ratio between actual distances of stations connected by an edge in the communication graph) and do not use any other additional features.…”

Section: Previous and Related Workmentioning

confidence: 99%

“…The communication graph, through restricting connections to ranges at most 1 − ε, envisions the network of such "reasonable reachability". It has become a classic tool in the analysis of ad hoc communication tasks under the SINR physical model c.f., [5,14,21].…”

Section: Introductionmentioning

confidence: 99%

On the impact of geometry on ad hoc communication in wireless networks

Jurdziński

Kowalski

Różański

et al. 2014

Proceedings of the 2014 ACM Symposium on Principles of Distributed Computing

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In this work we address the question how important is the knowledge of geometric location and network density to the efficiency of (distributed) wireless communication in ad hoc networks. We study fundamental communication task of broadcast and develop well-scalable, randomized algorithms that do not rely on GPS information, and which efficiency formulas do not depend on how dense the geometric network is. We consider two settings: with and without spontaneous wake-up of nodes. In the former setting, in which all nodes start the protocol at the same time, our algorithm accomplishes broadcast in O(D log n + log 2 n) rounds under the SINR model, with high probability (whp), where D is the diameter of the communication graph and n is the number of stations. In the latter setting, in which only the source node containing the original message is active in the beginning, we develop a slightly slower algorithm working in O(D log 2 n) rounds whp. Both algorithms are based on a novel distributed coloring method, which is of independent interest and potential applicability to other communication tasks under the SINR wireless model.

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“…Most existing work on distributed algorithms for wireless networks assumes low-level synchronous models that require algorithms to deal directly with link-layer issues such as signal fading and channel contention. Some of these models use topology graphs to determine message behavior (c.f., [6,24,27,34,13,16]) while others use signal strength calculations (c.f., [35,33,17,22,23,14]). These models are well-suited for asking basic science questions about the capabilities of wireless communication.…”

Section: Introductionmentioning

confidence: 99%

“…Most existing work on distributed algorithms for wireless networks assumes low-level synchronous models that force algorithms to directly grapple with issues caused by contention and signal fading. Some of these models describe the network topology with a graph (c.f., [8,16,20,28,32,39]), while others use signal strength calculations to determine message behavior (c.f., [17,21,26,27,38,40]).…”

Section: Introductionmentioning

confidence: 99%

Consensus with an abstract MAC layer

Newport

2014

Proceedings of the 2014 ACM Symposium on Principles of Distributed Computing

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In this paper, we study fault-tolerant distributed consensus in wireless systems. In more detail, we produce two new randomized algorithms that solve this problem in the abstract MAC layer model, which captures the basic interface and communication guarantees provided by most wireless MAC layers. Our algorithms work for any number of failures, require no advance knowledge of the network participants or network size, and guarantee termination with high probability after a number of broadcasts that are polynomial in the network size. Our first algorithm satisfies the standard agreement property, while our second trades a faster termination guarantee in exchange for a looser agreement property in which most nodes agree on the same value. These are the first known fault-tolerant consensus algorithms for this model. In addition to our main upper bound results, we explore the gap between the abstract MAC layer and the standard asynchronous message passing model by proving fault-tolerant consensus is impossible in the latter in the absence of information regarding the network participants, even if we assume no faults, allow randomized solutions, and provide the algorithm a constant-factor approximation of the network size. IntroductionConsensus provides a fundamental building block for developing reliable distributed systems [23][24][25]. Accordingly, it is well studied in many different system models [36]. Until recently, however, little was known about solving this problem in distributed systems made up of devices communicating using commodity wireless cards. Motivated by this knowledge gap, this paper studies consensus in the abstract MAC layer model, which abstracts the basic behavior and guarantees of standard wireless MAC layers. In recent work [43], we proved deterministic fault-tolerant consensus is impossible in this setting. In this paper, we describe and analyze the first known randomized fault-tolerant consensus algorithms for this well-motivated model.The Abstract MAC Layer. Most existing work on distributed algorithms for wireless networks assumes low-level synchronous models that force algorithms to directly grapple with issues caused by contention and signal fading. Some of these models describe the network topology with a graph (c.f., [8,16,20,28,32,39]), while others use signal strength calculations to determine message behavior (c.f., [17,21,26,27,38,40]). 1As also emphasized in [43], these models are useful for asking foundational questions about distributed computation on shared channels, but are not so useful for developing algorithmic strategies suitable for deployment. In real systems, algorithms typically do not operate in synchronous rounds and they are not provided unmediated access to the radio. They must instead operate on top of a general-purpose MAC layer which is responsible for many network functions, including contention management, rate control, and co-existence with other network traffic.Motivated by this reality, in this paper we adopt the abstract MAC layer model [34], an asynchrono...

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Distributed Randomized Broadcasting in Wireless Networks under the SINR Model

Cited by 21 publications

References 5 publications

Simple Distributed Δ + 1 Coloring in the SINR Model

Simple Distributed Δ + 1 Coloring in the SINR Model

On the impact of geometry on ad hoc communication in wireless networks

Consensus with an abstract MAC layer

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