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Documents authored by Zheng, Chaodong

Document
DOI: 10.4230/LIPIcs.OPODIS.2020.22
Broadcasting Competitively Against Adaptive Adversary in Multi-Channel Radio Networks

Authors: Haimin Chen and Chaodong Zheng

Published in: LIPIcs, Volume 184, 24th International Conference on Principles of Distributed Systems (OPODIS 2020)

Abstract
Broadcasting in wireless networks is vulnerable to adversarial jamming. To thwart such behavior, resource competitive analysis is proposed. In this framework, sending, listening, or jamming on one channel for one time slot costs one unit of energy. The adversary can employ arbitrary strategy to disrupt communication, but has a limited energy budget T. The honest nodes, on the other hand, aim to accomplish broadcast while spending only o(T). Previous work has shown, in a C-channels network containing n nodes, for large T values, each node can receive the message in Õ(T/C) time, while spending only Õ(√{T/n}) energy. However, these multi-channel algorithms only work for certain values of n and C, and can only tolerate an oblivious adversary. In this work, we provide new upper and lower bounds for broadcasting in multi-channel radio networks, from the perspective of resource competitiveness. Our algorithms work for arbitrary n,C values, require minimal prior knowledge, and can tolerate a powerful adaptive adversary. More specifically, in our algorithms, for large T values, each node’s runtime is O(T/C), and each node’s energy cost is Õ(√{T/n}). We also complement algorithmic results with lower bounds, proving both the time complexity and the energy complexity of our algorithms are optimal or near-optimal (within a poly-log factor). Our technical contributions lie in using "epidemic broadcast" to achieve time efficiency and resource competitiveness, and employing coupling techniques in the analysis to handle the adaptivity of the adversary. At the lower bound side, we first derive a new energy complexity lower bound for 1-to-1 communication in the multi-channel setting, and then apply simulation and reduction arguments to obtain the desired result.
Cite as

Haimin Chen and Chaodong Zheng. Broadcasting Competitively Against Adaptive Adversary in Multi-Channel Radio Networks. In 24th International Conference on Principles of Distributed Systems (OPODIS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 184, pp. 22:1-22:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{chen_et_al:LIPIcs.OPODIS.2020.22,
  author =	{Chen, Haimin and Zheng, Chaodong},
  title =	{{Broadcasting Competitively Against Adaptive Adversary in Multi-Channel Radio Networks}},
  booktitle =	{24th International Conference on Principles of Distributed Systems (OPODIS 2020)},
  pages =	{22:1--22:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-176-4},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{184},
  editor =	{Bramas, Quentin and Oshman, Rotem and Romano, Paolo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2020.22},
  URN =		{urn:nbn:de:0030-drops-135076},
  doi =		{10.4230/LIPIcs.OPODIS.2020.22},
  annote =	{Keywords: Broadcast, radio networks, resource competitive algorithms}
}
Document
DOI: 10.4230/LIPIcs.OPODIS.2018.26
Approximate Neighbor Counting in Radio Networks

Authors: Calvin Newport and Chaodong Zheng

Published in: LIPIcs, Volume 125, 22nd International Conference on Principles of Distributed Systems (OPODIS 2018)

Abstract
For many distributed algorithms, neighborhood size is an important parameter. In radio networks, however, obtaining this information can be difficult due to ad hoc deployments and communication that occurs on a collision-prone shared channel. This paper conducts a comprehensive survey of the approximate neighbor counting problem, which requires nodes to obtain a constant factor approximation of the size of their network neighborhood. We produce new lower and upper bounds for three main variations of this problem in the radio network model: (a) the network is single-hop and every node must obtain an estimate of its neighborhood size; (b) the network is multi-hop and only a designated node must obtain an estimate of its neighborhood size; and (c) the network is multi-hop and every node must obtain an estimate of its neighborhood size. In studying these problem variations, we consider solutions with and without collision detection, and with both constant and high success probability. Some of our results are extensions of existing strategies, while others require technical innovations. We argue this collection of results provides insight into the nature of this well-motivated problem (including how it differs from related symmetry breaking tasks in radio networks), and provides a useful toolbox for algorithm designers tackling higher level problems that might benefit from neighborhood size estimates.
Cite as

Calvin Newport and Chaodong Zheng. Approximate Neighbor Counting in Radio Networks. In 22nd International Conference on Principles of Distributed Systems (OPODIS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 125, pp. 26:1-26:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{newport_et_al:LIPIcs.OPODIS.2018.26,
  author =	{Newport, Calvin and Zheng, Chaodong},
  title =	{{Approximate Neighbor Counting in Radio Networks}},
  booktitle =	{22nd International Conference on Principles of Distributed Systems (OPODIS 2018)},
  pages =	{26:1--26:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-098-9},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{125},
  editor =	{Cao, Jiannong and Ellen, Faith and Rodrigues, Luis and Ferreira, Bernardo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.OPODIS.2018.26},
  URN =		{urn:nbn:de:0030-drops-100860},
  doi =		{10.4230/LIPIcs.OPODIS.2018.26},
  annote =	{Keywords: Radio networks, neighborhood size estimation, approximate counting}
}
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