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Distributed Online Data Aggregation in Dynamic Graphs

  • Quentin BramasEmail author
  • Toshimitsu Masuzawa
  • Sébastien Tixeuil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11704)

Abstract

We consider the problem of aggregating data in a dynamic graph, that is, aggregating the data that originates from all nodes in the graph to a specific node, the sink. We are interested in giving lower bounds for this problem, under different kinds of adversaries.

In our model, nodes are endowed with unlimited memory and unlimited computational power. Yet, we assume that communications between nodes are carried out with pairwise interactions, where nodes can exchange control information before deciding whether they transmit their data or not, given that each node is allowed to transmit its data at most once. When a node receives a data from a neighbor, the node may aggregate it with its own data.

We consider three possible adversaries: the online adaptive adversary, the oblivious adversary, and the randomized adversary that chooses the pairwise interactions uniformly at random. For the online adaptive and the oblivious adversaries, we give impossibility results when nodes have no knowledge about the graph and are not aware of the future. Also, we give several tight bounds depending on the knowledge (be it topology related or time related) of the nodes. For the randomized adversary, we show that the Gathering algorithm, which always commands a node to transmit, is optimal if nodes have no knowledge at all. Also, we propose an algorithm called Waiting Greedy, where a node either waits or transmits depending on some parameter, that is optimal when each node knows its future pairwise interactions with the sink.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Quentin Bramas
    • 1
    Email author
  • Toshimitsu Masuzawa
    • 3
  • Sébastien Tixeuil
    • 2
  1. 1.University of Strasbourg, ICUBE, CNRSStrasbourgFrance
  2. 2.Sorbonne University, LIP6, CNRSParisFrance
  3. 3.Osaka UniversitySuitaJapan

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