Deterministic Broadcasting and Gossiping with Beeps

TL;DR

This paper presents deterministic algorithms for broadcasting and gossiping in the beeping communication model, achieving optimal and near-optimal times, respectively, in very weak network conditions.

Contribution

It introduces the first deterministic algorithms for broadcasting and gossiping in the beeping model with proven optimal or near-optimal time complexities.

Findings

Broadcasting in time O(D + m), which is optimal.

Gossiping in time O(N(M + D log L)), providing an efficient solution.

Achieves communication in very weak, beeping-based network models.

Abstract

Broadcasting and gossiping are fundamental communication tasks in networks. In broadcasting,one node of a network has a message that must be learned by all other nodes. In gossiping, every node has a (possibly different) message, and all messages must be learned by all nodes. We study these well-researched tasks in a very weak communication model, called the {\em beeping model}. Communication proceeds in synchronous rounds. In each round, a node can either listen, i.e., stay silent, or beep, i.e., emit a signal. A node hears a beep in a round, if it listens in this round and if one or more adjacent nodes beep in this round. All nodes have different labels from the set $\{0,\dots , L-1\}$. Our aim is to provide fast deterministic algorithms for broadcasting and gossiping in the beeping model. Let $N$ be an upper bound on the size of the network and $D$ its diameter. Let $m$ be the size of the message in broadcasting, and $M$ an upper bound on the size of all input messages in gossiping. For the task of broadcasting we give an algorithm working in time $O(D+m)$ for arbitrary networks, which is optimal. For the task of gossiping we give an algorithm working in time $O(N(M+D\log L))$ for arbitrary networks. At the time of writing this paper we were unaware of the paper: A. Czumaj, P. Davis, Communicating with Beeps, arxiv:1505.06107 [cs.DC] which contains the same results for broadcasting and a stronger upper bound for gossiping in a slightly different model.