Deploying Wireless Networks with Beeps

TL;DR

This paper introduces a minimalistic beeping communication model for wireless networks, presenting efficient algorithms for interval coloring that outperform previous methods and are proven optimal for certain network types.

Contribution

The paper proposes the discrete beeping model, develops fast algorithms for interval coloring, and establishes lower bounds, advancing understanding of minimal communication in wireless networks.

Findings

An O(1) time algorithm for interval coloring with high probability.

A Las Vegas algorithm achieving O(log n) time for interval coloring.

Lower bound of Ω(log n) time for constant degree graphs.

Abstract

We present the \emph{discrete beeping} communication model, which assumes nodes have minimal knowledge about their environment and severely limited communication capabilities. Specifically, nodes have no information regarding the local or global structure of the network, don't have access to synchronized clocks and are woken up by an adversary. Moreover, instead on communicating through messages they rely solely on carrier sensing to exchange information. We study the problem of \emph{interval coloring}, a variant of vertex coloring specially suited for the studied beeping model. Given a set of resources, the goal of interval coloring is to assign every node a large contiguous fraction of the resources, such that neighboring nodes share no resources. To highlight the importance of the discreteness of the model, we contrast it against a continuous variant described in [17]. We present an O(1$ time algorithm that terminates with probability 1 and assigns an interval of size $\Omega(T/\Delta)$ that repeats every $T$ time units to every node of the network. This improves an $O(\log n)$ time algorithm with the same guarantees presented in \cite{infocom09}, and accentuates the unrealistic assumptions of the continuous model. Under the more realistic discrete model, we present a Las Vegas algorithm that solves $\Omega(T/\Delta)$-interval coloring in $O(\log n)$ time with high probability and describe how to adapt the algorithm for dynamic networks where nodes may join or leave. For constant degree graphs we prove a lower bound of $\Omega(\log n)$ on the time required to solve interval coloring for this model against randomized algorithms. This lower bound implies that our algorithm is asymptotically optimal for constant degree graphs.