Improved lower bound for deterministic broadcasting in radio networks

TL;DR

This paper establishes a new lower bound of Omega(√n) rounds for deterministic broadcasting in radio networks with limited topology knowledge, using a novel simulation argument that could influence future research.

Contribution

It introduces a new lower bound for broadcasting in radius 2 networks and develops a novel simulation technique for analyzing deterministic protocols.

Findings

Proves Omega(√n) lower bound for radius 2 networks.

Extends lower bound to Omega(√(nD)) for radius D networks.

Introduces a novel simulation argument for protocol analysis.

Abstract

We consider the problem of deterministic broadcasting in radio networks when the nodes have limited knowledge about the topology of the network. We show that for every deterministic broadcasting protocol there exists a network, of radius 2, for which the protocol takes at least $\Omega(\sqrt{n}) rounds for completing the broadcast. Our argument can be extended to prove a lower bound of Omega(\sqrt{nD}) rounds for broadcasting in radio networks of radius D. This resolves one of the open problems posed in [29], where in the authors proved a lower bound of $\Omega(n^{1/4}) rounds for broadcasting in constant diameter networks. We prove the new lower $\Omega(\sqrt{n})$ bound for a special family of radius 2 networks. Each network of this family consists of O(\sqrt{n}) components which are connected to each other via only the source node. At the heart of the proof is a novel simulation argument, which essentially says that any arbitrarily complicated strategy of the source node can be simulated by the nodes of the networks, if the source node just transmits partial topological knowledge about some component instead of arbitrary complicated messages. To the best of our knowledge this type of simulation argument is novel and may be useful in further improving the lower bound or may find use in other applications. Keywords: radio networks, deterministic broadcast, lower bound, advice string, simulation, selective families, limited topological knowledge.