Building Fastest Broadcast Trees in Periodically-Varying Graphs

TL;DR

This paper presents a method to construct fastest broadcast trees in periodically-varying graphs, addressing the challenge of optimizing broadcast duration in delay-tolerant networks with intermittent, arbitrarily long contacts.

Contribution

It introduces a novel approach to build fastest broadcast trees in time-varying graphs with periodic contacts, considering mixed contact durations and using temporal distance abstraction.

Findings

Successfully computes fastest broadcast trees in complex periodic graphs.

Addresses the challenge of mixed continuous and discontinuous contact segments.

Provides a framework for minimizing broadcast duration in delay-tolerant networks.

Abstract

Delay-tolerant networks (DTNs) are characterized by a possible absence of end-to-end communication routes at any instant. Still, connectivity can generally be established over time and space. The optimality of a temporal path (journey) in this context can be defined in several terms, including topological (e.g. {\em shortest} in hops) and temporal (e.g. {\em fastest, foremost}). The combinatorial problem of computing shortest, foremost, and fastest journeys {\em given full knowledge} of the network schedule was addressed a decade ago (Bui-Xuan {\it et al.}, 2003). A recent line of research has focused on the distributed version of this problem, where foremost, shortest or fastest {\em broadcast} are performed without knowing the schedule beforehand. In this paper we show how to build {\em fastest} broadcast trees (i.e., trees that minimize the global duration of the broadcast, however late the departure is) in Time-Varying Graphs where intermittent edges are available periodically (it is known that the problem is infeasible in the general case even if various parameters of the graph are know). We address the general case where contacts between nodes can have arbitrary durations and thus fastest routes may consist of a mixture of {\em continuous} and {\em discontinuous} segments (a more complex scenario than when contacts are {\em punctual} and thus routes are only discontinuous). Using the abstraction of \tclocks to compute the temporal distances, we solve the fastest broadcast problem by first learning, at the emitter, what is its time of {\em minimum temporal eccentricity} (i.e. the fastest time to reach all the other nodes), and second by building a {\em foremost} broadcast tree relative to this particular emission date.