Abstract

In this work we consider the edge searching problem for vertex-weighted graphs with arbitrarily fast and invisible fugitive. The weight function $${\omega }$$? provides for each vertex $$v$$v the minimum number of searchers required to guard $$v$$v, i.e., the fugitive may not pass through $$v$$v without being detected only if at least $${\omega }(v)$$?(v) searchers are present at $$v$$v. This problem is a generalization of the classical edge searching problem, in which one has $${\omega }\equiv 1$$??1. We assume that with a graph $$G$$G to be searched, there is associated a partition $$(V_1,\ldots ,V_t)$$(V1,?,Vt) of its vertex set such that edges are allowed only within each $$V_i$$Vi and between two consecutive $$V_i$$Vi's. We provide an algorithm for distributed monotone connected edge searching of such graphs, where the searchers are initially placed on an arbitrary vertex of $$G$$G and have no a priori knowledge on $$G$$G, but they have a sense of direction that lets them recognize whether an edge incident to already explored vertex in $$V_i$$Vi leads to a vertex in one of $$V_{i-1}, V_i$$Vi-1,Vi or $$V_{i+1}$$Vi+1. Starting from any vertex the algorithm uses at most $$3\cdot \max _{i=1,\ldots ,t}{\omega }(V_i)+1$$3·maxi=1,?,t?(Vi)+1 searchers, where $${\omega }(V_i) = \sum _{v\in V_i}{\omega }(v)$$?(Vi)=?v?Vi?(v). We also prove that this algorithm is best possible up to a small additive constant, that is, each distributed searching algorithm in worst case must use $$3\cdot \max _{i=1,\ldots ,t}{\omega }(V_i)-1$$3·maxi=1,?,t?(Vi)-1 searchers for some graphs.

Highlights

  • A team of mobile agents explores an unknown environment modeled as a graph in order to accomplish a selected task

  • We prove that the algorithm from Theorem 1 is best possible up to an additive constant of 2, i.e., there exists an infinite class of graphs with grid partitions such that each graph G in the class has a vertex h such that any monotone connected search strategy with homebase h uses at least 3w(G) − 1 searchers

  • An LL-expansion is a stage in which the searchers made sliding moves from all vertices in Vr(Lk−1) ∩ l(Rk+1) > r (Lk)−1 via ports leading to vertices in Vr(Lk−1)−1, with the latter ones possibly added to the new left border Lk (as we prove later, for an LL-expansion, r (Lk) < r (Lk−1))

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Summary

Introduction

A team of mobile agents explores an unknown environment modeled as a graph in order to accomplish a selected task. Even if the graph is a tree on n vertices, in the worst case every online search strategy of such a type needs Θ(n) searchers [31], while O(log n) searchers are enough in an offline solution [35] This negative result justifies an assumption that the searchers have some additional ability that allows them to construct a strategy more efficiently. The searchers have to agree on the placement of scanlines prior to the beginning of exploration and this model is valid if there are no tunnel intersections ‘between’ the scanlines— one way of overcoming this issue is to take small distance between consecutive scanlines (this may lead to many degree two vertices in the graph but such vertices introduce no difficulty while constructing edge search strategies). If the searchers are equipped with a compass and are able to measure the distance traveled, each of them is able to translate an edge traversal in the graph into the cor-

Graph searching problem
Search strategies
Our results
Related work
The algorithm
Analysis of the algorithm
Lower bound
Relations to connected path decompositions
Computations with advice
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