A Constant-Approximation Algorithm for Budgeted Sweep Coverage with Mobile Sensors

TL;DR

This paper introduces the first constant-approximation algorithm for the budgeted sweep coverage problem, enabling efficient routing of mobile sensors to maximize information collection within budget constraints.

Contribution

It presents a novel constant-approximation algorithm for the budgeted sweep coverage problem by leveraging solutions to the multi-orienteering problem.

Findings

Developed a constant-approximation algorithm for MOP

Achieved a constant-approximation for BSC using MOP solution

Enhanced optimization strategies for mobile sensor deployment

Abstract

In this paper, we present the first constant-approximation algorithm for {\em budgeted sweep coverage problem} (BSC). The BSC involves designing routes for a number of mobile sensors (a.k.a. robots) to periodically collect information as much as possible from points of interest (PoIs). To approach this problem, we propose to first examine the {\em multi-orienteering problem} (MOP). The MOP aims to find a set of $m$ vertex-disjoint paths that cover as many vertices as possible while adhering to a budget constraint $B$. We develop a constant-approximation algorithm for MOP and utilize it to achieve a constant-approximation for BSC. Our findings open new possibilities for optimizing mobile sensor deployments and related combinatorial optimization tasks.