SuMo-SS: Submodular Optimization Sensor Scattering for Deploying Sensor Networks by Drones

TL;DR

This paper introduces SuMo-SS, an online planning algorithm for drone-based sensor deployment that maximizes information gain while handling positional uncertainties, demonstrated through physical and simulated experiments.

Contribution

The paper presents a novel online planning method, SuMo-SS, for sensor scattering that accounts for uncertainty and achieves near-optimal solutions efficiently.

Findings

SuMo-SS effectively handles uncertainty in sensor positions.

The method achieves a (1-1/e)-approximation of the optimal solution.

Experimental validation confirms the method's practicality in indoor environments.

Abstract

To meet the immediate needs of environmental monitoring or hazardous event detection, we consider the automatic deployment of a group of low-cost or disposable sensors by a drone. Introducing sensors by drones to an environment instead of humans has advantages in terms of worker safety and time requirements. In this study, we define "sensor scattering (SS)" as the problem of maximizing the information-theoretic gain from sensors scattered on the ground by a drone. SS is challenging due to its combinatorial explosion nature, because the number of possible combination of sensor positions increases exponentially with the increase in the number of sensors. In this paper, we propose an online planning method called SubModular Optimization Sensor Scattering (SuMo-SS). Unlike existing methods, the proposed method can deal with uncertainty in sensor positions. It does not suffer from combinatorial explosion but obtains a (1-1/e)-approximation of the optimal solution. We built a physical drone that can scatter sensors in an indoor environment as well as a simulation environment based on the drone and the environment. In this paper, we present the theoretical background of our proposed method and its experimental validation.