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

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…
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Taxonomy
TopicsUAV Applications and Optimization · Distributed Control Multi-Agent Systems · Robotics and Sensor-Based Localization
