SuMo-SS: Submodular Optimization Sensor Scattering for Deploying Sensor Networks by Drones
This addresses the need for automated, safe, and efficient sensor deployment in environmental monitoring or hazard detection, though it appears incremental as it builds on submodular optimization methods.
The authors tackled the problem of deploying sensor networks via drones by maximizing information gain from scattered sensors, proposing SuMo-SS which achieves a (1-1/e)-approximation of the optimal solution without combinatorial explosion.
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.