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

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Presented at IROS 2018

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

  1. 1. Presentation #8 SuMo-SS: Submodular Optimization Sensor Scattering for Deploying Sensor Networks by Drones Komei Sugiura National Institute of Information and Communications Tech., Japan
  2. 2. Presentation #8 Target task = sensor scattering: Automatic deployment of disposable wireless sensors by a drone Motivation • Deploying sensors by drones instead of humans has advantages in terms of worker safety and time requirements Technical challenge • Optimal plan to maximize information gain from scattered sensors Human detection in landslideFlash flood detection [Abdulaal+, IWRSN14] Contamination detection NP-hard
  3. 3. Presentation #8 We prove that SubModular Sensor Scattering (SuMo-SS) is online, semi- optimal, and can handle uncertainty Sensor scattering problem is NP-hard • Combinatorial explosion Related work utilized submodular optimization (e.g. [Krause 08]) • (1-1/e)-approximation = 63% of optimal score is guaranteed • However, uncertainty was not handled
  4. 4. Presentation #8 We prove that SubModular Sensor Scattering (SuMo-SS) is online, semi- optimal, and can handle uncertainty Sensor scattering problem is NP-hard • Combinatorial explosion Related work utilized submodular optimization (e.g. [Krause 08]) • (1-1/e)-approximation = 63% of optimal score is guaranteed • However, uncertainty was not handled Proposed method (SuMo-SS) • Handles uncertainty in sensor positions • Does not suffer from combinatorial explosion • (1-1/e)-approximation is guaranteed Without remote control x10
  5. 5. Presentation #8 Sensor model 1. Observations from sensor sets follow the Gaussian distribution 2. Covariance between observations y and y’ can be approximated by the RBF kernel SuMo-SS does not need to know actual landed positions of sensors Sensor positions Input: Covariance between previously scattered sensors and their target positions Output: Next target position & info. gain We give theoretical proof on submodularity No information about actual landed positions is required
  6. 6. Presentation #8 Experimental setup: We used a simulation environment to make experimental results reproducible Physical model • AR.Drone 2.0 with a customized electromagnetic device for attaching/detaching a sensor • Monocular SLAM[Engel+ 14] Simulation model • Purpose: To make the results reproducible – cf. Estimated lifetime of the physical drone is <100 h
  7. 7. Presentation #8
  8. 8. Presentation #8 Quantitative results: Proposed method obtained larger mutual information than baseline and random selection methods Metric = Cumulative mutual info. (a) Proposed (SuMo-SS) (b) Baseline [Krause 08] (c) Random selection Sensitivity analysis • Proposed method outperformed the baseline[Krause 08] in 43/49 conditions Deviation in x-axis Deviationiny-axis *Average of 10 experiments Cumulativemutualinfo.
  9. 9. Presentation #8 Target task Automatic deployment of disposable wireless sensors by a drone Proposed method SuMo-SS can deal with uncertainty in sensor positions Results Proposed method obtained larger mutual information than baseline and random selection methods Interactive presentation #8

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