Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance

Ryusei Sugano; Ryoichi Shinkuma; Takayuki Nishio; Sohei Itahara; Narayan B. Mandayam

Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance

Machine Learning 2022-03-08 v1 Artificial Intelligence Multiagent Systems Networking and Internet Architecture

Authors: Ryusei Sugano , Ryoichi Shinkuma , Takayuki Nishio , Sohei Itahara , Narayan B. Mandayam

Abstract

This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding, computing, and patrolling, for predictive police surveillance. Our framework is promising for crime deterrence because UAVs are useful for collecting and distributing data and have high mobility. Our framework relies on machine learning (ML) technology for controlling and dispatching UAVs and predicting crimes. This paper compares the conceptual model of our framework against the literature. It also reports a simulation of UAV dispatching using reinforcement learning and distributed ML inference over a lossy UAV network.

Keywords

uav tracking unmanned aerial vehicle unmanned aerial vehicle networks

Cite

@article{arxiv.2203.02892,
  title  = {Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance},
  author = {Ryusei Sugano and Ryoichi Shinkuma and Takayuki Nishio and Sohei Itahara and Narayan B. Mandayam},
  journal= {arXiv preprint arXiv:2203.02892},
  year   = {2022}
}

Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance

Abstract

Keywords

Cite

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