Distributed and Autonomous Aerial Data Collection in Smart City Surveillance Applications

TL;DR

This paper introduces a novel distributed algorithm for autonomous aerial data collection in smart city surveillance, utilizing a deep learning auction mechanism to optimize UAV data transfer under constraints like mobility and battery life.

Contribution

It proposes a new algorithm combining mobility-aware data collection with a Myerson auction-based deep learning approach for UAV data transfer optimization.

Findings

Achieves improved data collection efficiency in UAV networks.

Demonstrates the effectiveness of auction-based deep learning in UAV data transfer.

Outperforms traditional methods in simulation results.

Abstract

The massive growth of Smart City and Internet of Things applications enables safety and security. The data those are produced from surveillance cameras in aerial devices such as unmanned aerial networks (UAVs) are needed to be transferred to ground stations for secure data analysis. When the scale of network is relatively large compare to the wireless communication coverage of device, it is not always available to transmit the data to the ground stations, thus distributed and autonomous algorithms are essentially desired. Based on the needs, we propose a novel algorithm that is for collecting surveillance data under the consideration of mobility and flexibility of UAV networks. Due to the battery limitation in UAVs, we selectively collect data from the UAVs by setting rules under the consideration of distance and similarity. As a sequence, the UAV devices have to compete for a chance to get data processing. For this purpose, this paper designs a Myerson auction-based deep learning algorithm to leverage the UAV's revenue compare to traditional second-price auction while preserving truthfulness. Based on simulation results, we verify that our proposed algorithm achieves desired performance improvements.