Neural Myerson Auction for Truthful and Energy-Efficient Autonomous
Aerial Data Delivery
- URL: http://arxiv.org/abs/2201.01170v1
- Date: Wed, 29 Dec 2021 12:14:34 GMT
- Title: Neural Myerson Auction for Truthful and Energy-Efficient Autonomous
Aerial Data Delivery
- Authors: Haemin Lee, Sean Kwon, Soyi Jung, and Joongheon Kim
- Abstract summary: We introduce a data delivery drone to transfer collected surveillance data in harsh communication conditions.
This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform.
- Score: 9.986880167690364
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A successful deployment of drones provides an ideal solution for surveillance
systems. Using drones for surveillance can provide access to areas that may be
difficult or impossible to reach by humans or in-land vehicles gathering images
or video recordings of a specific target in their coverage. Therefore, we
introduces a data delivery drone to transfer collected surveillance data in
harsh communication conditions. This paper proposes a Myerson auction-based
asynchronous data delivery in an aerial distributed data platform in
surveillance systems taking battery limitation and long flight constraints into
account. In this paper, multiple delivery drones compete to offer data transfer
to a single fixed-location surveillance drone. Our proposed Myerson
auction-based algorithm, which uses the truthful second-price auction (SPA) as
a baseline, is to maximize the seller's revenue while meeting several desirable
properties, i.e., individual rationality and incentive compatibility while
pursuing truthful operations. On top of these SPA-based operations, a deep
learning-based framework is additionally designed for delivery performance
improvements.
Related papers
- Drone Detection and Tracking with YOLO and a Rule-based Method [0.0]
An increased volume of drone activity in public spaces requires regulatory actions for purposes of privacy protection and safety.
detection tasks are usually automated and performed by deep learning models which are trained on annotated image datasets.
This paper builds on a previous work and extends an already published open source dataset.
Since the detection models are based on a single image input, a simple cross-correlation based tracker is used to reduce detection drops and improve tracking performance in videos.
arXiv Detail & Related papers (2025-02-07T19:53:10Z) - Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning [8.775925011558995]
This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities.
A holistic system that can reliably detect, track, and neutralize rogue drones is proposed.
arXiv Detail & Related papers (2025-01-07T16:22:51Z) - Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data [61.9426776237409]
Drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks.
A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn-temporal correlations.
arXiv Detail & Related papers (2025-01-07T03:23:28Z) - A Cross-Scene Benchmark for Open-World Drone Active Tracking [54.235808061746525]
Drone Visual Active Tracking aims to autonomously follow a target object by controlling the motion system based on visual observations.
We propose a unified cross-scene cross-domain benchmark for open-world drone active tracking called DAT.
We also propose a reinforcement learning-based drone tracking method called R-VAT.
arXiv Detail & Related papers (2024-12-01T09:37:46Z) - Obfuscated Location Disclosure for Remote ID Enabled Drones [57.66235862432006]
We propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID)
Instead of disclosing the actual drone's location, drones equipped with OLO-RID disclose a differentially private obfuscated location in a mobile scenario.
OLO-RID also extends RID messages with encrypted location information, accessible only by authorized entities.
arXiv Detail & Related papers (2024-07-19T12:35:49Z) - VBSF-TLD: Validation-Based Approach for Soft Computing-Inspired Transfer
Learning in Drone Detection [0.0]
This paper presents a transfer-based drone detection scheme, which forms an integral part of a computer vision-based module.
By harnessing the knowledge of pre-trained models from a related domain, transfer learning enables improved results even with limited training data.
Notably, the scheme's effectiveness is highlighted by its IOU-based validation results.
arXiv Detail & Related papers (2023-06-11T22:30:23Z) - Millimeter Wave Drones with Cameras: Computer Vision Aided Wireless Beam
Prediction [8.919072533905517]
Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications.
These drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget.
This paper proposes a vision-aided machine learning-based approach that leverages visual data collected from cameras installed on the drones.
arXiv Detail & Related papers (2022-11-14T17:42:16Z) - TransVisDrone: Spatio-Temporal Transformer for Vision-based
Drone-to-Drone Detection in Aerial Videos [57.92385818430939]
Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones.
Existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge devices.
We propose a simple yet effective framework, itTransVisDrone, that provides an end-to-end solution with higher computational efficiency.
arXiv Detail & Related papers (2022-10-16T03:05:13Z) - Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum
Learning Approach [1.094245191265935]
We present KUL-UAVSAFE, a first-of-its-kind dataset for the study of safety-critical people detection by drones.
We propose a CNN architecture with cross-fusion highways and introduce a curriculum learning strategy for multi-modal data.
arXiv Detail & Related papers (2021-09-28T12:34:13Z) - Dogfight: Detecting Drones from Drones Videos [58.158988162743825]
This paper attempts to address the problem of drones detection from other flying drones variations.
The erratic movement of the source and target drones, small size, arbitrary shape, large intensity, and occlusion make this problem quite challenging.
To handle this, instead of using region-proposal based methods, we propose to use a two-stage segmentation-based approach.
arXiv Detail & Related papers (2021-03-31T17:43:31Z) - Detection and Tracking Meet Drones Challenge [131.31749447313197]
This paper presents a review of object detection and tracking datasets and benchmarks, and discusses the challenges of collecting large-scale drone-based object detection and tracking datasets with manual annotations.
We describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South.
We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions.
arXiv Detail & Related papers (2020-01-16T00:11:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.