Stabilized adaptive sampling control for reliable real-time learning-based surveillance systems

Kim, Dohyun; Park, Soo-Hyun; Kim, Joongheon; Bang, Jae Young; Jung, Soyi

doi:10.23919/jcn.2021.000009

Cited by 17 publications

(4 citation statements)

References 23 publications

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“…Furthermore, to effectively deploy AI-enabled system in an ITS environment for CV applications, a robust computing infrastructure is required for handling large amounts of image data, processing it in real-time, and performing complex computations [70][71][72]. Edge servers and cloud servers are two potential solutions that can meet these requirements [72].…”

Section: Case Studymentioning

confidence: 99%

Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities

Islam

Shin

2023

Electronics

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Internet of Things (IoT) ecosystem in smart cities demands fast, reliable, and efficient image data transmission to enable real-time Computer Vision (CV) applications. To fulfill these demands, an Orthogonal Frequency Division Multiplexing (OFDM)-based communication system has been widely utilized due to its higher spectral efficiency and data rate. When adapting such a system to achieve fast and reliable image transmission over fading channels, noise is introduced in the signal which heavily distorts the recovered image. This noise independently corrupts pixel values, however, certain intrinsic properties of the image, such as spatial information, may remain intact, which can be extracted as multidimensional features (in the convolution layers) and interpreted (in the top layers) by a Deep Learning (DL) model. Therefore, the current study analyzes the robustness of such DL models utilizing various OFDM-based image communication systems for CV applications in an Intelligent Transportation Systems (ITS) environment. Our analysis has shown that the EfficientNetV2-based model achieved a range of 70–90% accuracy across different OFDM-based image communication systems over the Rayleigh Fading channel. In addition, leveraging different data augmentation techniques further improves accuracy up to 18%.

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Section: Case Studymentioning

confidence: 99%

Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities

Islam

Shin

2023

Electronics

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“…(4) Then the surveillance drone collects all bids from the delivery drone and which are in transform form. (5) The drone with the highest allocation probability becomes a winner and pays final the determined payment and is detailed in Section IV-C.…”

Section: B Auction Design For Delivery Drone Scheduling 1) Auction-ba...mentioning

confidence: 99%

“…Especially in the ongoing pandemic situation, drones are used for timely vaccine distribution during novel coronavirus (COVID-19) and future pandemics [2]. Furthermore, drones also facilitate the inspection of hard-toaccess areas configuring situational awareness [3]- [5], which is important for many applications such as remote sensing, search and rescue, and disaster response. As presented in Fig.…”

Section: Introductionmentioning

confidence: 99%

Neural Myerson auction for truthful and energy-efficient autonomous aerial data delivery

et al. 2022

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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 this SPA-based operations, a deep learning based framework is additionally designed for delivery performance improvements.

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“…The requirement for flexible and dependable network supporting systems grew in response to the enormous demand for trustworthy surveillance services [1][2][3]. Unmanned aerial vehicles (UAVs) apart from their conventional roles [3][4][5][6][7][8][9], have recently gained popularity as core network devices for delivering flexible and dependable network services such as mobile surveillance.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

et al. 2022

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In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.

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Stabilized adaptive sampling control for reliable real-time learning-based surveillance systems

Cited by 17 publications

References 23 publications

Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities

Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities

Neural Myerson auction for truthful and energy-efficient autonomous aerial data delivery

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

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