Intelli-Eye: An UAV Tracking System with Optimized Machine Learning Tasks Offloading

Yang, Bo; Wu, Hsiang-Huang; Xiao, Cao; Li, Xiangfang; Kroecker, Timothy; Han, Zhu; Qian, Lijun

doi:10.1109/infocomwkshps47286.2019.9093759

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…While all the previous works show that exploiting a remote computation system can offer great results for UAVs, they do so for particular tasks and do not offer a framework for general tasks. The work in [16] tries to do just that for tasks that use neural networks, where a task distribution framework is proposed. An optimization problem is formulated to determine the optimal data offload ratio β, the fraction of times a task is offloaded to a remote server.…”

Section: State Of the Artmentioning

confidence: 99%

Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Correia

Bernardino

Ribeiro

2023

IEEE Internet Things J.

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Unmanned Aerial Vehicles (UAVs) are getting more and more uses in recent times. However, low-cost commercial UAVs may not possess enough computational power to run state of the art algorithms in order to perform certain tasks, negatively affecting performance. Remote computational systems, where heavy processing tasks can be offloaded emerge as a solution. However, they introduce latency, which can be undesirable for real-time tasks. Furthermore, if the task is simple, using a local algorithm with worse performance may be acceptable to avoid latency. As such, a method to decide which algorithm to use is of great importance. We consider the use case of computer vision tasks, in particular detection and tracking. In these tasks, image properties such as brightness, contrast, motion blur and clutter affect the algorithm performance. Our proposed methods use a combination of neural networks and kernel machines to estimate the performance of the algorithm given the input image. An appropriate cost function is then used to identify the best algorithm for the task given the input image, the task deadline, and the uncertainty in the variables of the algorithm, in particular computing time and error rate. Results show that our method matches or outperforms similar state of the art methods, complying with time restrictions while delivering increased performance.

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Section: State Of the Artmentioning

confidence: 99%

Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Correia

Bernardino

Ribeiro

2023

IEEE Internet Things J.

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“…The work in [241], [242] examined the case of per-layer distribution with one split point between one UAV and one MEC server (see Fig. 24).…”

Section: Fast Interventionmentioning

confidence: 99%

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Baccour

Mhaisen

Abdellatif

et al. 2022

IEEE Commun. Surv. Tutorials

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes of real-time data streams. Designing accurate models using such data streams, to revolutionize the decision-taking process, inaugurates pervasive computing as a worthy paradigm for a better quality-of-life (e.g., smart homes and self-driving cars.). The confluence of pervasive computing and artificial intelligence, namely Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy and latency requirements. In this context, an intelligent resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and reinforcement learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed training and inference across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

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“…The work in [201], [202] examined the case of per-layer distribution with one split point between one UAV and one MEC server (see Fig. 21).…”

Section: Fast Interventionmentioning

confidence: 99%

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

Baccour,

Mhaisen,

Abdellatif

et al. 2021

Preprint

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Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services, spanning from recommendation systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams, to predict future insights and revolutionize the decision-taking process, inaugurates pervasive systems as a worthy paradigm for a better quality-oflife. The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged among IoT devices (e.g., smartphones, smart vehicles) and infrastructure (e.g. edge nodes, and base stations) to avoid communication and computation overheads and ensure maximum performance. In this paper, we conduct a comprehensive survey of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems. Specifically, we first present an overview of the pervasive computing, its architecture, and its intersection with artificial intelligence. We then review the background, applications and performance metrics of AI, particularly Deep Learning (DL) and online learning, running in a ubiquitous system. Next, we provide a deep literature review of communication-efficient techniques, from both algorithmic and system perspectives, of distributed inference, training and online learning tasks across the combination of IoT devices, edge devices and cloud servers. Finally, we discuss our future vision and research challenges.

show abstract

Intelli-Eye: An UAV Tracking System with Optimized Machine Learning Tasks Offloading

Cited by 9 publications

References 10 publications

Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Learning Performance Models of Distributed Computer Vision Methods for Decision Making in Detection and Tracking Algorithms in UAVs

Pervasive AI for IoT Applications: A Survey on Resource-Efficient Distributed Artificial Intelligence

Pervasive AI for IoT Applications: Resource-efficient Distributed Artificial Intelligence

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