Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach

Chen, Miaojiang; Liu, Wei; Wang, Tian; Liu, Anfeng; Zeng, Zhiwen

doi:10.1016/j.comnet.2021.108186

Cited by 33 publications

(17 citation statements)

References 39 publications

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“…Edge AI and mobile augmented reality—Mobile augmented reality applications have to process huge amounts of data that arrive from various devices such as a camera, OPS, and other video and audio data within the stipulated latency of around 15 to 20 milliseconds in order to show the augmented reality to the user [ 9 ]. The next generation IoT devices are equipped with reinforcement learning-based Al models implemented with the help of customized processors and 50 communication technologies.…”

Section: Motivating Use Cases Of Edge Aimentioning

confidence: 99%

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

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Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through ondevice data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

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Section: Motivating Use Cases Of Edge Aimentioning

confidence: 99%

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Surianarayanan

Lawrence

Raj

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…2) ENERGY HARVESTING If minimizing energy consumption is not sufficient or practical for prolonging the battery lifetime, energy harvesting approaches are used to recharge the battery. Sangoleye et al [99] identify the best base station to connect to for energy harvesting, whereas Chen et al [101] migrate computation tasks to nodes that are best positioned for harvesting. Elmagid et al [67] schedule packet transmissions in a way that is optimal for energy harvesting.…”

Section: Buildingmentioning

confidence: 99%

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

2022

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The transition to renewable production and smart grids is driving a massive investment to battery storages, and reinforcement learning (RL) has recently emerged as a potentially disruptive technology for their control and optimization of battery storage systems. A surge of papers has appeared in the last two years applying reinforcement learning to the optimization of battery storages in buildings, energy communities, energy harvesting Internet of Things networks, renewable generation, microgrids, electric vehicles and plug-in hybrid electric vehicles. This article reviews these applications through 4 different perspectives. Firstly, the type of optimization problem is analyzed; the literature can be divided to approaches that optimize either financial targets or energy efficiency. Secondly, the approaches for handling user comfort are analyzed for applications that may impact a human user. Thirdly, this paper discusses the approach to model and reduce battery degradation. Fourthly, the articles are categorized by application context and applications likely to attract a high amount of research are identified. The paper concludes with a list of unresolved challenges.

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“…Reinforcement learning (RL) is attracting attention as it is being applied as a new approach to various problems [2][3]. On the other hand, most real-world problems, including ones in multi-AGV warehouses, occur when many entities cooperate or compete with each other rather than a single entity does [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

MARL-Based Cooperative Multi-AGV Control in Warehouse Systems

et al. 2022

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Automated guided vehicles (AGVs) are essential components for the automation of fulfillment centers, a type of warehouse, where goods are stored on shelves and carried by AGVs. To increase the productivity in inventory management, a well-organized cooperative path control is required to transport goods to the designated picking stations. In this paper, we propose a QMIX-based scheme for the cooperative path control of multiple AGVs. Although QMIX is the one of popular cooperative multi-agent reinforcement learning algorithms, we find that QMIX alone was not enough to increase productivity in warehouse systems. So, we develop two novel techniques that can be used with QMIX: 1) sequential action masking that eliminates all the collision cases and 2) additional local loss that improves collaboration of individual AGVs. They help to encourage the AGVs to cooperate more for high productivity. By extensive simulations, we present the superiority of the proposed scheme on several layouts in fulfillment centers. The effect of cooperation among AGVs in the proposed scheme is verified through the comparison study with the existing algorithms. Additionally, we show the generalization performance by investigating the reusability of the model trained with the proposed scheme.

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Edge intelligence computing for mobile augmented reality with deep reinforcement learning approach

Cited by 33 publications

References 39 publications

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

A Survey on Optimization Techniques for Edge Artificial Intelligence (AI)

Exploiting Battery Storages With Reinforcement Learning: A Review for Energy Professionals

MARL-Based Cooperative Multi-AGV Control in Warehouse Systems

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