Fog Computing for Augmented Reality: Trends, Challenges and Opportunities

Salman, Shaik Mohammed; Sitompul, Taufik Akbar; Papadopoulos, Alessandro Vittorio; Nolte, Thomas

doi:10.1109/icfc49376.2020.00017

Cited by 13 publications

(8 citation statements)

References 47 publications

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“…To ensure a comfortable visual experience with head-mounted displays (HMDs), ideally, frame rates should reach around 60 frames per second. However, edge-based approaches in low-resolution video transmission can lead to latencies exceeding 16.67 milliseconds [86].…”

Section: Open Research Challengesmentioning

confidence: 99%

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

Khan,

Jamil,

Piran

et al. 2024

Technologies

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Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.

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Section: Open Research Challengesmentioning

confidence: 99%

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

Khan,

Jamil,

Piran

et al. 2024

Technologies

View full text Add to dashboard Cite

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“…Another study provides a survey of recent studies on fog environmenst from the perspectives of argument reality applications [21], smart city applications [22], and healthcare applications [23]. However, these studies did not consider offloading in fog settings.…”

Section: Related Work and Motivationmentioning

confidence: 99%

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Abdulazeez

Askar

2023

IEEE Access

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Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloadingrelated issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybridbased algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths and drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.INDEX TERMS Fog computing, Internet of Things (IoT), offloading, reinforcement learning, deep reinforcement learning.

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“…Numerous edge and fog computing architectures and offloading strategies in fog environments are investigated in [42]. Fog computing systems for augmented reality applications are studied in [43]. Authors have investigated several multilayer edge and fog computing architectures, energy optimization, and latency optimization techniques, and offloading approaches.…”

Section: Middleware Platforms For Distributed Computing Systemsmentioning

confidence: 99%

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Shah

2021

Sensors

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Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

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Fog Computing for Augmented Reality: Trends, Challenges and Opportunities

Cited by 13 publications

References 47 publications

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education

Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

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