Deep Learning at the Mobile Edge: Opportunities for 5G Networks

McClellan, Miranda; Cervelló-Pastor, Cristina; Sallent, S.

doi:10.3390/app10144735

Cited by 73 publications

(38 citation statements)

References 88 publications

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“… [ 125 ] Mobile Edge Computing Slicing, Caching, Mobility, offloading. [ 126 , 127 ] Cross-Layer Distributed Resource Allocation in Cognitive Radio Networks - [ 112 ] Interference Alignment and Caching Improve network sum-rate and energy-efficiency. [ 128 ] Mobile Social Network Optimization Improve reliability, optimal resource sharing, reduce latency.…”

Section: Enabling Technologies and Challenges For 6gmentioning

confidence: 99%

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Imoize

Adedeji

Tandiya

et al. 2021

Sensors

176

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The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

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Section: Enabling Technologies and Challenges For 6gmentioning

confidence: 99%

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Imoize

Adedeji

Tandiya

et al. 2021

Sensors

176

View full text Add to dashboard Cite

show abstract

“…In past years, the cloud-computing environment [ 26 ] has provided the best computing capabilities to mobile users. The cloud-based computing environment results in insufficient delays to mobile users due to relatively long distances.…”

Section: Architectural Comparisonsmentioning

confidence: 99%

“…Similarly, it is a very important concern to recognize DFD using DL architectures on mid-range smartphone class hardware and the memory requirements if they were implemented on mobile hardware instead of central cloud servers (CCS) [ 25 ]. Moreover, the authors used the fastest 5G [ 26 ] networks to bring power to MEC technology for mobile users to process the real-time demands of applications. Although, there is a dire need to discuss the DL architectures on MEC technology by using 5G networks in terms of adaptive resource allocation, mobility modeling, security, and energy efficiency.…”

Section: Architectural Comparisonsmentioning

confidence: 99%

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Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

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“…Therefore, a migration decision has a pivotal role since mobile user services have to migrate among multiple edge servers. To cope with the problem of migrating services to the irrelevant edge server, artificial intelligence (AI) based decision has been recently introduced [159]. AI is a pioneering solution for various key parameters such as service migration, edge server selection, user mobility, and cloudlet likelihood connection.…”

Section: ) Ai-based Mec Systemsmentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

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The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

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Deep Learning at the Mobile Edge: Opportunities for 5G Networks

Cited by 73 publications

References 88 publications

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

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