Optimizations and Economics of Crowdsourced Mobile Streaming

Tang, Ming; Gao, Lin; Pang, Haitian; Huang, Jianwei; Sun, Lifeng

doi:10.1109/mcom.2017.1600830

Cited by 23 publications

(16 citation statements)

References 11 publications

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“…Besides healthcare, other applications with different targets have been covered in the literature: video streaming by He et al [112] and Tang et al [113], UV radiation by Mei et al [114], website performance by Zhu et al [115], smart parking associations by Kim et al [116], and gaming by Li et al [117].…”

Section: ) Algorithms For Other Applicationsmentioning

confidence: 99%

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A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Mouradian

Naboulsi

Yangui

et al. 2018

IEEE Commun. Surv. Tutorials

840

405

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Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latencysensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.

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Section: ) Algorithms For Other Applicationsmentioning

confidence: 99%

“…In turn, Tang et al [113] target video streaming applications. They study cooperation among devices to enhance users QoE while streaming videos in cellular networks.…”

Section: Table VImentioning

confidence: 99%

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Mouradian

Naboulsi

Yangui

et al. 2018

IEEE Commun. Surv. Tutorials

840

405

View full text Add to dashboard Cite

show abstract

“…In particular, the requested video is transmitted to UE 1 without any transcoding, whereas the video is transcoded to appropriate qualities before it is streamed to UEs 2-4. Optimal allocation of edge-C3 resources to simultaneous video streaming tasks in real-time is challenging because specific allocation policies result in different performance trade-offs (e.g., QoE versus traffic or latency) [196,197] and economic models [198]. For instance, by allocating more computing resources, edge servers can transcode and send videos with different qualities to UEs rather than fetching them from the network backhaul, thereby reducing the network backhaul traffic.…”

Section: A Overviewmentioning

confidence: 99%

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Jedari

Premsankar

Illahi

et al. 2021

IEEE Commun. Surv. Tutorials

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“…The authors in [29,30] proposed strategies of computation offloading and load distribution. Fog computing has been applied to a variety of applications, including healthcare and medical applications, connected vehicles, smart cities, surveillance systems, and video streaming [31][32][33][34][35]. Previous research has shown that (i) fog computing reduces latency compared to traditional cloud computing [33] and (ii) reduces the volume of network traffic towards the cloud [25].…”

Section: Interaction With Cloudmentioning

confidence: 99%

Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

Lee

2020

Sustainability

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It has been found that a cloud building energy management system (BEMS) alone cannot support increasing numbers of end devices (e.g., energy equipment and IoT devices) and emerging energy services efficiently. To resolve these limitations, this paper proposes Fog BEMS, which applies an emerging fog computing concept to a BEMS. Fog computing places small computing resources (fog nodes) just next to end devices, and these nodes process data in real time and manage local contexts. In this way, the BEMS becomes distributed and scalable. However, existing fog computing models have barely considered scenarios where many end devices and fog nodes are deployed and interconnected. That is, they do not scale up and cannot be applied to scalable applications like BEMS. To solve the problem, this paper (i) designs a fog network where a list of functionally heterogeneous nodes is deployed in a hierarchy for collaboration and (ii) designs an agent-based, modular programming model that eases the development and management of computing services at a fog node. We develop a prototype of a fog node and build a real-world testbed on a campus to demonstrate the feasibility of the proposed system. We also conduct experiments, and results show that Fog BEMS is scalable enough for a node to connect up to 900 devices and that network traffic is reduced by 27.22–97.63%, with varying numbers of end devices.

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Optimizations and Economics of Crowdsourced Mobile Streaming

Cited by 23 publications

References 11 publications

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Video Caching, Analytics, and Delivery at the Wireless Edge: A Survey and Future Directions

Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

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