The Design of Cloud-Based 4G/LTE for Mobile Augmented Reality with Smart Mobile Devices

Lin, Bor‐Shyh; Tsai, Wen‐Hsiang; Wu, Chinyu; Hsu, Pei-Hsien; Huang, Jung Y.; Liu, Tsai-Hwa

doi:10.1109/sose.2013.57

Cited by 24 publications

(10 citation statements)

References 9 publications

(5 reference statements)

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“…Servers were built in the cloud by using Amazon Web Services cloud platform. A similar study has conducted by Bao-Shuh P. Lin et al [14], in which, they used cloud-based 4G broadband mobile system (BMS) (TD-LTE) to develop mobile augmented reality (MAR) applications. They addressed a design of navigation/tourism applications for indoor and outdoor, collaborative urban design, and multiuser interactive motion learning system.…”

Section: Related Workmentioning

confidence: 94%

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Efficient framework for mobile walkthrough application

Fathy

Hassan

Sheta

et al. 2015

Pervasive and Mobile Computing

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Section: Related Workmentioning

confidence: 94%

“…• Pervasive mobile computing and cloud computing (light-weight applications) to overcome processing power limitation [13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Efficient framework for mobile walkthrough application

Fathy

Hassan

Sheta

et al. 2015

Pervasive and Mobile Computing

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“…Based on the platform, many applications were introduced. For example, research [11] investigated the roles of 5G in the development of cloud, big data, SDN, and IoT, and then it proposed an integrated architecture of these technologies for 5G; research [12] addressed the technology outlook of computing power and system characteristics of 5G Mobile broadband system; research [13] described and implemented cloud computing for various Mobile Augmented Reality (MAR) applications with smart mobile devices; research [14] showed the challenges of applying SDN/NFV to 5G and IoT. [15][16] investigated various ML algorithms to cluster, forecast, and manage handover behaviors and traffic behaviors of a huge number of cells based on analyzing a huge amount of collected data.…”

Section: Experimental Architecture Based On Big Data ML and Sdn/nfv mentioning

confidence: 99%

Applying Big Data, Machine Learning, and SDN/NFV for 5G Early-Stage Traffic Classification and Network QoS Control

Lin

2018

TNC

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Due to the rapid growth of mobile broadband and IoT applications, the early-stage mobile traffic classification becomes more important for traffic engineering to guarantee Quality of Service (QoS), implement resource management, and network security. Therefore, identifying traffic flows based on a few packets during the early state has attracted attention in both academic and industrial fields. However, a powerful and flexible platform to handle millions of traffic flows is still challenging. This study aims to demonstrate how to integrate various state-of-the-art machine learning (ML) algorithms, big data analytics platforms, software-defined networking (SDN), and network functions virtualization (NFV) to build a comprehensive framework for developing future 5G SON applications. This platform successfully collected, stored, analyzed, and identified a huge number of real-time traffic flows at broadband Mobile Lab (BML), National Chiao Tung University (NCTU). Moreover, we also implemented network QoS control to configure priorities per-flow traffic to enable bandwidth guarantees for each application by using SDN. Finally, the performance of the proposed models was evaluated by applying them to a real testbed environment. The powerful computing capacity of the platform was also analyzed.

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“…The integration of cloud computing, mobile clients (such as the smart mobile devices including the smartphones and tablets), servers and data center [4][5][6]9,19,23] and logistics management [24] could be applied for the precast supply chain management. As illustrated in Figure 1, the architectural system of cloud computing for the precast supply chain management is illustrated.…”

Section: Cloud Computing System Architecture Within the Precast Constmentioning

confidence: 99%

Cloud Computing Information System Architecture for Precast Supply Chain Management

Abedi

Rawai

Fathi

et al. 2015

AMM

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Abstract. The precast construction industry is associated with a lot of activities, vast effort, many parties and numerous processes. The precast supply chain phases include the planning, design, manufacturing, transportation, installation and construction. The parties within the precast supply chain phases should have efficient communication and access to precise and latest information contributing to the enhanced collaboration, sustainability and improving the integration. The aim of this study is to explore the collaboration tools with proposing the cloud system architecture for the precast supply chain management. The research findings are according to the comprehensive review of the literature on supply chain management, precast construction industry and cloud computing. Findings demonstrate the major problems within the precast supply chain phases comprised of poor planning, ineffective communications among designers and manufacturers, incompetent employees and damage to raw materials, large size and heavy precast components and the poor on-site coordination. These major problems within the precast supply chain phases could contribute to negative consequences on the efficiency, productivity and effectiveness of precast delivery. Therefore, to mitigate and overcome these major problems within the precast construction, the cloud computing implementation as the valuable alternative could be delivered to enhance the efficiencies and effectiveness of the collaboration systems. This research proposes and establishes the concepts of valuable collaborative tools, for instance the Cloud Computing Information Systems (CCIS). These tools will assist the processes, activities, information and network to improve integration with enhanced collaboration within the precast supply chain management through increasing the opportunities to attain sustainability with higher competitive advantages.

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The Design of Cloud-Based 4G/LTE for Mobile Augmented Reality with Smart Mobile Devices

Cited by 24 publications

References 9 publications

Efficient framework for mobile walkthrough application

Efficient framework for mobile walkthrough application

Applying Big Data, Machine Learning, and SDN/NFV for 5G Early-Stage Traffic Classification and Network QoS Control

Cloud Computing Information System Architecture for Precast Supply Chain Management

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