Improved Resource Allocation Algorithms for Practical Image Encoding in a Ubiquitous Computing Environment

Dong, Mianxiong; Zheng, Long; Ota, Kaoru; Guo, Song; Guo, Minyi; Li, Li

doi:10.4304/jcp.4.9.873-880

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…However, compared to the traditional cloud, computational resources such as the CPU cycle and memory, are limited. To address this situation, some research [ 19 , 20 ] has focused on how to perform optimal resource allocations on resource-constrained mobile devices, and conducted MEC in various scenarios [ 21 , 22 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Zhang

Cao

et al. 2018

Sensors

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By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices.

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Section: Related Workmentioning

confidence: 99%

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Zhang

Cao

et al. 2018

Sensors

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“…We observe that some HTTP flows in IPv4 obtain a higher throughput than other types of flows. Digging into the flows that obtain a higher throughput, we identified the reason to be web caches inside the network. This explains what we found from Figure .…”

Section: Flow Propertiesmentioning

confidence: 99%

Insights into the issue in IPv6 adoption: A view from the Chinese IPv6 Application mix

Han

Xie

et al. 2014

Concurrency and Computation

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Abstract-Although IPv6 has been standardized more than 15 years ago, its deployment is still very limited. China has been strongly pushing IPv6, especially due to its limited IPv4 address space. In this paper, we describe measurements from a large Chinese academic network, serving a significant population of IPv6 hosts. We show that despite its expected strength, China is struggling as much as the western world to increase the share of IPv6 traffic. To understand the reasons behind this, we examine the IPv6 applicative ecosystem. We observe a significant IPv6 traffic growth over the past 3 years, with P2P file transfers responsible for more than 80% of the IPv6 traffic, compared to only 15% for IPv4 traffic. Checking the top websites for IPv6 explains the dominance of P2P, with popular P2P trackers appearing systematically among the top visited sites, followed by Chinese popular services (e.g., Tencent), as well as surprisingly popular third-party analytics including Google. Finally, we compare the throughput of IPv6 and IPv4 flows. We find that a larger share of IPv4 flows get a high-throughput compared to IPv6 flows, despite IPv6 traffic not being rate limited. We explain this through the limited amount of HTTP traffic in IPv6 and the presence of Web caches in IPv4. Our findings highlight the main issue in IPv6 adoption, i.e., the lack of commercial content, which biases the geographic pattern and flow throughput of IPv6 traffic.

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A Scalable Multiprocessor Architecture for Pervasive Computing

Zheng

Zhou

et al. 2011

Advances in Grid and Pervasive Computing

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Improved Resource Allocation Algorithms for Practical Image Encoding in a Ubiquitous Computing Environment

Cited by 5 publications

References 10 publications

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

Insights into the issue in IPv6 adoption: A view from the Chinese IPv6 Application mix

A Scalable Multiprocessor Architecture for Pervasive Computing

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