Quality-aware traffic offloading in wireless networks

Hu, Wenjie

doi:10.1145/2632951.2632954

Cited by 49 publications

(28 citation statements)

References 24 publications

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“…Most existing researches on collaborative task offloading mainly focus on the delay tolerant networking (DTN) applications and the homogenous tasks utilizing the same type of resource (see [5], [6] and references therein). For example, Shi et.…”

Section: A Related Workmentioning

confidence: 99%

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Chen

Gao

et al. 2017

IEEE Wireless Commun.

220

123

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In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints.We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.

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Section: A Related Workmentioning

confidence: 99%

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Chen

Gao

et al. 2017

IEEE Wireless Commun.

220

123

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“…For the first categories, users or experimenters report their semantically annotated data about the locations, phone usages and network performance by installing some Apps in their devices [19,10]. The limitation of this approach comes from the limited number of users sampled, which cannot stand for the global characteristics of a large scale cellular network.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, it is also used for recovering the human mobility trajectory [9], inferring demographics [6], and uncovering urban ecology [2]. Another type of data is the device-level metric obtained from mobile devices, such as device and application usage [8,21], network access bandwidth [10], energy computation [11], personal GPS locations [28], etc. With the popularity of 3G and LTE access, mobile and application data traces become available as well.…”

Section: Related Workmentioning

confidence: 99%

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

202

105

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Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis. Categories and Subject Descriptors Modeling techniquesPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. IMC '15, October 28-30, 2015, Tokyo, Japan.

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“…Among them, a manifold LSTM variant was proposed by Gers and Schmidhuber, which added the peephole connection. 41 The peephole connection makes the network node also accepts input of the node state. Gated recurrent unit (GRU) is a more obvious structural change variant.…”

Section: Figurementioning

confidence: 99%

Big data‐driven machine learning‐enabled traffic flow prediction

Kong

Jiang

et al. 2018

Trans Emerging Tel Tech

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Real‐time effective traffic flow big data prediction network has important application significance. Over the past few years, traffic flow data have been exploding and we have entered the big data era. The key challenge of traffic flow prediction network is how to construct an adaptive model relying on historical data. Existing big data‐driven traffic flow prediction networking approaches mainly use shallow learning, and there are unsatisfying for many realistic applications, which inspire us to rethink the traffic flow big data prediction problem with deep learning. In this paper, we propose a novel prediction approach based on machine learning. In addition to the minimum prediction error as the goal, we present the long short‐term memory model, which is a typical machine learning algorithm with deep learning network. This method is applied into the real‐world traffic big data from performance measurement system. Experimental results show that the proposed machine learning algorithm has more applicability and higher performance, compared with shallow machine learning prediction network.

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Quality-aware traffic offloading in wireless networks

Cited by 49 publications

References 24 publications

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Big data‐driven machine learning‐enabled traffic flow prediction

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