Offloading Multiple Mobile Data Contents Through Opportunistic Device-to-Device Communications

Cheng, Ray-Guang; Chen, Nien-Sheng; Chou, Yu-Feng; Bečvář, Zdeněk

doi:10.1007/s11277-015-2492-1

Cited by 23 publications

(8 citation statements)

References 12 publications

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“…Offloading over secondary networks has been studied in the following literature, which can be mainly divided into two categories: ISM offloading [29,34,51,53] and white space offloading [17,28,[54][55][56].…”

Section: Related Workmentioning

confidence: 99%

“…In our research, we take a content-centric approach to selecting distributors and do not rely on the social aspects of offloading. Cheng et al [53] take a content-centric approach to selecting distributors. The authors propose an algorithm to determine the number of distributors based on the popularity of the content and rate of contacts between devices.…”

Section: Related Workmentioning

confidence: 99%

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Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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The author of this dissertation contributed to the development of the heuristic algorithms, the implementation of the simulator, the evaluation through simulation, the analysis of results, and writing the article.Publication IV empirically evaluates the impact of edge computing on emerging interactive applications and services. At the time of writing, there was no clear consensus on what specifically constitutes the edge of the network in edge computing. Thus, we first survey the state of the art in edge computing architectures and determine different deployment options for edge devices. Next, we experimentally evaluate edge computing when available at different physical locations and with different virtualization technologies. Specifically, we characterize the end-to-end latency (including

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient placement of edge computing devices for vehicular applications in smart cities

Premsankar

Ghaddar

Francesco

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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show abstract

“…The proposed method was compared with the k-most anxious user (k-MAS) method and the push-and-track (PnT) method [24]. In the two contrastive methods, the edge server will directly push the data blocks to the users via the cellular network, if some users fail to receive the required data before the end of their respectively time window or the remaining time is insufficient to transmit the data via the cellular network.…”

Section: Algorithm 2 Seed Selectionmentioning

confidence: 99%

An Edge-Assisted Data Distribution Method for Vehicular Network Services

Wang

Zhang

et al. 2019

IEEE Access

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The current data distribution method of vehicular network cannot satisfy the strict spatiotemporal constraints on the transmission of massive service data. Neither can the 5th generation mobile network (5G) meet the massive data demand of vehicular network services. To solve the problems, this paper designs an edge-assisted service data distribution method for vehicular network services. Specifically, the service data distribution was predicted by time series analysis through edge computing, based on the storage capacity of base stations. Then, a spatiotemporal constrained data sharing algorithm was proposed, which sets up a distribution tree to evaluate the importance of each vehicle in data transmission, and heuristically chooses the most important vehicles as seeds to speed up the vehicle-to-vehicle data sharing. Finally, the simulation experiment verifies that our method can greatly reduce the load of 5G network without breaking the spatiotemporal constraints of data transmission in vehicular network. INDEX TERMS Vehicular network, data distribution, 5th generation mobile network (5G), edge computing, spatiotemporal constraint.

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“…Very lately, a new opportunistic network approach named Opportunistic device-to-device (D2D) [10,4] communication has caught the attention of the research community. In opportunistic D2D, different devices communicate with each other in an autonomous way without having any centralised infrastructure.…”

Section: Device-to-device Opportunistic Networkmentioning

confidence: 99%

Explore and wait: A composite routing-delivery scheme for relative profile-casting in opportunistic networks

Borrego

Sánchez-Carmona

Li³

et al. 2017

Computer Networks

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In the context of Opportunistic Networking (OppNet), designing routing and delivery protocols is currently an open and active line of research. In some OppNet scenarios, destination addresses are not always known by sending applications. Profile-cast models solve this problem by allowing message destinations to be users or groups of users defined by their profiles. These profiles provide very effective ways of characterizing nodes in terms of node's attributes such as their profession, interests or typical whereabouts, for example. However, there are strong limitations in OppNet Profile-casting. There is no current way of representing special profiles defined by relative delivery functions such as best, maximum or over-the-average: nodes belong to these relative profiles taking into account not only attributes from the very same node but also relative to others from the same profile. In this article, we introduce Relcast, a Profile-cast model that allows messages to be sent to profiles defined in terms of relative delivery functions. Additionally, we present Explore and Wait, a composite routing-delivery scheme that uses optimal stopping theory-based delivery strategies to route Relcast messages. We show, using simulations that this routing-delivery scheme performs better than traditional approaches that use state-of-the-art routing-delivery primitives.

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Offloading Multiple Mobile Data Contents Through Opportunistic Device-to-Device Communications

Cited by 23 publications

References 12 publications

Efficient placement of edge computing devices for vehicular applications in smart cities

Efficient placement of edge computing devices for vehicular applications in smart cities

An Edge-Assisted Data Distribution Method for Vehicular Network Services

Explore and wait: A composite routing-delivery scheme for relative profile-casting in opportunistic networks

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