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GoSharing: An intelligent incentive framework based on users’ association for cooperative content sharing in mobile edge networks

Lookup NU author(s): Dr Zhenyu Wen, Professor Raj Ranjan

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Abstract

© 2019 Elsevier B.V. In most metropolises, commuters spend a considerable amount of time on public transport, and many of them entertain themselves with the content (like music or videos) on their mobile devices to alleviate boredom. Currently, the content, usually shared in co-located wireless networks to avoid huge monetary cost of using cellular data, is delivered from single host (resource owner) to single request user, which brings low transmission quality, due to the uncertainty of mobile edge networks in public transport environments. In this paper, we present an intelligent incentive framework called GoSharing which encourages multiple hosts to share content collaboratively to improve delivery quality, by taking advantage of users’ association and consideration of network Quality of Service(QoS) requirements. The highlight of GoSharing is the novel Association-based Intelligent incentive mechanism that consists of three key components. First, a Fast Candidate Generation algorithm discovers users’ association according to their stored content and QoS requirements and filters the candidate groups from large host groups. Second, a Host Selection algorithm finds a near-optimal solution among candidate groups within an approximate factor of F(d), where d denotes the maximum size of completed tasks when any candidate group is selected. Last but not least, a Payment Determination algorithm determines the payment of resource contributors while guaranteeing the truthfulness of their bids based on the procurement auction. Both theoretical analysis and extensive simulations demonstrate that GoSharing not only effectively motivates hosts’ collaborative sharing, but also achieves the properties of truthfulness, individual rationality, high computational efficiency, low overpayment ratio, and high download ratio.


Publication metadata

Author(s): Luo S, Wen Z, Zhang X, Xu W, Zomaya AY, Ranjan R

Publication type: Article

Publication status: Published

Journal: Future Generation Computer Systems

Year: 2019

Volume: 95

Pages: 601-614

Online publication date: 22/01/2019

Acceptance date: 09/02/2019

ISSN (print): 0167-739X

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.future.2019.01.013

DOI: 10.1016/j.future.2019.01.013


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