Targeted content dissemination in mobile social networks taking account of resource limitation. (11th July 2017)

• Record Type: Journal Article
• Authors: Ravaei, Bahman; Sabaei, Masoud; Pedram, Hossein; Valaee, Shahrokh
• Is Part Of: Concurrency and computation. Volume 29:Number 18(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-07-11
• Subjects: advertisement distribution -- content exchange application -- delay tolerant network -- mobile social network -- targeted content dissemination; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4207
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Mobile social networks are presenting new opportunities for content dissemination. User location, mobility, and social communities can be used to deliver delay‐tolerant content. Most existing dissemination methods either fail to consider the user's interest in their protocol design or just allow exchange of contents between nodes that have a similar interest. If nodes with similar interest never encounter each other, then contents might not be exchanged with them. In this paper, we propose a method in which user location, mobility, and interest as well as certain limiting factors such as relay buffer size and communication overhead are used to select a set of relays for targeted advertisement distribution. Besides, our method does not depend on nodes with similar interest encountering one another. In the proposed method, a distribution agent exploits user location, mobility, and social networks as well as the interest of destinations to select a group of relays to carry content to the targeted destinations. Users move among social communities and carry advertisements to their peers in other communities. We have also developed an optimization problem for advertisement selection and scheduling. Since the problem is NP‐hard, we propose a heuristic solution. Evaluation of the proposed approach shows that the algorithm is not sensitive to network resource variation and readily outperforms popular methods reported in the literature in terms of delay, delivery ratio, and interest compatibility.