Disseminating Authorized Content in Interest-Centric Opportunistic Social Networks

Kong, Chenguang; Cao, Xiaojun

doi:10.1109/icccn.2015.7288398

Cited by 5 publications

(8 citation statements)

References 18 publications

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“…This is simply saying that scheduling when to forward which object is a challenge in an efficient content dissemination strategy . The scheduler should find the best number of content copies to forward when a content‐holder meets a noncontent‐holder , based on the likelihood of a noncontent‐holder being able to deliver the content to the interested destinations …”

Section: Related Workmentioning

confidence: 99%

“…They also find destinations' interests by looking at their behaviors and visited locations. For example, in Moghadam and Schulzrinne, an interest‐aware algorithm learns destinations' interests from the history of their cached content by exchanging nodes' interest to calculate the peer's interest in the contents and in SCPD authors categorized the content in different topics that they had gathered from the contacted or connected nodes. In SPOON, to derive a node's interests, a document clustering technique is used to infer keywords from the node's stored contents.…”

Section: Related Workmentioning

confidence: 99%

“…In TAD, generation of advertisements is the duty of source nodes, referred to as advertisement agents . Advertisement agents create advertisements based on authorized content model . In this model, contents (advertisements) are generated only by content providers (advertisement agents) and are then sent to the targeted destinations, based on their interests, by relay nodes.…”

Section: Targeted Advertisement Distributionmentioning

confidence: 99%

“…Then, by exploiting the node dwelling probability at different communities, we will define TAD as an optimization problem. Advertisements will be generated by advertisement agents based on the authorized content model . In the authorized content model, the contents can only be generated by content providers , and the content copies delivered to a destination return some reward to content provider if it is adopted by the destination.…”

Section: Introductionmentioning

confidence: 99%

“…Advertisements will be generated by advertisement agents based on the authorized content model. 22 In the authorized content model, the contents can only be generated by content providers, and the content copies delivered to a destination return some reward to content provider if it is adopted by the destination. In TAD, the overall reward obtained by an advertisement agent depends on how much the destination is interested in the received advertisement.…”

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confidence: 99%

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Targeted content dissemination in mobile social networks taking account of resource limitation

Ravaei

Sabaei

Pedram

et al. 2017

Concurrency and Computation

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Targeted Advertisement Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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