Journal
IEEE TRANSACTIONS ON MOBILE COMPUTING
Volume 16, Issue 11, Pages 3084-3099Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TMC.2017.2685390
Keywords
Incentive; data dissemination; autonomous mobile social networks; two-person cooperative game; optimal stopping theory; secretary problem
Funding
- National Science Foundation [CNS-1528004]
- National Natural Science Foundation of China [61602038]
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This work focuses on the incorporation of incentive stimulations into data dissemination in autonomous mobile social networks with selfish nodes. The key challenge of enabling incentives is to effectively track the value of a message under such a unique network setting with intermittent connectivity and multiple interest data types. We propose two data dissemination models: the data pulling model where mobile users pull data from data providers, and the data pushing model where data providers generate personalized data and push them to the intended users. For data pulling, we present effective mechanisms to estimate the expected credit reward of a message that helps intermediate nodes to evaluate the potential reward of it. Nodal message communication is formulated as a two-person cooperative game, whose solution is found by a heuristic approach which achieves Pareto optimality. Under the data pushing model, virtual checks are introduced to eliminate the needs of accurate knowledge about whom and how many credits data providers should pay. The check buying process is formulated as an online auction model to further accelerate the circulation of credits. Extensive simulations carried out based on real-world traces show the proposed schemes achieve better performance than fully cooperative scheme, but significantly reduce communication cost.
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