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Community recombination and duplication node traverse algorithm in opportunistic social networks

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Abstract

In social communication, mobile devices can be regarded as socialization nodes in opportunistic social networks. Furthermore, they can carry and store useful information. Mobile devices can select destination nodes and deliver messages through opportunistic social networks because messages can be securely and conveniently stored, carried, and transmitted with nodes. However, many communities in traditional methods, may deliver messages often depending on one or two nodes, such as messages exchange by two nodes in Epidemic algorithm, or messages are sprayed with node in Spray and Wait algorithm. If those nodes are not enough cache and over-flooding, data transmission in communities may wait for a long time. In this study, communities are recombined and then duplication nodes are filtered in our method. According to evaluate contribution by node and restructure community, deliver ratio and overhead can be improved in opportunistic networks. With satisfactory results from simulation and comparison with some existing algorithms, the average delivery ratio of new research method is 0.91, which is 30% higher than that of Epidemic algorithm; the end-to-end delay reduces 75% with Spray and wait algorithm.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This work was supported in The National Natural Science Foundation of China (61672540); Hunan Provincial Natural Science Foundation of China (2018JJ3299, 2018JJ3682).

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Jia WU and Ming ZHAO designed the project and drafted the manuscript, collected the data, wrote the code and performed the analysis. All participated in finalizing and approved the manuscript.

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Correspondence to Jia Wu.

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The authors declare that they have no competing interests (All financial).

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Wu, J., Chen, Z. & Zhao, M. Community recombination and duplication node traverse algorithm in opportunistic social networks. Peer-to-Peer Netw. Appl. 13, 940–947 (2020). https://doi.org/10.1007/s12083-019-00833-0

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