Effective path prediction and data transmission in opportunistic social networks
Abstract With the development of social media, social networks have become an important platform for people to share and communicate. In social network communication, when people carry mobile devices for data transmission, they need to find a definite transmission destination to ensure the normal co...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-10-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12254 |
| _version_ | 1818312065582891008 |
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| author | Jia Wu Wenhao Zou Huiyun long |
| author_facet | Jia Wu Wenhao Zou Huiyun long |
| author_sort | Jia Wu |
| collection | DOAJ |
| description | Abstract With the development of social media, social networks have become an important platform for people to share and communicate. In social network communication, when people carry mobile devices for data transmission, they need to find a definite transmission destination to ensure the normal conduct of information exchange activities. This is manifested in the process of data transmission by nodes, which requires analysis and judgment of surrounding areas, and finds suitable nodes for effective data classification and transmission. However, the node cache space in social networks is limited, and the process of waiting for the target node will cause end‐to‐end transmission delay. In order to improve such a transmission environment, this paper proposes a node trajectory prediction method named EDPPM algorithm. The EDPPM algorithm guarantees that nodes with high probability are given priority to obtain data information, which realized an effective data transmission mechanism. Through experiments and comparison of opportunistic transmission algorithms in social networks, such as Epidemic algorithm, Spray and Wait algorithm, and PRoPHET algorithm, the proposed scheme outperforms ProPhet by 47% in terms of cache utilization of nodes, 55% in terms of data transmission delay, and 32% in terms of network efficiency; compared with Epidemic by 53% in terms of cache utilization of nodes, 62% in terms of data transmission delay, and 47% in terms of network efficiency; same thing with regard to the Spray and wait by 27% in terms of cache utilization of nodes, 31% in terms of data transmission delay, and 17% in terms of network efficiency. |
| first_indexed | 2024-12-13T08:11:56Z |
| format | Article |
| id | doaj.art-c95859e3429f4cb6b2d47680b375d4f2 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-12-13T08:11:56Z |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-c95859e3429f4cb6b2d47680b375d4f22022-12-21T23:54:12ZengWileyIET Communications1751-86281751-86362021-10-0115172202221110.1049/cmu2.12254Effective path prediction and data transmission in opportunistic social networksJia Wu0Wenhao Zou1Huiyun long2School of Computer Science and Engineering Central South University Chang sha 410083 ChinaSchool of Computer Science and Engineering Central South University Chang sha 410083 ChinaSchool of Computer Science Guizhou University Guiyang 550002 ChinaAbstract With the development of social media, social networks have become an important platform for people to share and communicate. In social network communication, when people carry mobile devices for data transmission, they need to find a definite transmission destination to ensure the normal conduct of information exchange activities. This is manifested in the process of data transmission by nodes, which requires analysis and judgment of surrounding areas, and finds suitable nodes for effective data classification and transmission. However, the node cache space in social networks is limited, and the process of waiting for the target node will cause end‐to‐end transmission delay. In order to improve such a transmission environment, this paper proposes a node trajectory prediction method named EDPPM algorithm. The EDPPM algorithm guarantees that nodes with high probability are given priority to obtain data information, which realized an effective data transmission mechanism. Through experiments and comparison of opportunistic transmission algorithms in social networks, such as Epidemic algorithm, Spray and Wait algorithm, and PRoPHET algorithm, the proposed scheme outperforms ProPhet by 47% in terms of cache utilization of nodes, 55% in terms of data transmission delay, and 32% in terms of network efficiency; compared with Epidemic by 53% in terms of cache utilization of nodes, 62% in terms of data transmission delay, and 47% in terms of network efficiency; same thing with regard to the Spray and wait by 27% in terms of cache utilization of nodes, 31% in terms of data transmission delay, and 17% in terms of network efficiency.https://doi.org/10.1049/cmu2.12254Other topics in statisticsMobile radio systemsOther topics in statisticsFile organisationInformation networksMobile, ubiquitous and pervasive computing |
| spellingShingle | Jia Wu Wenhao Zou Huiyun long Effective path prediction and data transmission in opportunistic social networks IET Communications Other topics in statistics Mobile radio systems Other topics in statistics File organisation Information networks Mobile, ubiquitous and pervasive computing |
| title | Effective path prediction and data transmission in opportunistic social networks |
| title_full | Effective path prediction and data transmission in opportunistic social networks |
| title_fullStr | Effective path prediction and data transmission in opportunistic social networks |
| title_full_unstemmed | Effective path prediction and data transmission in opportunistic social networks |
| title_short | Effective path prediction and data transmission in opportunistic social networks |
| title_sort | effective path prediction and data transmission in opportunistic social networks |
| topic | Other topics in statistics Mobile radio systems Other topics in statistics File organisation Information networks Mobile, ubiquitous and pervasive computing |
| url | https://doi.org/10.1049/cmu2.12254 |
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