A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network
The amount of data has skyrocketed in Fifth-generation (5G) networks. How to select an appropriate node to transmit information is important when we analyze complex data in 5G communication. We could sophisticate decision-making methods for more convenient data transmission, and opportunistic comple...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2018-11-01
|
| Series: | Symmetry |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-8994/10/11/600 |
| _version_ | 1818039039186436096 |
|---|---|
| author | Genghua Yu Zhigang Chen Jia Wu Jian Wu |
| author_facet | Genghua Yu Zhigang Chen Jia Wu Jian Wu |
| author_sort | Genghua Yu |
| collection | DOAJ |
| description | The amount of data has skyrocketed in Fifth-generation (5G) networks. How to select an appropriate node to transmit information is important when we analyze complex data in 5G communication. We could sophisticate decision-making methods for more convenient data transmission, and opportunistic complex social networks play an increasingly important role. Users can adopt it for information sharing and data transmission. However, the encountering of nodes in mobile opportunistic network is random. The latest probabilistic routing method may not consider the social and cooperative nature of nodes, and could not be well applied to the large data transmission problem of social networks. Thus, we quantify the social and cooperative relationships symmetrically between the mobile devices themselves and the nodes, and then propose a routing algorithm based on an improved probability model to predict the probability of encounters between nodes (PEBN). Since our algorithm comprehensively considers the social relationship and cooperation relationship between nodes, the prediction result of the target node can also be given without encountering information. The neighbor nodes with higher probability are filtered by the prediction result. In the experiment, we set the node’s selfishness randomly. The simulation results show that compared with other state-of-art transmission models, our algorithm has significantly improved the message delivery rate, hop count, and overhead. |
| first_indexed | 2024-12-10T07:52:17Z |
| format | Article |
| id | doaj.art-e7832c5face84ee1a730b50a81f69040 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-12-10T07:52:17Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-e7832c5face84ee1a730b50a81f690402022-12-22T01:57:00ZengMDPI AGSymmetry2073-89942018-11-01101160010.3390/sym10110600sym10110600A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social NetworkGenghua Yu0Zhigang Chen1Jia Wu2Jian Wu3School of Information Science and Engineering, Central South University, Changsha 410075, ChinaSchool of Software, Central South University, Changsha 410075, ChinaSchool of Software, Central South University, Changsha 410075, ChinaSchool of Information Science and Engineering, Central South University, Changsha 410075, ChinaThe amount of data has skyrocketed in Fifth-generation (5G) networks. How to select an appropriate node to transmit information is important when we analyze complex data in 5G communication. We could sophisticate decision-making methods for more convenient data transmission, and opportunistic complex social networks play an increasingly important role. Users can adopt it for information sharing and data transmission. However, the encountering of nodes in mobile opportunistic network is random. The latest probabilistic routing method may not consider the social and cooperative nature of nodes, and could not be well applied to the large data transmission problem of social networks. Thus, we quantify the social and cooperative relationships symmetrically between the mobile devices themselves and the nodes, and then propose a routing algorithm based on an improved probability model to predict the probability of encounters between nodes (PEBN). Since our algorithm comprehensively considers the social relationship and cooperation relationship between nodes, the prediction result of the target node can also be given without encountering information. The neighbor nodes with higher probability are filtered by the prediction result. In the experiment, we set the node’s selfishness randomly. The simulation results show that compared with other state-of-art transmission models, our algorithm has significantly improved the message delivery rate, hop count, and overhead.https://www.mdpi.com/2073-8994/10/11/600Opportunistic complex social networkcooperativeneighbor nodeprobability modelsocial relationship |
| spellingShingle | Genghua Yu Zhigang Chen Jia Wu Jian Wu A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network Symmetry Opportunistic complex social network cooperative neighbor node probability model social relationship |
| title | A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network |
| title_full | A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network |
| title_fullStr | A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network |
| title_full_unstemmed | A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network |
| title_short | A Transmission Prediction Neighbor Mechanism Based on a Mixed Probability Model in an Opportunistic Complex Social Network |
| title_sort | transmission prediction neighbor mechanism based on a mixed probability model in an opportunistic complex social network |
| topic | Opportunistic complex social network cooperative neighbor node probability model social relationship |
| url | https://www.mdpi.com/2073-8994/10/11/600 |
| work_keys_str_mv | AT genghuayu atransmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT zhigangchen atransmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT jiawu atransmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT jianwu atransmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT genghuayu transmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT zhigangchen transmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT jiawu transmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork AT jianwu transmissionpredictionneighbormechanismbasedonamixedprobabilitymodelinanopportunisticcomplexsocialnetwork |