Improving Traffic Load Distribution Fairness in Mobile Social Networks
Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the be...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/15/7/222 |
| _version_ | 1797433936878501888 |
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| author | Bambang Soelistijanto Vittalis Ayu |
| author_facet | Bambang Soelistijanto Vittalis Ayu |
| author_sort | Bambang Soelistijanto |
| collection | DOAJ |
| description | Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance. |
| first_indexed | 2024-03-09T10:24:00Z |
| format | Article |
| id | doaj.art-4664294a6b0f44d8a5b93a9b79938ba1 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-09T10:24:00Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-4664294a6b0f44d8a5b93a9b79938ba12023-12-01T21:46:40ZengMDPI AGAlgorithms1999-48932022-06-0115722210.3390/a15070222Improving Traffic Load Distribution Fairness in Mobile Social NetworksBambang Soelistijanto0Vittalis Ayu1Department of Informatics, Sanata Dharma University, Yogyakarta 55281, IndonesiaDepartment of Informatics, Sanata Dharma University, Yogyakarta 55281, IndonesiaMobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node’s sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node’s periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.https://www.mdpi.com/1999-4893/15/7/222fair traffic distributionhuman mobilitynode popularitymobile social networks |
| spellingShingle | Bambang Soelistijanto Vittalis Ayu Improving Traffic Load Distribution Fairness in Mobile Social Networks Algorithms fair traffic distribution human mobility node popularity mobile social networks |
| title | Improving Traffic Load Distribution Fairness in Mobile Social Networks |
| title_full | Improving Traffic Load Distribution Fairness in Mobile Social Networks |
| title_fullStr | Improving Traffic Load Distribution Fairness in Mobile Social Networks |
| title_full_unstemmed | Improving Traffic Load Distribution Fairness in Mobile Social Networks |
| title_short | Improving Traffic Load Distribution Fairness in Mobile Social Networks |
| title_sort | improving traffic load distribution fairness in mobile social networks |
| topic | fair traffic distribution human mobility node popularity mobile social networks |
| url | https://www.mdpi.com/1999-4893/15/7/222 |
| work_keys_str_mv | AT bambangsoelistijanto improvingtrafficloaddistributionfairnessinmobilesocialnetworks AT vittalisayu improvingtrafficloaddistributionfairnessinmobilesocialnetworks |