Details

Autor(en) / Beteiligte

• Zenggang, Xiong
• Mingyang, Zeng
• Xuemin, Zhang
• Sanyuan, Zhu
• Fang, Xu
• Xiaochao, Zhao
• Yunyun, Wu
• Xiang, Li

Titel

Social Similarity Routing Algorithm based on Socially Aware Networks in the Big Data Environment

Ist Teil von

• Journal of signal processing systems, 2022-11, Vol.94 (11), p.1253-1267

Ort / Verlag

New York: Springer US

Erscheinungsjahr

2022

Link zum Volltext

Quelle

SpringerLink

Beschreibungen/Notizen

• In the big data environment, the social information of a large number of nodes cannot be reasonably analyzed and utilized, thus leading to the problem of uneven routing performance. Therefore, this paper proposes a Social Similarity Routing Algorithm (SRRA) based on socially aware networks in the big data environment. In the SRRA algorithm, two main parts in which the process of nodes forwarding messages are in-community and out-of-community. First, we defined three indexes for nodes and communities in which nodes are located by analyzing human social behavior: community connectedness between communities, the activity of nodes, and the social similarity of nodes. Then these three indexes are used to make up two measures: the in-community forwarding measure and the out-of-community forwarding measure. When messages are forwarded within a community, we choose nodes with high in-community forwarding measures as relay nodes so that messages can be delivered quickly in the same community. The relay node with the highest out-of-community forwarding measure is chosen to forward the message to the adjacent communities that are as near as possible to the destination community as much as possible when messages are forwarded outside the community, which ensures that messages can always be sent to the target community fast and accurately. The results of the simulation experiments compared with existing routing algorithms prove that the SRRA routing algorithm significantly improves the message delivery ratio while effectively reducing the network overhead and average latency.