Social-aware dynamic router node placement in wireless mesh networks
详细信息 查看全文
- 作者：Chun-Cheng Lin ; Pei-Tsung Tseng ; Ting-Yu Wu ; Der-Jiunn Deng
- 刊名：Wireless Networks
- 出版年：2016
- 出版时间：May 2016
- 年：2016
- 卷：22
- 期：4
- 页码：1235-1250
- 全文大小：2,312 KB
- 刊物类别：Computer Science
- 刊物主题：Computer Communication Networks
Electronic and Computer Engineering
Business Information Systems - 出版者：Springer Netherlands
- ISSN：1572-8196
- 卷排序：22
文摘
The problem of dynamic router node placement (dynRNP) in wireless mesh networks (WMNs) is concerned with determining a dynamic geographical placement of mesh routers to serve mobile mesh clients at different times, so that both network connectivity (i.e., the greatest topology subgraph component size) and client coverage (i.e., the number of the served mesh clients) are maximized. Mesh clients are wireless devises associated with users, and in real world, the users with same interests or some social relationship have higher chance to gather and move together geographically, i.e., they form a community, and the WMN with multiple communities can be regarded as a social network. Therefore, this paper investigates the so-called social-aware WMN-dynRNP problem assuming that mesh routers should be aware of the social community structure of mesh clients to dynamically adjust their placement to improve network performance. To cope with this problem, this paper proposes a social-based particle swarm optimization approach, which additionally includes a social-supporting vector to direct low-loading mesh routers to support the heavy-loading mesh routers in the same topology subgraph component (community), so as to dynamically adopt to the social community behavior of mesh clients. As compared with the previous approach, our experimental results show that the proposed approach is capable of effectively reducing number of the unserved mesh clients and increasing network connectivity in dynamic social scenarios.KeywordsSocial networkWireless mesh networkRouter node placementCommunity movementParticle swarm optimization