.png){kind=icon}
.png){kind=icon}
Return
DATM: a dynamic attribute trust model for efficient collaborative routing
.png){kind=icon}
PRE
AI
DOI：10.1007/s10479-018-2864-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the context of Intermittently Connected Mobile Networks, researchers have proposed different collaborative routing schemes with fundamental objectives to maximize packets delivery, and minimise latency through improving peers' routing decisions. One promising approach adopted by researchers to improve peers' routing decisions is a trust-based routing mechanism. Most current trust-based protocols in the literature enable peers to make routing decisions based on trust relationships that manifest among peers but with less concern about the peers' attributes. This further made the design of efficient, collaborative routing protocols a challenging task due to the high dynamics of peers' characteristics and mobility. In this paper, we hypothesise that in a trust-based routing protocol, it will be additional trust evaluation reliability if peers can take into consideration their partners attributes' status in addition to the trust relationships. We propose DATM protocol; a trust-based scheme to enforce collaborative behaviour in wireless mobile networks taking into consideration the peers' attributes for an efficient routing scheme. We introduce a personalised similarity algorithm for peers' attributes modelling as a scaling factor for trust evaluation. The relative comparison of the DATM and PROPHET protocol using simulation shows the improved performance of our proposed model in terms of messages delivery, messages overhead and average latency.
Keywords:
Trust-based routing protocol
Secure mobile wireless networks
Collaborative routing
Peers' routing attributes
Routing performance
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.5
Papers:
8.0K
Citations:
2.1W