An Improved Content-Based Data Dissemination In Human Contact-Based Network

Abstract

Limited wireless communication devices carried by individuals make up human contact-based network. Such network often suffers from intermittent connectivity. This fact decreases successful ratio of information spreading and prolongs data delivery process. To get good performance on delivery ratio and transmission delay, providing effective data dissemination algorithm among large-scale human contact-based network is very important. In this paper, we develop an Improved Content-based Data Dissemination algorithm, which efficiently resolves data dissemination as data sources and interested receivers are agnostic of each other. In our algorithm, nodes exchange information according to respective interests with each other, simultaneously, exploit gathered history neighbor interests to dynamically adjust the probability of requesting messages not of interest to themselves and make distributed decisions on whether storing messages or not to benefit other nodes. Compared with the Epidemic algorithm and Opportunistic Content-Based Dissemination algorithm, simulation results have confirmed that our algorithm obtains the highest delivery ratio and almost the lowest transmission delay, as the buffer size and bandwidth are restricted.