Enhancing Information Dissemination in Dynamic Wireless Network using Stability and Beamforming

TL;DR

This paper proposes a distributed beamforming algorithm leveraging node stability predictions based on human mobility patterns to enhance information dissemination in dynamic, sparse wireless networks without global network knowledge.

Contribution

It introduces a novel stability-based beamforming approach that operates in a distributed manner, improving dissemination efficiency in rapidly changing networks.

Findings

Beamforming significantly improves information spread.

Stability prediction based on human mobility enhances node selection.

Distributed algorithm performs better than random or no beamforming models.

Abstract

Mobility causes network structures to change. In PSNs where underlying network structure is changing rapidly, we are interested in studying how information dissemination can be enhanced in a sparse disconnected network where nodes lack the global knowledge about the network. We use beamforming to study the enhancement in the information dissemination process. In order to identify potential beamformers and nodes to which beams should be directed we use the concept of stability. We first predict the stability of a node in the dynamic network using truncated levy walk nature of jump lengths of human mobility and then use this measure to identify beamforming nodes and the nodes to which the beams are directed. We also develop our algorithm such that it does not require any global knowledge about the network and works in a distributed manner. We also show the effect of various parameters such as number of sources, number of packets, mobility parameters, antenna parameters, type of stability used and density of the network on information dissemination in the network. We validate our findings with three validation model, no beamforming, beamforming using different stability measure and when no stability measure is associated but same number of node beamform and the selection of the beamforming nodes is random. Our simulation results show that information dissemination can be enhanced using our algorithm over other models.