Title
Hierarchic Topology Management by Decision Model and Smart Agents in Space Information Networks
Abstract
Space information network, which is envisioned as a new type of self-organizing networks constituted by information systems of land, sea, air, and space, has attracted tremendous interest recently. In this paper, to improve the data delivery performance and the network scalability of space information networks, a new hierarchic topology management scheme based on decision model and smart agents is proposed. Different from the schemes studied in mobile ad hoc networks and wireless sensor networks, the proposed algorithm in space information networks introduces a decision model based on analytic hierarchy process (AHP) to first select cluster heads, and then forms non-overlapping k-hop clusters. The proposed dynamical self-maintenance mechanisms take not only the node mobility but also the cluster equalization into consideration. Smart mobile agents are used to migrate and duplicate functions of cluster heads in a recruiting way, besides of cluster merger/partition disposal, reaffiliation management and adaptive adjustment of information update period. Simulation experiments are performed to evaluate the performance of the proposed algorithm in terms of network scalability, overhead of clustering and reaffiliation frequency. It is shown from the analytical and simulation results that the proposed hierarchic topology management algorithm significantly improves the performance and the scalability of space information networks.
Year
DOI
Venue
2015
10.1109/HPCC-CSS-ICESS.2015.122
HPCC/CSS/ICESS
Keywords
Field
DocType
smart agent, decision model, space information networks, topology management
Mobile ad hoc network,Information system,Logical topology,Computer science,Computer network,Network topology,Hierarchical network model,Cluster analysis,Wireless sensor network,Distributed computing,Scalability
Conference
ISSN
Citations 
PageRank 
2576-3504
0
0.34
References 
Authors
10
5
Name
Order
Citations
PageRank
Ning Ye101.01
Rong Geng200.34
Xiaoshi Song300.34
Qunyang Wang400.34
Zhaolong Ning555350.11