Research Of Military Wireless Self-organized Network Based On Clustering Algorithm

The military wireless self-organized network based on Ad Hoc network is a nocenter, self-management and multi-hop sharing wireless network, which is getting moreand more attention with its advantages of no using infrastructure, expanded rapidly andstrong survivability.Now, it is already widely used in many foreign troops, but in ourcountry the research of this field is still at the level of beginning and some technicalquestions are not solved. On the basis of analysis of the battlefield environment andmilitary communication’s characteristics, this paper gives a study on the architecture ofMilitary wireless self-organized network, protocol stack model and the networkclustering algorithm, which is combined with our military formation and the state ofcommunication equipment. The main contents are as follows:Firstly, the paper makes an analysis on business requirement, functionalrequirement and technical requirement of the military self-organized network, and thenit gives the goal and technical indicators of building a network.Secondly, the characteristics of battlefield environment, battlefield communicationand military self-organization network are analyzed in detail, combined with workexperience, a kind of hierarchical tactical network structure which suits the divisionlevel army is proposed, then the common protocol stack model of military self-organization network is put forward based on the Ad Hoc network protocol stack model.At last, a comparison of several traditional clustering algorithms applied in AdHoc Networks is made, and one clustering algorithm named AOW is selected to bestudied in-depth. The paper points out the reason why AOW is not suitable for thebattlefield environment and proposes one improved AOW algorithm based on commandand subjection relationship. The article imitates the battlefield environment withMATLAB to make some simulations and the results show that the improved AOWalgorithm is more suitable for the tactical environment than traditional clusteringalgorithm. In the end, a discussion and outlook on the prospects of the military self-organization network is given.