Research On Group Mobility Models Of Tactical Ad Hoc Network

Tactical Ad Hoc network (TA) is a typical application of Ad Hoc network in thefield of battlefield communication, and the Mobile Nodes (MNs) in the TA have suchcharacters as wide distribution and strong demand of combat. Mobility model is oneof the key technologies that determine battle ability in TA. MNs in TA have someremarkable characteristics of collaboration, purposefulness and strategy. How toreflect battle requirements in mobility model is significant for TA to adapt dynamictopology. Consequently, it is necessary to develop a new mobility model to reflectdemand of combat.Cloud model is one model for uncertain transformation between qualitative andquantitative knowledge, and this model integrates fuzziness with randomnesscontained in concepts. Due to normal cloud model with good mathematical properties,so normal cloud model is one of the most important cloud models, and it can be usedto represent a large number of uncertain phenomena in natural science and socialscience. With the restriction on nodal mobility, so there exist some uncertaintiesduring node movement. Therefore, this thesis will combine with cloud model toresearch mobility model, the main works are summarized as follows:In order to realize topological transformation in tactical Ad Hoc networkaccording to the changes of target and surrounding environment during nodemovement, and to ensure that tactical mission can be implemented safely andeffectively, the Group Mobility model in tactical Ad Hoc network based on normalCloud Model (GMCM) is proposed, which introduces normal cloud model of artificialintelligence with uncertainty. Forward normalized cloud is used as generationalgorithm of MNs, which transforms C(Ex, Ey, Enx, Eny, Hex, Hey) to nodal positionsets of numerical representation. So it realizes to convert quantitative mobile intentioninto real node movement. Through setting different Ex, En and He, the proposedmodel can respectively adjust center of a group, coverage area and dispersion degreeof node movement. Additionally, this model achieves group merging or splittingthrough reconfiguring information of groups in switch station.The simulations show that input the same parameters will lead to slightlydifferent simulation results in accordance with mobile intention. But the trend of nodal position distribution is much the same, which conforms to node mobility of real-worldscenario. In addition, the simulation results show that different group mobility modelshave different impact on the performance evaluation of network protocol, and GMCMmodel is better than reference point group mobility model on the performance in somecircumstances.