Research And Application On Movement Model In Delay Tolerant Network

Delay Tolerant Network broke through the restrictions on communication in traditional network which require end-to-end connectivity by adopting the "Store-Carry-Forward" mechanism to transmit message hop-by-hop and utilizing nodes' moving bring the opportunities of encounter to complete communication. Movement model in Delay Tolerant Network describes the moving characteristic of nodes, which affects the probability, duration and frequency of encounter between nodes and therefore affects the message delivery probability; Routing algorithm, as one of the research focuses in Delay Tolerant Network, also has an important influence on network performance. So from the perspective of movement model and routing algorithm in Delay Tolerant Network, this thesis makes the following research works:For the problem that movement model should better imitate the nodes moving characteristic in network and should better fit real network scenario efficiently and reliably, the Daily Activity Movement model based on realistic human scenarios is established. The model considers three social roles in society including office workers, students, and the others to simulate their moving characteristic in real social life. Meanwhile, submodels of each kind of social role(containing office workers submodel, students submodel and the others submodel) and traffic submodel are designed. When in simulation, nodes move and take activities according to the movement model which predefined. The results demonstrate that, compared with the same type of movement model and dataset collected from real society, the Daily Activity Movement model can better fit the real network scenario.In order to solve the problem that the network performance will be affected by the number of message duplicates in Delay Tolerant Network, the message transmission strategy based on Continuous Time Markov Chain and Encounter Frequency Prediction is proposed. Firstly, make full use of encounter information between nodes, containing of contact time and inter-contact time and establish a continuous time markov chain based on nodes connected state of connection and disconnection. Secondly, predict encounter frequency through the established continuous time markov chain. Thirdly, utilize the encounter frequency prediction to distribute message duplicates for relay nodes. Through the above mentioned approaches, the transmission strategy of dynamic adaptive control message duplicates is finally designed. Compared with the same type of proposed routing algorithms and improved algorithm, the buffer size and number of nodes are treated as variables in simulation experiment. And results show that the message transmission strategy based on continuous time markov chain and encounter frequency prediction can improve the network performance by improving message delivery probability and reducing overhead ratio with increasing transmission latency slightly.The integration of movement model and message transmission strategy proposed in this thesis applied in Delay Tolerant Network, not only can well fit the realistic network scenario, but also improve message delivery probability.