DTN中基于生命游戏的拥塞控制策略

为了应对容迟网络中拓扑结构剧烈变化、节点间连接频繁中断等问题，报文通常采用“存储—携带—转发”的方式进行传输：节点将报文存储在缓存中，携带报文直到遇到合适的机会才将报文转发给其他节点.因为缓存有限，这样的传输方式会使节点缓存溢出，导致拥塞的发生.在容迟网络环境下提出一种基于生命游戏的拥塞控制策略(game of life based congestion control strategy in delay tolerant networks, GLCCS)，并将其应用于Epidemic路由方式.GLCCS借鉴生命游戏的演化思想，依据邻居节点中持有特定报文的节点比例来决定节点本地缓存中相应报文的操作.同时还提出了基于全网信息的报文排队机制和丢弃策略，依据传递或者丢弃一个报文对整个网络投递成功率的影响，计算出报文的效用值，按照效用值对缓存中报文进行排队和丢弃.在机会网络模拟器ONE中对仿真移动模型和真实运动轨迹进行模拟，实验结果表明，GLCCS与其他拥塞控制策略相比提高了投递成功率，减小了网络时延、丢包率以及负载比率.

Game of Life Based Congestion Control Strategy in Delay Tolerant Networks

In delay tolerant networks, to deal with the problems that the topology of the network changes dramatically and the disconnections between nodes are prevalent, researchers propose the store-carry-forward protocols: nodes store the messages in buffers and may carry them for a long time until they encounter the proper next hops or the destination nodes. Because of the limited buffer capacity of nodes, this way of message transmission is bound to bring buffer overflows and then lead to network congestion. A congestion control strategy based on the game of life is proposed for delay tolerant networks in this paper and it is applied to classic Epidemic routing protocol. This strategy determines the specific operations of a message stored in the local buffer of node according to the proportion of the holders of this message in all the nodes’ neighbors. Furthermore, the policies of message queuing and dropping are designed. The utility value of a certain message is calculated based on the influence of delivering or dropping this message on the delivery ratio of the whole network, and the messages stored in the buffers are queued by the utility value. The messages with large utility values have high priority to send and the messages with small utility values are dropped. Experiments under movement model (which is the build-in model in the ONE) and real trajectories are carried out in the ONE. Simulation results show that the congestion control strategy based on the game of life significantly improves the delivery ratio compared with other congestion control strategies, while the delivery latency, packet loss rate and overhead ratio are reduced at the same time.