Nowadays, both vehicular active safety service and user infotainment service have become two core applications for urban Vehicular Delay Tolerant Networks(u VDTNs). Both core applications require a high data transmission capacity over u VDTNs. In addition, the connection between any two vehicles in u VDTNs is intermittent and opportunistic. Intermittent data dissemination over u VDTNs is a stringent and challenging issue. In this paper,we propose Intermittent Geocast Routing(IGR). For the first step, IGR has to estimate the active connection time interval via the moving directions and velocities between any two vehicles. Second, the throughput function for u VDTNs is fitted by building a wavelet neural network traffic model. Third, the throughput function within the effective connection time interval is integrated to obtain the forwarding capability estimation of the node. Fourth, a high-efficiency geocast routing algorithm using the node forwarding capability for u VDTNs is designed. Finally, IGR is simulated on the opportunistic Network Environment simulator. Experimental results show that IGR can greatly improve the packet delivery ratio, transmission delay, delay jitter, and packet loss rate compared with the state of the art.
为了解决传统数据网格调度算法在对层次式数据网格调度过程中出现的极易陷入局部最优值和收敛速度过慢的问题,将粒计算的思想引入到网格调度中,提出了一种基于商空间的层次式数据网格资源调度QSHDGRA(quotient space theory based hierarchical data grid resource allocation)算法。首先分析了层次式数据网格的特点,接着提出一种基于业务请求平均等待时间和网络与节点资源利用均衡度的调和函数的调度问题模型,随后设计了基于商空间的层次式最优资源调度算法。该算法的特点是可以在不同粒度上由粗至细地对网格业务进行调度,从而保证不同业务的QoS,并实现系统全局最优资源分配。仿真实验表明,算法可以显著地提升系统整体的吞吐率,具有更快的收敛速度,并具备线性扩展能力。
