节点-区域关联度感知的区域数据分发算法

为了减少系统开销和降低网络负荷量，设计了节点-区域关联度感知的区域数据分发算法(RDAA-RP):首先，以时间片为周期持续记录和更新节点的区域属性；然后，计算节点对区域的权值并设置阈值作为数据转发限制条件；最后，根据权值控制不同程度关联度的节点参与完成区域数据的共享和交换.为验证RDAA-RP算法的效果，在The ONE平台进行了仿真实验，对比了在不同节点缓存大小和不同传输速度下，RDAA-RP算法、地图分区算法(SSMZ)和Epidemic算法的性能.仿真结果表明:(1)RDAA-RP算法能够在消息采集率与Epidemic算法及SSMZ算法基本相当的情况下，较大程度地降低网络负荷量，并降低消息平均缓存时间; (2)RDAA-RP算法可以有效屏蔽无关或低关联度节点数据带来的干扰，提供可靠的区域特定数据采集分发功能，实现关联节点数据共享的目标.

A Local Data Dissemination Strategy of Node-Region Correlation Sensing

A regional data acquisition algorithm (RDAA-RP) based on relevance perception is designed to reduce system overhead and network load. Firstly, the region attributes of nodes are continuously recorded and updated with time slice as a period. Then, the node-region weights are calculated and the weight threshold is set as restriction condition on data forwarding. Finally, nodes with different degrees of association are controlled to participate in the sharing and exchange of regional data according to their weights. To verify the effectiveness of the RDAA-RP algorithm, simulation experiments are carried out through The ONE platform, and the performance of the RDAA-RP algorithm, map partitioning based data distribution strategy (SSMZ) and Epidemic algorithm under different node cache sizes and transmission speeds are studied. The simulation results show that the RDAA-RP algorithm can greatly reduce the network load and the average message buffering time when the message collection rate is basically equal to the Epidemic algorithm and the SSMZ algorithm. The RDAA-RP algorithm also effectively shields the interference caused by irrelevant or low-correlation node data and provides reliable region-specific data collection and distribution function to realize the goal of data sharing among related nodes.