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.