  In mobile crowd sensing networks data forwarding through opportunistic
contacts between participants. Data is replicated to encountered participants.
For optimizing data delivery ratio and reducing redundant data a lot of data
forwarding schemes, which selectively replicate data to encountered
participants through node's data forwarding metric are proposed. However most
of them neglect a kind of redundant data whose Time-To-Live is expired. For
reducing this kind of redundant data we proposed a new method to evaluate
node's data forwarding metric, which is used to measure the node's probability
of forwarding data to destination within data's constraint time. The method is
divided into two parts. The first is evaluating nodes whether have possibility
to contact destination within time constraint based on transient cluster. We
propose a method to detect node's transient cluster, which is based on node's
contact rate. Only node, which has possibility to contact destination, has
chances to the second step. It effectively reduces the computational
complexity. The second is calculating data forwarding probability of node to
destination according to individual ICT (inter contact time) distribution.
Evaluation results show that our proposed transient cluster detection method is
more simple and quick. And from two aspects of data delivery ratio and network
overhead our approach outperforms other existing data forwarding approach.
