The combination of Mobile Crowdsensing (MCS) with Opportunistic Networking (OppNet) allows mobile users to share sensed data easily and conveniently without the use of fixed infrastructure. OppNet is based on intermittent connectivity among wireless mobile devices, in which mobile nodes may store, carry and forward messages (sensing information) by taking advantage of wireless ad hoc communication opportunities. A common approach for the diffusion of this sensing data in OppNet is the epidemic protocol, which carries out a fast data diffusion at the expense of increasing the usage of local buffers on mobile nodes and also the number of transmissions, thereby limiting scalability.

A way to reduce this consumption of local resources is to set a message expiration time that forces the removal of old messages from local buffers. Since dropping messages too early may reduce the speed of information diffusion, we propose a dynamic expiration time setting to limit this effect. Moreover, we introduce an epidemic diffusion model for evaluating the impact of the expiration time. This model allows us to obtain optimal expiration times that achieve performances similar to those other approaches where no expiration is considered, with a significant reduction of local buffer and network usage. Furthermore, in our proposed model, the buffer utilisation remains steady with the number of nodes, whereas in other approaches it increases sharply. Finally, our approach is evaluated and validated in a mobile crowdsensing scenario, where students collect and broadcast information regarding a university campus, showing a significant reduction on buffer usage and nodes message transmissions, and therefore, decreasing battery consumption.

移动人群感知（MCS）与机会网络（OppNet）的结合使移动用户可以轻松便捷地共享感测数据，而无需使用固定基础结构。OppNet基于无线移动设备之间的间歇性连接，其中，移动节点可以利用无线自组织通信机会来存储，携带和转发消息（传感信息）。在OppNet中传播此感测数据的常用方法是流行病协议，该协议执行快速数据传播，但代价是增加了移动节点上本地缓冲区的使用率以及传输次数，从而限制了可伸缩性。

减少本地资源消耗的一种方法是设置消息过期时间，以强制从本地缓冲区中删除旧消息。由于过早丢弃消息可能会降低信息传播的速度，因此我们建议设置动态到期时间以限制这种影响。此外，我们引入了一种流行病扩散模型来评估到期时间的影响。该模型使我们能够获得最佳的失效时间，从而获得与不考虑失效的其他方法类似的性能，并显着减少本地缓冲区和网络使用率。此外，在我们提出的模型中，缓冲区利用率随着节点数量的增加而保持稳定，而在其他方法中，缓冲区利用率却急剧增加。最后，我们在移动人群感知场景中对我们的方法进行了评估和验证，