Crowdsensing is, for a few years, a hot topic. Until now, research on crowdsensing mainly focused on scenarios with devices such as smartphones with huge memory and high computive skills. With the development of the Internet of Things (IoT), crowdsensing can be envisaged with other constraints. Indeed, some IoT nodes are mobile but with limitations about storage and processing capabilities, then connectivity disruptions might occur between the nodes. These issues are tackled by a Disruption Tolerant Networking architecture. In this article, we focus on a subset of IoT, Mobile Sensing Networks (MSN). We propose then, a mechanism which respects the constraints of the nodes and maintains high performance. This mechanism, the multi-level FREAK, uses the mean frequency of contacts with the destination. The metrics drives the transmission. Since some nodes might not meet the destination nor nodes meeting the destination, we had the idea of a multi-level metrics to allow these “disconnected” nodes to transmit data to the destination. We evaluate our proposal through simulations based on several real mobility traces. Our solution outperforms reference replication and quota-based DTN solutions.