Incremental Survivability Enhancement in Mobile Crowdsensing Systems

Mobile crowdsensing (MCS) is a sensing paradigm that leverages the collaboration of mobile devices to collect and process data, and it has been widely applied in domains such as transportation, environmental monitoring, and public safety. However, its open nature and reliance on cooperative networks render it vulnerable to threats such as malicious nodes, link attacks, and data tampering. Given that MCS systems heavily depend on user participation and a stable network topology, these threats are not only difficult to fully mitigate but may also lead to service interruptions, degradation in data quality, or even system-wide failures—particularly in resource-constrained or unstable network environments. To address these challenges, a method to enhance the survivability of MCS systems is proposed. Firstly, to capture the complex interaction patterns among sensing devices in an MCS system, a neighbor interaction encoding scheme is proposed. This scheme learns the temporal node sensing representation of the dynamic graph by considering not only the information of nodes, edges, and time intervals but also the interactions between neighboring devices. Then, by integrating node-specific attribute information with betweenness centrality, the goal is to predict the importance of nodes in the dynamic graph and classify their roles accordingly. Finally, hierarchical threat response strategies are devised for nodes of varying importance, with adaptive topology reconstruction then performed based on predictions of future links between devices. Simulation experiments were conducted on eight real-world datasets, where the average AP and AUC-ROC under three sampling strategies were 88.84 and 87.86, respectively. These results represent average improvements of 6.24% and 4.35% compared to the baseline methods. Additionally, the effectiveness of the proposed method was validated through a mitigation effect evaluation experiment.