Sparse mobile crowdsensing (Sparse MCS), a new paradigm for large-scale fine-grained urban monitoring applications, collects sensing data from relatively few areas and infers data for uncovered areas. In Sparse MCS, the task allocation problem is simplified to the area selection problem since it is typically assumed that there were enough participants across the target sensing area. However, in many real scenarios, there is no guarantee the platform can find participants to execute tasks in vital areas. In this case, additional moving costs are incurred, which is not beneficial for the MCS platform as organizers are cost-sensitive. To address this problem, we propose a novel Subarea Division Learning based Task Allocation framework in Sparse mobile Crowdsensing (SDLSC-TA) that integrates subarea division learning, task allocation, and sensing map reconstruction. Different from existing research, we design the subarea division learning module to provide guidance for a more reasonable task allocation scheme. Specifically, subarea division learning utilizes the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA) to perform uneven subarea division considering historical data and spatio-temporal correlations. Based on subarea division learning results, task allocation iteratively selects the most suitable cell and participant combining sensing levels, sensing, and moving costs. Finally, sensing map reconstruction utilizes Bayesian compressive sensing (BCS) to infer missing data while ensuring high quality. Using four typical urban sensing datasets, SDLSC-TA outperforms state-of-the-art sparse MCS frameworks by 15% lower total costs on average and 40% lower average sensing map error rate.

Acquiring synthetic aperture radar (SAR) images is inherently difficult and laborious. To mitigate this data scarcity, generative models for SAR images aim to learn underlying data distributions from large-scale datasets, ensuring robust performance. Generative models mitigate data scarcity but rely on centralized frameworks, posing privacy risks and limiting deployment in Internet of Things (IoT) scenarios. To tackle the aforementioned challenges, we introduce an innovative federated angle-aware generative diffusion (FAGD) framework for secure SAR image generation. This framework integrates three key innovations: federated learning (FL), the angle-aware generative diffusion (AAGDiff) model, and the local data knowledge distillation (LDKD) strategy. Specifically, we introduce the FL framework, which enables clients to collaboratively train a generative model by transmitting model weights, thereby eliminating raw data exchange and enhancing security. We then propose the AAGDiff model for client-side high-quality generation, leveraging denoising diffusion probabilistic models (DDPMs) to synthesize high-quality SAR images from noise using an angle-conditioned encoder, enabling the generation of SAR images at different target azimuth angles. Additionally, the LDKD strategy alleviates overfitting during federated training under Non-Independent and Non-Identically Distributed (non-IID) data distributions by allowing the model to distill knowledge primarily from less frequent classes, thus enhancing generalization and robustness. Evaluated extensively on MSTAR and OpenSARShip datasets, the proposed framework ensures secure SAR image generation while achieving superior target recognition performance. Overall, the FAGD framework offers an effective and scalable solution for privacy-preserving SAR image synthesis.

Federated learning (FL), owing to its distributed training paradigm and inherent privacy-preserving properties, has shown great potential in multi-sensor SAR target recognition. However, heterogeneous data distributions across clients and hierarchical heterogeneity among sensors can induce local model drift. The existing FL methods mitigate the impact of data heterogeneity but assume uniform differences in client data heterogeneity and ignore the inherent multi-level data heterogeneity of SAR images. To address this limitation, we propose FedC-DAC, a clustered FL framework designed to explicitly capture and utilize heterogeneity at multiple levels. The method integrates GMM-guided soft grouping to reveal latent sensor similarities, introduces intra-cluster dynamic aggregation to enhance representation sharing while mitigating overfitting, and applies cross-cluster calibration to align feature distributions and reduce global inconsistency. Experimental results on representative SAR benchmark datasets show consistent gains over representative FL baselines in Accuracy, Kappa, and F1 under strong heterogeneity, and performance close to centralized training when distributions are near uniform. These results demonstrate improved robustness and generalization for distributed SAR recognition.

Sparse mobile crowdsensing (Sparse MCS) is an emerging paradigm for urban-scale sensing applications, which recruits suitable participants to complete sensing tasks in only a few selected cells and then infers data of unsensed cells for saving sensing costs and obtaining high-quality sensing maps. In Sparse MCS, one crucial issue is task assignment, in which the platform selects cells whose sensing data can reduce inferred sensing maps errors (i.e., cell selection) and recruits the participant set with the maximum contribution for performing tasks (i.e., participant recruitment). The research on participant recruitment mainly focuses on single participatory-based or single opportunistic-based sensing mode. Due to the complementarity of two sensing modes, recruiting participants by only one sensing mode would result in wasting sensing resources and compromising the quality of task completion. Thus, combining the advantages of two sensing modes, we propose a task assignment framework based on hybrid sensing modes in Sparse MCS (HSM-SMCS) for achieving a good tradeoff between sensing quality and cost. Specifically, we propose a heuristic two-stage search strategy that simultaneously recruits opportunistic and participatory participants to perform tasks in significant cells within the constraint of total costs, considering their contributions to sensing map inference. Thereinto, for opportunistic participants, mobility prediction greatly affects task assignment effectiveness. However, existing prediction algorithms lead to unsatisfactory outcomes when the historical trajectory data of opportunistic participants are scarce. To effectively improve the predictive accuracy, we design a mobility prediction model based on transfer learning. The experimental evaluation on real trajectory data sets and sensor data sets of corresponding areas demonstrates that our framework outperforms state-of-the-art methods with higher quality reconstructed sensing maps.