Training on time-series data generated from mobile networks is a resource-intensive and time-consuming task that encounters various training failures. To cope with this issue, we propose CheckBullet, a lightweight checkpoint system to minimize storage requirements and enable fast recovery in mobile networks. First, CheckBullet determines a checkpointing interval based on the characteristics of the model and the timing of failure occurrences. This approach ensures fast recovery while preserving the existing training runtime. Second, CheckBullet quantizes the weight tensor and eliminates duplicate weights, which significantly reduces the overall checkpoint size, leading to a substantial decrease in storage requirements. Third, CheckBullet selects the minimum training loss among the deduplicated checkpoints and merges the selected checkpoints. This approach reduces recovery time while preserving existing training loss. The experimental results show that CheckBullet can reduce the recovery time by$6\times$to$11\times$barely increasing the training runtime. Furthermore, CheckBullet can save storage requirements by up to 70% while maintaining the minimum training loss.

Vehicular sensing has become attracting an increasing research interest for cost-effective monitoring in urban areas. Even though multiple types of sensing data are required to form a multidimensional sensing map in urban sensing applications, most of the previous works have only considered the sensing quality of single sensor type. In this article, we formulate an optimization problem of task allocation to improve the overall sensing quality in multidimensional vehicular urban sensing. To mitigate the high complexity of the formulated problem, we prove the submodularity of the objective function and present a low-complexity heuristic algorithm called sensing quality-aware task allocation (SQTA) leveraging the property of submodular optimization. Extensive experiments have been conducted by using two real-world data sets, which demonstrate that SQTA can improve the average sensing quality of multiple sensor types and also guarantee sufficient levels of the sensing quality of all sensor types.