Mobile edge computing significantly reduces the delay for smart city services by offloading the related datasets on the edge server. The correlation among smart city datasets regarding their location and functionality plays a crucial role in determining the data offloading strategy. The more closely related these datasets are, the higher the probability they will be requested simultaneously. However, most existing methods often ignore the impact of location-functionality correlation on the datasets' access frequencies. If datasets with high correlation are offloaded on separate edge servers, it will increase cross-server delay. Therefore, we proposed a method to analyze location-functionality correlation among smart city datasets and optimize data offloading strategy. Then, we formalize a data offloading model to optimize the total expected profits, which is an NP-hard problem. To solve this problem, we propose a pruned Q-learning for data offloading algorithm that learns the location-functionality correlation among the datasets. To validate its effectiveness, we first conduct simulation experiments to validate the offloading strategy under both complex service scenarios and large-scale datasets. Furthermore, we implement a data offloading architecture based on SuperMap iServer. The experimental results demonstrate that our algorithm reduces the delay and energy consumption by 34.65% and 25.54%, respectively, compared to the baseline algorithms.

In vehicular edge computing, the vehicular services are cached and replaced among Roadside Units (RSU) to minimize delay in delay-sensitive services. However, without prior knowledge of these service preferences, the cached services suffer a low hit ratio and a high network delay. Before the appearance of requests from vehicles, most existing vehicular service caching methods design the service-sharing mechanisms among RSUs to optimize the hit ratio, but they ignore the influence of vehicle mobility on service-sharing. Moreover, in vehicular services replacement, the differences between actual and historical vehicle trajectories negatively impact the service-sharing mechanisms constructed before, which existing methods ignored. Thus, this paper addresses the vehicular service caching problem based on the surrounding function-features of vehicles and transportation correlations. Firstly, we use the surrounding function-features of vehicles to estimate the service preference and ensure the hit ratio of cached services. Secondly, we formulate the vehicular service caching problem as a constrained optimization problem and design a cooperation mechanism between RSUs, which is based on transportation correlations of vehicle trajectories. Thirdly, we propose two vehicular service caching methods based on Gibbs sampling to optimize the network delay of vehicular services and deal with the negative influence of vehicle mobility. Simulation in real datasets from Shenzhen, China shows that our methods have a better network delay and hit ratio than existing baselines by 15.26% and 5.6%, respectively.