The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

In the Internet of Things (IoT) environment, due to the limited computing power of terminal equipment, edge-cloud computing is a good solution for sharing the computation resources. Terminals can offload partial computations to the edge servers for energy saving and reduce the data transmission delays since the edge servers are usually deployed near the terminal devices. However, in the existing solutions, there is still a lot of unnecessary data transmission when the data used in the offloading jobs of several terminals are related. Adding a shareable cache to the edge server and allowing different computing jobs to share the related data can further reduce the data transmission cost and job delay. In this paper, we study the computation offloading method with cached data and propose a novel cache-aware computation offloading strategy for edge-cloud computing in IoT. First, we formulize the cache-aware computation offloading location problem, our goal is to minimize the equivalent weighted response time of all jobs with computing power and cache capacity constraints. Then, we derive the global optimum solution based on transforming the problem to the transportation problem. Next, since the terminal device is difficult to obtain global status information, we propose an online computation offloading strategy, which is convenient in practical deployment. Finally, experiments show that our online offloading strategy approximates the global optimal solution and it reduces the weighted response time by about 26.13% on average compared to other competing algorithms.