MobilityAware Service Caching in Mobile Edge Computing for Internet of Things 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 mobilityaware service caching mechanism. Our goal is to maximize the number of users who are served by the local edgecloud, and we need to make predictions about the userxe2x80x99s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a userxe2x80x99s 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 userxe2x80x99s 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 backpropagation (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.

ClusteringBased Algorithm for Services Deployment in Mobile Edge Computing Environment In the edge computing, the service is deployed to the edge server through virtualization technology. Most strategies of service deployment are singleness and ignore the diversity of usersu0027 requirements and the service deployment cost at the edge. This paper proposes a clusteringbased algorithm for service deployment, which considers the delay at the user side and the edgeside services deployment cost, and establishes a service deployment model based on multiobjective integer linear programming. Firstly, the Kmeans clustering algorithm is optimized to solve the problem of hotspot migration in the process of service deployment and reduce the deployment cost at the edge server side, then alternative enhanced heuristic algorithm is proposed to find the approximate optimal solution of services deployment. Experiments show that the algorithm can reasonably deploy services. Compared with traditional heuristic algorithms, the algorithm proposed in this paper has better performance in terms of user side and edge server side.