Federated Learning (FL), allowing data owners to conduct model training without sending their raw data to third-party servers, can enhance data privacy in Mobile Edge Computing (MEC) which brings data processing closer to the data sources. However, the heterogeneity of local data and constrained local resources in MEC bring new challenges hindering the development of FL. To this end, we propose an Auction-based Cluster Federated Learning scheme, called ACFL, comprising a clustered FL framework and an auction-based client selection strategy. Our clustered FL framework first introduces a mean-shift clustering algorithm to FL, which can intelligently cluster clients according to their local data distribution. Then, we select clients from each cluster using an auction mechanism to participate in FL training, which can mitigate the impact of data heterogeneity on model convergence and balance energy consumption. Moreover, we prove the proposed clustered FL framework converges at a sublinear rate. Extensive experiments conducted on real-world datasets demonstrate that the proposed FL scheme outperforms the conventional FL schemes in terms of convergence rate and energy balance.

The rapid development of Internet of Vehicles (IoV) and Mobile Edge Computing (MEC) enables vehicles to offload their applications to Roadside Units (RSU). However, without knowledge of vehicles' future locations, wireless connections between vehicles and RSUs are too vulnerable to finish offloading processes. Most existing methods allocate computation tasks statically, ignoring latency between task reception and processing in real IoV environments. Moreover, multiple services running on RSUs limit the availability of resources for offloading which require an efficient allocation of limited available resources. Therefore, this paper addresses the vehicular computation offloading problem by predicting vehicle mobility. We first introduce the Estimated Time of Arrival service into the vehicular MEC architecture to predict vehicles' driving state. Second, we formulate vehicular computation offloading as a dynamic process that efficiently selects the offloading location for each computation task and satisfies the constraints of limited available resources to balance QoS and response fairness. Thirdly, the cooperation among RSUs in our work optimizes the task QoS, but it increases the search space causing a high time consumption. We use the Dynamic Programming algorithm to optimize QoS and response fairness in the cooperative computation offloading problem and propose two offloading algorithms. To obtain the performance of our algorithms close to real IoV environments, we propose a fine-grained simulation method based on Veins. We evaluate our and existing algorithms based on real datasets from Erlangen, Germany, and Shenzhen, China.

Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

In the context of mobile edge computing (MEC), the traditional method of requesting microservices from a central cloud can result in increased delay for users due to the physical distance between the user and the cloud server. To address this issue, MEC advocates for placing servers closer to the users at the edge of the network. However, this approach is constrained by the storage capacity and computing resources of edge servers (ESs). Therefore, it is crucial to devise a strategy for processing user requests that minimizes the average request delay. To address this problem, This article formulates the microservice caching problem as an image-based microservice placement and task request routing problem. We model the problem as an integer linear programming problem with multicondition constraints. Considering the limited resources of ESs, we propose a microservice placement algorithm called approximate algorithm based on randomized task request routing. The proposed algorithm is designed to provide near-optimal solutions in polynomial time, leveraging Chernoff’s theorem. Our approach is evaluated through comparisons with two existing algorithms: 1) the image-pull-based microservice cache request algorithm and 2) the greedy-based microservice cache and request routing algorithm. The results demonstrate that our algorithm exhibits superior performance compared to existing methods.

Mobile edge computing (MEC) plays a significant role in reducing network delay for Mobile Augmented Reality (MAR) services by caching these services close to the User Equipments (UEs). These MAR services collect UEs' network traffic and orientation information, and generate the service results back to UEs. However, the UE's mobility features change network traffic and orientation, negatively impacting MAR services' access frequencies and service preferences. Moreover, the changed access frequencies also influence the workload of cached MAR services, resulting in the uneven workload of edge servers. Therefore, this paper formalizes cooperative service caching based on UEs' location and orientation to optimize network delay and response fairness in MEC environments. To solve the problem, we propose a Service Caching strategy based on Regional Mobility features Awareness (SCRMA) algorithm, which consists of two stages. Firstly, the Regional Mobility features Awareness (RMA) algorithm perceives the user mobility features and service preferences, which provides a prerequisite for determining service caching strategy. Then, a Service Caching strategy based on a Genetic Algorithm (SCGA) is proposed to optimize network delay and response fairness. The simulation experiment on a real dataset shows that our service caching strategy averagely reduces network delay, fairness factor, and total cost by 11.49%, 33.24%, and 17.86% compared with the existing algorithms, respectively.