With the rapid development of Internet-of-Things (IoT) devices and mobile communication technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to extend cloud computing and storage capabilities to the edge of cellular networks, near to IoT devices. MEC enables IoT devices with limited battery capacity and computation/storage capabilities to execute their computation-intensive and latency-sensitive applications at the edge of the networks. However, to efficiently execute these applications in MEC systems, each task must be properly offloaded and scheduled onto the MEC servers. Additionally, the MEC servers may intelligently balance and share their computing resources to satisfy the application QoS and QoE. Therefore, effective resource allocation (RA) mechanisms in MEC are vital for ensuring its foreseen advantages. Recently, Machine Learning (ML) and Deep Learning (DL) have emerged as key methods for many challenging aspects of MEC. Particularly, ML and DL play a crucial role in addressing the challenges of RA in MEC. This paper presents a comprehensive survey of ML/DL-based RA mechanisms in MEC. We first present tutorials that demonstrate the advantages of applying ML and DL in MEC. Then, we present enabling technologies for quickly running ML/DL training and inference in MEC. Afterward, we provide an in-depth survey of recent works that used ML/DL methods for RA in MEC from three aspects: (1) ML/DL-based methods for task offloading; (2) ML/DL-based methods for task scheduling; and (3) ML/DL-based methods for joint resource allocation. Finally, we discuss key challenges and future research directions of applying ML/DL for resource allocation in MEC networks.

Cloud technology has brought great convenience to enterprises as well as customers. System logs record notable events and are becoming valuable resources to track and investigate system status. Detecting anomaly from logs as fast as possible can improve the quality of service significantly. Although many machine learning algorithms (e.g., SVM, Logistic Regression) have high detection accuracy, we find that they assume data are clean and might have high training time. Facing these challenges, in this paper, we propose Robust Online Evolving Anomaly Detection (ROEAD) framework which adopts Robust Feature Extractor (RFE) to remove the effects of noise and Online Evolving Anomaly Detection (OEAD) to dynamic update parameters. We propose Online Evolving SVM (OES) algorithm as the example of online anomaly detection methods. We analyze the performance of OES in theory and prove the performance difference between OES and the best hypothesis tends to zero as time goes infinity. We compare the performance of ROEAD against state-of-the-art anomaly detection algorithms using public log datasets. The results demonstrate that ROEAD is able to remove the effects of noise and OES can improve the detection accuracy by more than 40%.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) can be deployed to complement fixed charging stations. Currently, the strategy of MCSs is to move towards the EVs with insufficient energy (IEVs) only after being requested, which is not efficient. However, similar to online car-hailing services, more IEVs could be charged and the charging expenses could be reduced if idle MCSs can actively move towards the potential charging positions. In this paper, the problem of placements of idle MCSs in an IoEV is investigated in order to enhance the proportion of charged IEVs and reduce the charging expenses of IEVs. To this end, we propose a Federated Learning based Placement Decision Method of Idle MCSs (FL-PDMIM) to help the idle MCSs to predict the future charging positions, by exploiting the historical routes of MCSs which contain rich information regarding the charging demand of IEVs. In the proposed framework, the historical routes are trained locally by each MCS, and then the local model parameters and charging records are periodically uploaded to an edge server for a global parameter aggregation. Then, idle MCSs decide their placements according to the predicted charging positions (potential charging positions). The training time can be largely shortened, because the distributed learning on each MCS is executed in parallel. Extensive simulations and comparisons demonstrate the performance superiority of FL-PDMIM. Specifically, with the proposed federated learning-based predictions, the waiting time of IEVs to be served can be significantly shortened, and FL-PDMIM enhances the proportion of charged IEVs and reduces the charging expenses of IEVs effectively.

Mobile crowdsourcing has become an efficient paradigm for performing large scale tasks. The incentive mechanism is important for the mobile crowdsourcing system to stimulate participants, and to achieve good service quality. In this paper, we focus on solving the insufficient participation problem for the budget constrained online crowdsourcing system. We present a two-tiered social crowdsourcing architecture, which can enable the selected registered users to recruit their social neighbors by diffusing the tasks to their social circles. We present three system models for two-tiered social crowdsourcing system based on the arrival modes of registered users and social neighbors: offline model, semi-online model, and full-online model. We consider the tasks are associated with different end times. We present an incentive mechanism for each of three system models. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed incentive mechanisms achieve computational efficiency, individual rationality, budget feasibility, cost truthfulness, and time truthfulness. We further show that our incentive mechanisms for semi-online model and full-online model can obtain averagely 51.1 $\%$ and 39.7 $\%$ value of approximate optimal untruthful offline algorithm, respectively.

Electric vehicles (EVs), as promising components of the sustainable and eco-friendly transportation systems, are being widely adopted to reduce the consumption of fossil fuel and pollution of environments. EVs are usually equipped with wireless modules to support the vehicle to vehicle communications, by which an electric vehicular network (EVN) is formed. In EVN, some EVs are with insufficient battery energy and may exhaust the battery energy before arriving at their destinations, and these EVs are referred to as IEVs. More seriously, IEVs probably cannot find any fixed charging facilities nearby. With the development of mobile charging technology, some movable charging stations (MCSs) are deployed into EVN, and MCSs can actively navigate to charge IEVs. In this paper, an assignment rescheduling mechanism of movable charging stations (ARMM) is proposed, where the MCS assignments are dynamically rescheduled. In ARMM, in order to reduce the charging expenses of IEVs and enhance the proportion of charged IEVs, the assigned IEVs of some MCSs could be switched to other MCSs, while the charging positions of MCSs are selected by minimizing the charging expenses of IEVs and are dynamically altered. Besides, the incentives of assigned IEVs to reduce the charging expenses of unassigned IEVs are proven. Simulation results demonstrate the preferable performance of ARMM, i.e. ARMM can reduce the charging expenses of IEVs and enhance the proportion of charged IEVs effectively.