关键词： micro-service composition   micro-services   mobile edge computing   multi-objective optimization  

摘要： As a new paradigm of distributed computing, mobile edge computing (MEC) has gained increasing attention due to its ability to expand the capabilities of centralized cloud computing. In MEC environments, a software application typically consists of multiple micro-services, which can be composed together in a flexible manner to achieve various user requests. However, the composition of micro-services in MEC is still a challenging research issue arising from three aspects. Firstly, composite micro-services constructed by ignoring the processing capabilities of different micro-services may cause waste of edge resources. Secondly, edge servers' limitations in terms of computational power can easily cause service occupancy between composite micro-services, severely affecting the user experience. Thirdly, in dynamic and unstable mobile environments, different edge users have different sensitivities to request latency, which increases the complexity of micro-service composition. In order to improve edge resource utilization and user experience on micro-service invocations, in this paper, we comprehensively consider the above three factors, and we first model the micro-services composition problem in MEC as a constrained multi-objective optimization problem. Then, a micro-service composition optimization method M3C combining graph search and branch-and-bound strategy is proposed to find a composition solution set with low energy consumption and high success rate for multiple edge users. Finally, we perform a series of experiments on two widely used datasets. Experimental results show that our proposed approach significantly outperforms the four competing baseline approaches, and that it is sufficiently efficient for practical deployment.

关键词： Internet of vehicles   Mobile edge computing   Deep reinforcement learning   Federated learning   Task offloading   Resource allocation  

摘要： Mobile edge computing (MEC) enables computation intensive applications in the Internet of Vehicles (IoV) to no longer be limited by device resources. However, the lack of an effective task scheduling strategy will seriously affect users' quality of experience (QoE). In this paper, a task type-based task offloading and resource allocation strategy is proposed to reduce delay and energy consumption during task execution. First, we establish communication, computing, and system cost models based on task offloading schemes, and model the joint optimization problem of task offloading and resource allocation as a Markov decision process. The utility function is obtained based on the task completion rate and the system cost. Second, an algorithm framework based on multi-agent deep deterministic policy gradient (MADDPG) is designed to solve the difficulty that traditional single-agent reinforcement learning algorithms are difficult to converge in a dynamic environment. In distributed scenarios, the proposed framework can also reduce system costs while handling more tasks. Finally, federated learning is introduced in the training process to reduce the impact of non-IID data while protecting privacy. Simulation results show that the proposed algorithm can effectively improve system processing efficiency and reduce device energy consumption compared to the popular reinforcement learning algorithms.

关键词： Servers   Computer architecture   Internet of Things   Task analysis   Energy consumption   Security   Optimization   Mobile edge computing   radio access networks   resource allocation   energy consumption   Q-learning algorithm  

摘要： As information technology rapidly advances, 5G technology, Radio Access Networks (RAN), and the Internet of Things (IoT) have emerged as the core elements of next-generation communication technology. There is an increasing demand for real-time communication and reduced latency in various applications. Therefore, this paper proposes a four-layer Mobile Edge Computing (MEC) architecture that connects user devices to the core network using RAN. Blockchain verification is used for data storage and access permission separation. The architecture aims to address the high latency, low flexibility, and security issues in cloud computing communication. We also propose a MEC server location algorithm to optimize communication distance, and a Q-learning algorithm for selection and resource allocation. Experimental results demonstrate significant energy savings compared to baseline algorithms.

关键词： Mobile edge computing   Multi-UAV network   Task offloading   Path optimization   Multi-agent deep reinforcement learning  

摘要： To tackle task offloading and path planning challenges in multi-UAV-assisted mobile edge computing, this paper proposes a task offloading and path optimization approach via multi -agent deep reinforcement learning. The primary goal is to minimize the overall energy consumption of the system and improve computational performance. Initially, we established a model for a multi-UAV-assisted mobile edge computing system that centrally manages the UAV network through software -defined networking technology. Subsequently, considering UAV load and fairness in user equipment -related services, we employ the multi -agent deep deterministic policy gradient algorithm to optimize task offloading and UAV path management, aiming at load balancing and reducing overall system energy consumption. Simulation results demonstrate our method's effectiveness in reducing energy consumption and computation latency compared to benchmark algorithms. It ensures system efficiency, stability, and reliability, meeting mobile edge users' service requests while utilizing computing resources efficiently.

关键词： Video caching   Mobile edge computing   Asynchronous federated learning   User mobility  

摘要： With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real -time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AFMPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

关键词： Task analysis   Containers   Image edge detection   Internet of Things   Clustering algorithms   Heuristic algorithms   Dynamic scheduling   Container scheduling   mobile edge computing   reinforcement learning  

摘要： In Mobile Edge Computing (MEC), Internet of Things (IoT) devices offload computationally-intensive tasks to edge nodes, where they are executed within containers, reducing the reliance on centralized cloud infrastructure. Cluster software upgrades are essential to maintain the efficient and secure operation of edge clusters. However, traditional cloud cluster upgrade strategies are ill-suited for edge clusters due to their geographically distributed nature and resource limitations. Therefore, it is crucial to properly schedule containers during edge cluster upgrades to minimize the impact on running tasks. This article proposes a latency-aware container scheduling algorithm for efficient edge cluster upgrading. Specifically: 1) We formulate the online container scheduling problem for edge cluster upgrade to minimize the total task latency. 2) We propose a policy gradient-based reinforcement learning algorithm that addresses this problem by considering the characteristics of MEC, including heterogeneous resources, image distribution, and low-latency requirements. Subsequently, a location feature extraction method based on self-attention is designed to fully extract and utilize edge node distribution. 3) Experiments based on simulated and real-world data traces demonstrate that our algorithm reduces total task latency by approximately 30% compared to baseline algorithms.

关键词： Task analysis   Resource management   Autonomous aerial vehicles   Servers   Computer architecture   Sun   Optimization   Mobile edge computing   post-disaster rescue   task offloading   UAV communication   vehicle fog computing  

摘要： Unmanned aerial vehicles (UAVs) are playing an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address these challenges, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to alternative approaches, especially under heavy system workloads.

关键词： Federated learning   mobile edge computing   unmanned aerial vehicle   wireless powered communication network   Federated learning   mobile edge computing   unmanned aerial vehicle   wireless powered communication network  

摘要： In recent years, federated learning (FL) has been adopted in mobile edge computing (MEC) to protect user privacy. However, in some cases, in addition to performing FL to process private data, users also need to process other non-private data. Therefore, how to integrate private data and non-private data in a MEC system for comprehensive processing is an issue worth studying. In this paper, we propose an unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN) to process both FL tasks and offloadable MEC tasks of UEs, where the UAV charges UEs via wireless power transfer (WPT) technology, executes offloaded tasks, and aggregates the FL model parameters. To minimize energy consumption of the UAV, we formulate a problem to jointly optimize the hovering position of UAV, the WPT power, the proportion of UAV's computing resources, the percentage of offloaded tasks, and the time scheduling of FL under the energy harvesting constraint. The problem is divided into three subproblems with the aid of block coordinate descent (BCD) method. These subproblems are solved by Lagrange method and heuristic algorithm respectively. Numerical results show our algorithm can reduce the energy consumption of the UAV with low time complexity compared with several benchmarks.

关键词： Mobile edge computing   Task offloading   Task scheduling   Particle swarm optimization   Genetic algorithm  

摘要： Mobile edge computing (MEC) is considered one of the key technologies for large-scale network services. Task scheduling helps to improve the task completion rate of MEC, by properly mapping tasks generated by devices onto MEC resources. However, the mobility of devices introduces complexities, potentially resulting in failed task offloading or unavailable task results. To tackle this issue, we propose a mobile-aware task scheduling scheme. We first model the trajectory of mobile devices and introduce a strategy for the fastest task offloading, coupled with an efficient result return method. Subsequently, to improve the task completion rate, we present a task scheduling model based on task migration and formulate the relevant problem as a Mixed Integer Non-linear Programming (MINLP) problem. To achieve a solution within a reasonable time complexity, we propose a Particle Swarm Optimization and Genetic Algorithm with a Rescheduling operator (PSOGAR). In PSOGAR, particles update their positions using a mating operator, while maintaining diversity by a mutation operator. In addition, a rescheduling operator is used to further improve the task completion rate. Finally, through simulation experiments, compare PSOGAR with state-of-the-art and classic algorithms. The experimental results show that PSOGAR can improve the task completion rate by 18-31% and can be applied to scenarios with tight task deadlines.

关键词： acoustic sensing   mobile edge computing   Parkinson's disease   privacy protection  

摘要： In this article, we propose a mobile edge computing (MEC)-related system named PD-Gait, which can measure gait parameters of Parkinson's disease patients in a contactless and privacy-preserving manner. We utilize inaudible acoustic signals and band-pass filters to achieve privacy data protection in the physical layer. The proposed framework can be easily deployed in the mobile end of MEC, and hence release the edge server in cybersecurity attacks fighting. The gait parameters include stride cycle time length and moving speed, and hence providing an objective basis for the doctors' judgment. PD-Gait utilizes acoustic signals in bands from 16 to 23 kHz to achieve device-free sensing, which would release both doctors and patients from the tedious wearing process and psychological burden caused by traditional wearable devices. To achieve robust measurement, we propose a novel acoustic ranging method to avoid "broken tones" and "uneven peak distribution" in the received data. The corresponding ranging accuracy is 0.1 m. We also propose auto-focus micro-Doppler features to extract robust stride cycle time length, and can achieve an accuracy of 0.052 s. We deployed PD-Gait in a brain hospital and collected data from 8 patients. The total walked distance is over 330 m. From the overall trend, our results are highly correlated with the doctor's judgment.