关键词： Optimization   Servers   Artificial intelligence   Resource management   Batteries   Energy consumption   Delays   Latency minimization   delay minimization   mobile edge computing   resource allocation   user association   task offloading   energy efficiency   edge AI  

摘要： The growth in artificial intelligence (AI) technology has attracted substantial interests in latency-aware task offloading of mobile edge computing (MEC)-namely, minimizing service latency. Additionally, the use of MEC systems poses an additional problem arising from limited battery resources of MDs. This paper tackles the pressing challenge of latency-aware distributed task offloading optimization, where user association (UA), resource allocation (RA), full-task offloading, and battery of mobile devices (MDs) are jointly considered. In existing studies, joint optimization of overall task offloading and UA is seldom considered due to the complexity of combinatorial optimization problems, and in cases where it is considered, linear objective functions such as power consumption are adopted. Revolutionizing the realm of MEC, our objective includes all major components contributing to users' quality of experience, including latency and energy consumption. To achieve this, we first formulate an NP-hard combinatorial problem, where the objective function comprises three elements: communication latency, computation latency, and battery usage. We derive a closed-form RA solution of the problem;next, we provide a distributed pricing-based UA solution. We simulate the proposed algorithm for various resource-intensive tasks. Our numerical results show that the proposed method Pareto-dominates baseline methods. More specifically, the results demonstrate that the proposed method can outperform baseline methods by 1.62 times shorter latency with 41.2% less energy consumption.

关键词： Autonomous aerial vehicles   Task analysis   Energy efficiency   Laser beams   Heuristic algorithms   Reinforcement learning   Inductive charging   Mobile edge computing   multi-objective reinforcement learning   trajectory optimization   unmanned aerial vehicle   wireless charging  

摘要： This paper investigates the problem of energy-efficient trajectory optimization with wireless charging (ETWC) in an unmanned aerial vehicle (UAV)-assisted mobile edge computing system. A UAV is dispatched to collect computation tasks from specific ground smart devices (GSDs) within its coverage while transmitting energy to the other GSDs. In addition, a high-altitude platform with a laser beam is deployed in the stratosphere to charge the UAV, so as to maintain its flight mission. The ETWC problem is characterized by multi-objective optimization, aiming to maximize both the energy efficiency of the UAV and the number of tasks collected via optimizing the UAV's flight trajectories. The conflict between the two objectives in the problem makes it quite challenging. Recently, some single-objective reinforcement learning (SORL) algorithms have been introduced to address the aforementioned problem. Nevertheless, these SORLs adopt linear scalarization to define the user utility, thus ignoring the conflict between objectives. Furthermore, in dynamic MEC scenarios, the relative importance assigned to each objective may vary over time, posing significant challenges for conventional SORLs. To solve the challenge, we first build a multi-objective Markov decision process that has a vectorial reward mechanism. There is a corresponding relationship between each component of the reward and one of the two objectives. Then, we propose a new trace-based experience replay scheme to modify sample efficiency and reduce replay buffer bias, resulting in a modified multi-objective reinforcement learning algorithm. The experiment results validate that the proposed algorithm can obtain better adaptability to dynamic preferences and a more favorable balance between objectives compared with several algorithms.

关键词： Graph neural networks   Resource management   Microservice architectures   Servers   Security   Electrical engineering   Knowledge engineering   Mobile edge computing   microservices   graph neural network   reinforcement learning  

摘要： Microservice (MS) structures a service application as a collection of independently deployable service modules, making it particularly suitable for delivering complex applications in distributed computing systems. This article investigates MS architecture over Mobile Edge Computing (MEC) networks (hereafter referred to as EdgeMS) and studies an EdgeMS placement problem that aims to deploy MS modules over the MEC network in a manner that maximizes the reward of MS application providers. A novel algorithm called Dual-GNN Deep Deterministic Policy Gradient (DG-DDPG) is proposed to establish an intelligent EdgeMS placement policy for optimizing the location of MS modules and performing fractional computing resource allocation. DG-DDPG leverages the graph neural network (GNN) to comprehend the graph-structured information encapsulated in the MS application structure and MEC network. A dual-GNN core is constructed in DG-DDPG, one GNN for MS applications to distill knowledge from intricate connections between MS modules, and the other GNN for MEC networks to capture complicated interactions between edge sites when providing EdgeMS. DG-DDPG embeds the dual-GNN core in a DDPG-based reinforcement learning framework, which not only handles temporal dependencies between EdgeMS placement decisions for maximizing long-term reward but also supports continuous action space for enabling fractional resource allocation. In particular, the learning process of DG-DDPG is tailored to address hard constraints (i.e., computing capacity and MS application completeness) in the EdgeMS placement problem. We design constraint-based regularization terms and add them to the objective of DG-DDPG, which facilitates the identification of feasible placement decisions during learning. We carry out systematic experiments to evaluate the performance of DG-DDPG, and the results show that DG-DDPG outperforms state-of-the-art benchmarks in terms of reward, service delay and deployment cost.

关键词： Computational modeling   Performance evaluation   Adaptation models   Accuracy   Servers   Internet of Things   Computer architecture   mobile edge computing   personalized federated learning   resource constraint   statistical heterogeneity  

摘要： Federated learning (FL) opens a new promising paradigm for the Industrial Internet of Things (IoT) since it can collaboratively train machine learning models without sharing private data. However, deploying FL frameworks in real IoT scenarios faces three critical challenges, i.e., statistical heterogeneity, resource constraint, and fairness. To address these challenges, we design a fair and efficient FL method, termed FedASA, which can address the challenge of statistical heterogeneity in resource-constrained scenarios by determining the shared architecture adaptively. In FedASA, we first present a cell-wised shared architecture selection strategy, which can adaptively construct the shared architecture for each device. We then design a cell-based aggregation algorithm for aggregating heterogeneous shared architectures. In addition, we provide a theoretical analysis of the federated error bound, which provides a theoretical guarantee for the fairness. At the same time, we prove the convergence of FedASA at the first-order stationary point. We evaluate the performance of FedASA through extensive simulation and experiments. Experimental results in cross-location scenarios show that FedASA outperformed the state-of-the-art approaches, improving accuracy by up to 13.27% with better fairness and faster convergence and communication requirement has been reduced by 81.49%.

关键词： Deep reinforcement learning   energy harvesting   laser technology   mobile edge computing   unmanned aerial vehicles   wireless sensor networks  

摘要： In sixth-generation (6G) networks, unmanned aerial vehicles (UAVs) are expected to be widely used as aerial base stations (ABS) due to their adaptability, low deployment costs, and ultra-low latency responses. However, UAVs consume large amounts of power to collect data from multiple sensor nodes (SNs). This can limit their flight time and transmission efficiency, resulting in delays and low information freshness. In this paper, we present a multi-access edge computing (MEC)-integrated UAV-assisted wireless sensor network (WSN) with a laser technology-based energy harvesting (EH) system that makes the UAV act as a flying energy charger to address these issues. This work aims to minimize the age of information (AoI) and improve energy efficiency by jointly optimizing the UAV trajectories, EH, task scheduling, and data offloading. The joint optimization problem is formulated as a Markov decision process (MDP) and then transformed into a stochastic game model to handle the complexity and dynamics of the environment. We adopt a multi-agent deep Q-network (MADQN) algorithm to solve the formulated optimization problem. With the MADQN algorithm, UAVs can determine the best data collection and EH decisions to minimize their energy consumption and efficiently collect data from multiple SNs, leading to reduced AoI and improved energy efficiency. Compared to the benchmark algorithms such as deep deterministic policy gradient (DDPG), Dueling DQN, asynchronous advantage actor-critic (A3C) and Greedy, the MADQN algorithm has a lower average AoI and improves energy efficiency by 95.5%, 89.9%, 78.02% and 65.52% respectively.

关键词： Collaborative inference   federated learning   mobile edge computing   semantic communication   time series forecasting   Collaborative inference   federated learning   mobile edge computing   semantic communication   time series forecasting  

摘要： Time series data widely exist in public services, industrial environments, and military applications. Traditionally, the transmission of a huge volume of data for analytic tasks poses challenges, particularly in mobile environments with limited computing and communication resources. Semantic communication emerges as a solution for intelligently extracting various features from source data and efficiently transmitting task-related information to receivers, thereby reducing bandwidth consumption significantly. In this paper, we introduce a novel federated semantic communication system tailored for forecasting-oriented time series transmission tasks. The correlation of source data collected from terminal devices is mined and the corresponding semantic information is transmitted to an edge server for collaborative inference. To optimize the semantic analysis process, we devise a deep decomposition block at the transmitter side, decomposing time series into trend and multiple period components. This reduces noise interference from wireless channels, enhancing the overall transmission quality. For effective training and collaborative inference, we propose a Federated Mixture of period Routers (FedMoR) architecture. Within each channel encoder, period routers are divided into private and public ones. Private routers extract specialized features from individually collected data, mitigating accuracy degradation. Public routers share knowledge across all transmitters, enhancing temporal analysis robustness. Simulation results demonstrate that the proposed system outperforms two traditional technique-based and two semantic communication-based baselines under three common channels. The system achieves low mean square errors on five widely-used real-world time series forecasting datasets, particularly in the low signal-to-noise ratio regime.

关键词： Training   Resource management   Task analysis   Smart cities   Servers   Quality of service   Delays   Mobile edge computing   computation offloading   resource allocation   deep reinforcement learning   personalized federated learning  

摘要： Through deploying computing resources at the network edge, Mobile Edge Computing (MEC) alleviates the contradiction between the high requirements of intelligent mobile applications and the limited capacities of mobile End Devices (EDs) in smart communities. However, existing solutions of computation offloading and resource allocation commonly rely on prior knowledge or centralized decision-making, which cannot adapt to dynamic MEC environments with changeable system states and personalized user demands, resulting in degraded Quality-of-Service (QoS) and excessive system overheads. To address this important challenge, we propose a novel Personalized Federated deep Reinforcement learning based computation Offloading and resource Allocation method (PFR-OA). This innovative PFR-OA considers the personalized demands in smart communities when generating proper policies of computation offloading and resource allocation. To relieve the negative impact of local updates on global model convergence, we design a new proximal term to improve the manner of only optimizing local Q-value loss functions in classic reinforcement learning. Moreover, we develop a new partial-greedy based participant selection mechanism to reduce the complexity of federated aggregation while endowing sufficient exploration. Using real-world system settings and testbed, extensive experiments demonstrate the effectiveness of the PFR-OA. Compared to benchmark methods, the PFR-OA achieves better trade-offs between delay and energy consumption and higher task execution success rates under different scenarios.

关键词： mobile edge computing   data integrity verification   dynamic  

摘要： Mobile edge computing (MEC), is a technology that extends the power of cloud computing to the edge of the end device, with the primary goal of providing faster response times and an optimized quality of service. Given that edge devices are often used by smaller organizations with less computing power, data on the edge are more susceptible to data corruption, which is closely related to uneven resource allocation and uneven security protection. It is therefore particularly important to check the integrity of the MEC to ensure that it is intact, which in turn serves the symmetry of data security protection and facilitates the realization and optimization of symmetry in resource allocation. An integrity verification protocol MEC-P is proposed. MEC-P allows a third-party auditor (TPA) to check data integrity on the edge without violating users' data privacy and query pattern privacy. We rigorously demonstrate the security and privacy guarantees of the protocol. In addition, this protocol carefully considers the scenarios of single edge node and multiple edge nodes, as well as the complexity of dynamic modifications. Both theoretical analysis and experimental results demonstrate that the proposed protocol is effective.

关键词： Mobile Edge Computing   Federated Learning   Privacy-Preserving Algorithms   Resource Allocation   Data Offloading  

摘要： Mobile Edge Computing (MEC) and Federated Learning (FL) have recently attracted considerable interest for their potential applications across diverse domains. MEC is an architecture for distributed computing that utilizes computational capabilities near the network edge, enabling quicker data processing and minimizing latency. In contrast, FL is a method in the field of Machine learning (ML) that allows for the simultaneous involvement of multiple participants to collectively train models without revealing their raw data, effectively tackling concerns related to security and privacy. This systematic review explores the core principles, architectures, and applications of FL within MEC and vice versa, providing a comprehensive analysis of these technologies. The study emphasizes FL and MEC's unique characteristics, advantages, and drawbacks, highlighting their attributes and limitations. The study explores the complex architectures of both technologies, showcasing the cutting-edge methods and tools employed for their implementation. Aside from examining the foundational principles, the review explores the depths of the internal mechanisms of FL and MEC, offering a valuable in-depth of their architecture understanding and the fundamental principles and processes that facilitate their operation. At last, the concluding remarks and future research directions are provided.

关键词： Autonomous aerial vehicles   Security   Switches   Jamming   Interference   Task analysis   Relays   Consensus   mobile edge computing   physical layer security   unmanned aerial vehicle (UAV) communications  

摘要： Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) have become a hotspot in recent years. However, due to the broadcast nature of wireless channels, it is difficult to avoid the presence of eavesdroppering in the networks, which can severely influence the users privacy protection between users and UAVs. In order to enhance the communication security of the networks while reducing system latency, this paper considers a dual UAV-assisted edge computing system that contains two UAVs: one UAV acts as an MEC server to process users' offloaded computation tasks, and the other multi-role UAV can function as a jammer or a relay, which depends on the users' expectations, to improve communication security and reduce system latency. However, due to the variables coupling and inconsistency in user preferences for UAV functionalities, the jointly optimization of UAV trajectories, computation resource allocation, and multi-role UAV's functionality switching is challenging. To address this issue, this paper proposes Distributed UAV Functionality Switching and Trajectory Design (DUFST) algorithm, which utilizes block coordinate descent (BCD) and successive convex approximation (SCA) to decouple the variables. Then, DUFST adopts the alternating direction method of multipliers (ADMM) to tackle the problem of inconsistent user preferences for UAV functionality selection. Simulation results verify the effectiveness of this algorithm, which outperforms other comparative algorithms in terms of performance.