关键词： Mobile edge computing   Computation offloading   Privacy -preservation   Multi-agent DRL   Resource allocation  

摘要： With the development of industrialization and intelligence, the Industrial Internet of Things (IIoT) has gradually become the direction for traditional industries to transform into modern ones. In order to adapt to the emergence of a large number of edge access devices such as sensors, as well as the demand for high-consumption and low-latency computing tasks, Mobile Edge Computing (MEC) has been proposed as an effective paradigm by the academic community. Users can offload tasks to MEC servers, greatly reducing the computing latency and energy consumption when using services. However, traditional single access point edge computing networks cannot meet the usage requirements of a large number of users. In addition, the privacy leakage issues arising from the offloading process are also easily overlooked. In this paper, we propose a privacy-preserving offloading scheme based on stochastic game theory considering multiple access points. We first construct a multi-access point offloading framework and quality of service (QoS) model. In terms of privacy, the privacy risks caused by the offloading preferences of different edge nodes are studied, and the privacy entropy is used to evaluate the privacy protection level. We comprehensively consider the energy consumption, latency requirements, user experience, and privacy protection of the system and formulate the problem as a Markov Decision Process (MDP). Finally, a joint optimal DRL algorithm with privacy preservation (JODRL-PP) is proposed to achieve the optimal offloading scheme of the system. Simulation results verify the effectiveness of our model and algorithm. (c) 2023 Elsevier Inc. All rights reserved.

关键词： Mobile edge computing   industrial internet of things   deep reinforcement learning   adaptive traffic matching  

摘要： Traffic matching for transmitting and computing is a pivotal limitation of data processing in mobile edge computing (MEC)-enabled Industrial Internet of Things (IIoT), which remains insufficiently addressed. This letter proposes a hybrid DRL-based adaptive traffic matching strategy to augment the data processing capability of IIoT. A multivariate model is proposed to maximize the long-term processed data volume by matching the traffic for wireless transmitting, backhaul transmitting, and edge computing in MEC-enabled IIoT, which takes into account minimum processed data volume and long-term energy constraints. A hybrid DRL-based method is proposed to solve the intractable problem, which combines operations research and a proposed twin delayed deep deterministic policy gradient (TD3) with the reward function engineering (RFE) method. The TD3 with RFE method introduces an enhanced reward function to alleviate sparse penalty predicament arising from the long-term constraint and incorporates prior knowledge (PK) to expedite convergence. The simulation validates that the proposed method attains processed data gains of 468.43%, 14.35%, 13.45%, 11.93%, and 4.65% over different benchmark methods, respectively.

关键词： Mobile edge computing   service function chain   digital twin   reliable service provisioning   approximation algorithm   online algorithm   resource allocation and optimization  

摘要： Mobile Edge Computing (MEC) has been identified as a desirable computing paradigm that provides efficient and effective services for various applications, while meeting stringent service delay requirements. Orthogonal to the MEC computing paradigm, Network Function Virtualization (NFV) technology is another enabling technology that provides the network resource management with great flexibility and scalability, where the instances of Virtual Network Functions (VNFs) are deployed in edge servers as Service Function Chains (SFCs) for SFC-enabled services. Although reliable service provisioning in MEC environments is fundamentally important, the deployed VNF instances usually are not reliable, which can be affected by their software implementation, their execution duration, the workload among edge servers, and so on. Empowered by digital twin techniques, the states of VNF instances can be maintained by their digital twins in a real-time manner and their reliability can be accurately predicted through their digital twins. In this paper, we study digital twin-assisted, SFC-enabled reliable service provisioning in MEC networks by exploiting the dynamics of VNF instance reliability. We concentrate on two novel optimization problems of reliable service provisioning: the service cost minimization problem, and the dynamic service admission maximization problem. We first show their NP-hardness. We then formulate an Integer Linear Program (ILP) solution, and devise an approximation algorithm with a constant approximation ratio for the service cost minimization problem. We third provide an ILP solution to the offline version of the dynamic service admission maximization problem. Built upon this offline ILP solution, we also develop an online algorithm with a provable competitive ratio for the problem, by adopting the primal-dual dynamic updating technique. We finally evaluate the performance of the proposed algorithms via simulations. Simulation results demonstrate that the prop

关键词： The Internet of unmanned aerial vehicles   mobile edge computing   machine learning   computation offloading   intelligent decision making  

摘要： Unmanned Aerial Vehicles (UAVs) play an important role in the Internet of Things and form the paradigm of the Internet of UAVs, due to their characteristics of flexibility, mobility, and low costs. However, resource constraints such as dynamic wireless channels, limited battery capacities, and computation resources of UAVs make traditional methods inefficient in the Internet of UAVs. The thriving of Mobile Edge Computing (MEC) and Machine Learning (ML) is of great significance and is promising for real-time resource allocation, trajectory design, and intelligent decision making. This survey provides a comprehensive review of key technologies, applications, solutions, and challenges based on the integration of MEC and ML in the Internet of UAVs. First, key technologies of MEC and ML are presented. Then, their integration and major issues in the Internet of UAVs are presented. Furthermore, the applications of MEC and ML in the Internet of UAVs under urban, industrial, and emergency scenarios are discussed. After that, this survey summarizes the current solutions for MEC and ML in the Internet of UAVs based on the considered issues. Finally, some open problems and challenges are discussed.

关键词： mobile edge computing   location privacy   differential privacy   location perturbation   safe reinforcement learning  

摘要： Mobile edge computing (MEC) integration with 5G/6G technologies is an essential direction in mobile communications and computing. However, it is crucial to be aware of the potential privacy implications of task offloading in MEC scenarios, specifically the leakage of user location information. To address this issue, this paper proposes a location-privacy-preserved task offloading (LPTO) scheme based on safe reinforcement learning to balance computational cost and privacy protection. This scheme uses the differential privacy technique to perturb the user's actual location to achieve location privacy protection. We model the privacy-preserving location perturbation problem as a Markov decision process (MDP), and we develop a safe deep Q-network (DQN)-based LPTO (SDLPTO) scheme to select the offloading policy and location perturbation policy dynamically. This approach effectively mitigates the selection of high-risk state-action pairs by conducting a risk assessment for each state-action pair. Simulation results show that the proposed SDLPTO scheme has a lower computational cost and location privacy leakage than the benchmarks. These results highlight the significance of our approach in protecting user location privacy while achieving improved performance in MEC environments.

关键词： Task analysis   Servers   Federated learning   Training   Computational modeling   Data models   Computer architecture   continual learning   mobile edge computing  

摘要： Federated Learning (FL) in mobile edge computing (MEC) systems has recently been studied extensively. In ubiquitous environments, there are usually cross-edge devices that learn a series of tasks across multiple independent edge FL systems. Due to the differences in the scenarios and tasks of different FL systems, cross-edge devices will forget past tasks after learning new tasks, which is unacceptable for devices that pay system costs to participate in FL. Continual learning (CL) is a viable solution to this problem, which aims to train a model to learn a series of tasks without forgetting old knowledge. Currently, there is no work to investigate the problem of CL in a cross-edge FL scenario. In this paper, we propose Cross-FCL, a Cross-edge Federated Continual Learning framework. Specifically, it enables devices to retain the knowledge learned in the past when participating in new task training through a parameter decomposition based FCL model. Then various cross-edge strategies are introduced, including biased global aggregation and local optimization, to trade off memory and adaptation. We conducted experiments on a real-world dataset and other public datasets. Extensive experiments demonstrate that Cross-FCL achieves best accuracy on IID and highly non-IID tasks with a low storage cost compared to other baselines.

关键词： Computational offloading   deep reinforcement learning   mobile edge computing   resource allocation   trajectory optimization   unmanned aerial vehicles  

摘要： With the promising techniques and mobile applications of the Internet of Things (IoT) in the fifth-generation (5G) wireless networks, the mobile edge computing (MEC) paradigm has been widely utilized to support the massive data processing and meet the computation demand of mobile devices. However, the service coverage is limited by the static servers. Therefore, the unmanned aerial vehicles (UAVs) with high mobility are deployed as aerial base stations (BS) to assist ground users to offload the task and process the data on edge. In this article, we introduce a decentralized user allocation and dynamic service scheme to assign the UAV deployment for a multi-UAV MEC system, which incorporates the deep reinforcement learning (DRL) technology into a two-layer training framework. The continuous action, including the UAV trajectory and bit allocation of offloading, is first optimized in the lower layer, while the upper one concentrates on the discrete action space for the user allocation. In particular, we develop a constraint reward function that considers the communication quality in the lower layer to accomplish efficient communication. The well-trained neural network is embedded in the upper layer to provide rapid feedback for the upper environment. Experimental results demonstrate that the proposed approach achieves superior performance than the baseline methods in terms of the user energy consumption.

关键词： air-ground collaborative heterogeneous networks   age of information   contract theory   generative diffusion model   mobile edge computing  

摘要： Mobile edge computing is critical for improving the user experience of latency-sensitive and freshness-based applications. This paper provides insights into the potential of non-orthogonal multiple access (NOMA) convergence with heterogeneous air-ground collaborative networks to improve system throughput and spectral efficiency. Coordinated resource allocation between UAVs and MEC servers, especially in the NOMA framework, is addressed as a key challenge. Under the unrealistic assumption that edge nodes contribute resources indiscriminately, we introduce a two-stage incentive mechanism. The model is based on contract theory and aims at optimizing the utility of the service provider (SP) under the constraints of individual rationality (IR) and incentive compatibility (IC) of the mobile user. The block coordinate descent method is used to refine the contract design and complemented by a generative diffusion model to improve the efficiency of searching for contracts. During the deployment process, the study emphasizes the positioning of UAVs to maximize SP effectiveness. An improved differential evolutionary algorithm is introduced to optimize the positioning of UAVs. Extensive evaluation shows our approach has excellent effectiveness and robustness in deterministic and unpredictable scenarios.

关键词： Optical communication   Mobile edge computing   Channel allocation   Task scheduling   Machine learning   5G networks  

摘要： Edge computing enables even low-powered devices to move part of their processing work to the edges of the network, improving service quality for everyone. Though several studies have hinted that offloaded work's data volume might affect resource allocation, no comprehensive study has been undertaken on the issue. In this research, a new optical communication strategy for 5G networks is presented, one that makes use of a machine learning-based routing architecture for channel allocation and job scheduling. Within HDFS (Hadoop data file system) environment for Hadoop processing, the header controls the stimulated control. Edge fog networks communicate through optical tier networks, and resources are managed by a service reinforcement heterogeneous multi-objective architecture. In order to schedule jobs, a multi-node delay proximal strategy is used. This policy uses genetically competing objectives. Throughout the course of the experiment, data on things like throughput, delivery rate, efficiency, end-to-end latency, and collision probability will be collected and analyzed. By implementing the plan, we decreased the probability of a collision by 65%, increased efficiency by 95%, decreased latency to all nodes by 55%, increased throughput by 98%, and increased the delivery rate by 92%.

关键词： Contracts   Backscatter   Wireless communication   Resource management   Autonomous aerial vehicles   Task analysis   Optimization   Mobile edge computing   UAV   contract incentive   energy harvesting   deep reinforcement learning  

摘要： Mobile edge computing (MEC) is viewed as a promising technology to address the challenges of intensive computing demands in hotspots (HSs). In this article, we consider a unmanned aerial vehicle (UAV)-assisted backscattering MEC system. The UAVs can fly from parking aprons to HSs, providing energy to HSs via RF beamforming and collecting data from wireless users in HSs through backscattering. We aim to maximize the long-term utility of all HSs, subject to the stability of the HSs' energy queues. This problem is a joint optimization of the data offloading decision and contract design that should be adaptive to the users' random task demands and the time-varying wireless channel conditions. A deep reinforcement learning based contract incentive (DRLCI) strategy is proposed to solve this problem in two steps. First, we use deep Q-network (DQN) algorithm to update the HSs' offloading decisions according to the changing network environment. Second, to motivate the UAVs to participate in resource sharing, a contract specific to each type of UAVs has been designed, utilizing Lagrangian multiplier method to approach the optimal contract. Simulation results show the feasibility and efficiency of the proposed strategy, demonstrating a better performance than the natural DQN and Double-DQN algorithms.