关键词： Internet of vehicles   mobile edge computing   resource allocation  

摘要： Benefiting from its abundant computing resources and low computing latency, mobile edge computing (MEC) is a promising approach for enhancing the computing capacity of the 5G Internet of vehicles (IoV). Because of the high mobility, handover is frequent and inevitable in IoV networks. In this paper, we investigate an edge collaborative task offloading and splitting strategy in MEC-enabled IoV networks, in which the task is splitted on the edge and paralleling executed by each part of the task on several MEC servers when handover is occured. Applications in IoV networks have flexible requirements on latency and energy consumption. To realize the tradeoff between latency and energy consumption, we formulate the task offloading and splitting as an optimization problem with the aim of minimizing the total cost of latency and energy consumption by jointly optimizing the task splitting ratio and uplink transmit power of vehicle terminal (VT). Because the proposed problem is non-smooth and non-convex, we divide the original problem into two convex subproblems, and apply an alternate convex search (ACS) algorithm to obtain the optimized solution with low computational complexity. Numerical simulation results show that the proposed method can adjust the offloading strategy properly according to task preference, and obtain a lower total cost compared with the baseline algorithms.

关键词： Task analysis   Resource management   Device-to-device communication   Servers   Jamming   Copper   Wireless networks   Device-to-device   mobile edge computing   physical layer security   deep reinforcement learning  

摘要： This paper investigates secure computation offloading in device-collaborative mobile edge computing (MEC) networks, where mobile users (MUs) can process a part of their tasks locally and offload the other part to the MEC server or other idle paired MUs. All the transmission links are exposed to a potential eavesdropper. To prevent information leakage, artificial noise (AN) is employed and the secrecy capacity is considered. For such a system, we formulate a weighted latency-energy-aware cost minimization problem by jointly optimizing the subchannel allocation, the offloading proportions, the MUs' transmit powers, the MUs' and the MEC server's CPU frequencies, and the AN power while guaranteeing the secrecy capacities of all transmission links. Since the variables are multi-dimensional and coupled, the formulated problem is challenging to solve with the traditional optimization method. We first propose a swap-matching-based algorithm to achieve the optimal subchannel allocation. We then propose a two-layer asynchronous advantage actor-critic (A3C) algorithm to optimize the rest of the resource variables. Specifically, in the inner layer, by utilizing the Karush-Kuhn-Tucker conditions, we derive the optimal CPU frequency with semi-closed-form expressions. In the outer layer, we propose an A3C-based algorithm to achieve near-optimal solutions with fast convergence speed based on the obtained optimal CPU frequency. Simulation results demonstrate that our proposed scheme can decrease the latency-energy-aware cost compared with other learning-based algorithms.

关键词： Mobile edge computing   task offloading   privacy preservation   deep reinforcement learning   computation rate  

摘要： Multi-access Edge Computing (MEC) enables mobile users to run various delay-sensitive applications via offloading computation tasks to MEC servers. However, the location privacy and the usage pattern privacy are disclosed to the untrusted MEC servers. The most related work concerning privacy-preserving offloading schemes in MEC either consider an impractical MEC scenario consisting of a single user or take a large amount of computation and communication cost. In this article, we propose a deep reinforcement learning based joint optimization of delay and privacy preservation during offloading for multiple-user wireless powered MEC systems, preserving users' both location privacy and usage pattern privacy. The main idea is that, to protect both the two kinds of privacy, we propose to disguise users' offloading decisions and deliberately offloading redundant tasks along with the actual tasks to the MEC servers. On this basis, we further formalize the task offloading as an optimization problem of computation rate and privacy preservation. Then, we design a deep reinforcement learning based offloading algorithm to solve such an non-convex problem, aiming to obtain the better tradeoff between the computation rate and the privacy preservation. Finally, extensive simulation results demonstrate that our algorithm can maintain a high level of computation rate while protecting users' usage pattern privacy and location privacy, compared with two learning-based methods and two Baselines.

关键词： Intelligent reflecting surface   Mobile edge computing   Secure offloading   Block coordinate descent   Computation setting   Communication design  

摘要： As an novel paradigm, computation offloading in the mobile edge computing (MEC) system can effectively support the resource-intensive applications for the mobile devices (MD) equipped with limited computing capability. However, the hostile radio transmission and data leakage during the offloading process may erode the MEC system's potential. To tackle these hindrances, we investigate an IRS-assisted secure MEC system with eavesdroppers, where the intelligent reflecting surface (IRS) is deployed to enhance the communication between the MD and the AP equipped with edge servers and the malicious eavesdroppers may attack the wireless data offloaded by MD. The MD opt for offloading part of the tasks to the edge server for execution to support the computation-intensive applications. Moreover, the relevant latency minimization problem is formulated by optimizing the offloading ratio, the allocation of edge server computing capability, the multiple-user-detection (MUD) matrix and the IRS phase shift parameters, subject to the constraints of edge computation resource and practical IRS phase shifts. Then, the original problem is decouple into two subproblem, and the computing and communication subproblems are alternatively optimized by block coordinate descent (BCD) method with low complexity. Finally, simulation results demonstrate that the proposed scheme can significantly enhance the performance of secure offloading in the MEC system.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing  

摘要： Personalized Federated Learning (PFL) is a new Federated Learning (FL) paradigm, particularly tackling the heterogeneity issues brought by various mobile user equipments (UEs) in mobile edge computing (MEC) networks. However, due to the ever-increasing number of UEs and the complicated administrative work it brings, it is desirable to switch the PFL algorithm from its conventional two-layer framework to a multiple-layer one. In this paper, we propose hierarchical PFL (HPFL), an algorithm for deploying PFL over massive MEC networks. The UEs in HPFL are divided into multiple clusters, and the UEs in each cluster forward their local updates to the edge server (ES) synchronously for edge model aggregation, while the ESs forward their edge models to the cloud server semi-asynchronously for global model aggregation. The above training manner leads to a tradeoff between the training loss in each round and the round latency. HPFL combines the objectives of training loss minimization and round latency minimization while jointly determining the optimal bandwidth allocation as well as the ES scheduling policy in the hierarchical learning framework. Extensive experiments verify that HPFL not only guarantees convergence in hierarchical aggregation frameworks but also has advantages in round training loss maximization and round latency minimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing   UAVs hybrid networks   Multi-objective cooperative computation   offloading   Integrated optimization framework  

摘要： With the continuous expansion of the application field of the Internet of Things (IoT), mobile edge computing (MEC) is regarded as a promising technique to reduce the time-delay and energy-consumption of application. However, the conventional MEC infrastructure is lack of flexibility, and failed to meet the different requires of mobile device. UAVs have the distinct features of high scalability and mobility for communications, which can act as the complement of conventional MEC infrastructure. This paper investigates the issues of multi -objective cooperative computation offloading for MEC in UAVs hybrid Networks. The proposed UAVs hybrid MEC system enables edge-cloud and UAVs cooperation to address the flexible limitations of conventional MEC infrastructure and the efficient computation offloading of computation task. To support good-quality services in a cost-effective manner, we model the computation offloading problem as a multi-objective optimization process, and propose an intelligent computation offloading algorithms based on integrated optimization framework, including mixed integer transformation solving framework, improved multi-adaptive MOEA/D-DE(MOEA/D-MSDE) and Grey Relational Projection (GRP). Evaluation results show that the proposed algorithms outperform in solving multi-objective cooperative computation offloading problem in terms of service time-delay, energy-consumption and server-cost.

关键词： Mobile edge computing   Service caching   Task offloading   Resource allocation   Deep reinforcement learning  

摘要： Multiple-Services Mobile Edge Computing enables task-relate services cached in edge server to be dynamically updated, and thus provides great opportunities to offload tasks to edge server for execution. However, the requirements and popularity of services, the computing requirement and the amount of data transferred from users to edge server are dynamic with time. How to adaptively adjust the subset of total service types in the resource-limited edge server and determine the task offloading destination and resource allocation decisions to improve the overall system performance is a challenge problem. To solve this challenge, we firstly convert it into a Markov decision process, then propose a soft actor-critic deep reinforcement learning-based algorithm, called DSOR, to jointly determine not only the discrete decisions of service caching and task offloading but also the continuous allocation of bandwidth and computing resource. To improve the accuracy of our algorithm, we employ an efficient trick of converting the discrete action selection into a continuous space to deal with the key design challenge that arises from continuous-discrete hybrid action space. Additionally, to improve resource utilization, a novel reward function is integrated to our algorithm to speed up the convergence of training while making full use of system resources. Extensive numerical results show that compared with other baseline algorithms, our algorithm can effectively reduce the long-term average completion delay of tasks while accessing excellent performance in terms of stability.

关键词： NOMA   Three-dimensional displays   Servers   Approximation algorithms   Optimization   Mobile computing   Resource management   3D UAV placement   mobile edge computing   MIMO   machine learning  

摘要： This paper investigates an Unmanned Aerial Vehicle (UAV)-enabled network consisting of smart mobile devices and multiple UAVs as aerial base stations in a Multiple-Input Multiple-Output (MIMO) architecture. Mobile devices are partitioned into several clusters and offload their tasks to the UAV servers via the Non-Orthogonal Multiple Access (NOMA) protocol. The main goal of the paper is to jointly maximize the number of served terrestrial users and their scheduling. Moreover, the number of UAV servers and their 3D placement are optimized. To this end, we formulate an optimization problem subject to some Quality of Service (QoS) constraints. The resulting problem is non-convex and intractable to solve. Therefore, we break the problem into two subproblems. We propose an efficient algorithm based on machine learning to solve the first subproblem, i.e., optimizing the number of UAVs and their 3D placements, and the user association. Different from existing literature, our proposed algorithm can achieve low computational complexity and fast convergence. The second subproblem, the user scheduling, is non-convex too. We utilize the l(p)-norm concept to find a convex upper bound for the subproblem and optimize the user scheduling by applying the Successive Convex Approximation (SCA) algorithm. The aforementioned process is performed iteratively until the overall algorithm converges and a near-optimal solution is achieved for the optimization problem. Moreover, the computational complexity of the proposed scheme is analyzed. Finally, we evaluate the performance of our proposed algorithm via the simulation results. Regarding fast convergence and low computational complexity of the proposed algorithm, its superior performance is confirmed through numerical results.

关键词： mobile edge computing   serial task of-floading   computation partitioning   6G edge intelli-gence   I/O interference  

摘要： Driven by the demands of diverse artificial intelligence(AI)-enabled application,Mobile Edge Computing(MEC)is considered one of the key technologies for 6G edge *** this paper,we consider a serial task model and design a quality of service(QoS)-aware task offloading via communication-computation resource coordination for multi-user MEC systems,which can mitigate the I/O interference brought by resource reuse among virtual *** we construct the system utility measuring QoS based on application latency and user devices’energy *** also propose a heuristic offloading algorithm to maximize the system utility function with the constraints of task priority and I/O *** results demonstrate the proposed algorithm’s significant advantages in terms of task completion time,terminal energy consumption and system resource utilization.

关键词： Data caching   mobile edge computing   online algorithm   cost-effective  

摘要： Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its $\mathcal {NP}$NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41% on average.