关键词： Vehicular networks   mobile edge computing   task collaborative offloading   BES algorithm   computation offloading  

摘要： To reduce computing delay and energy consumption in the Vehicular networks, the total cost of task offloading, namely delay and energy consumption, is studied. A task offloading model combining local vehicle computing, MEC (Mobile Edge Computing) server computing, and cloud computing is proposed. The model not only considers the priority relationship of tasks, but also considers the delay and energy consumption of the system. A computational offloading decision method IBES based on an improved bald eagle search optimization algorithm is designed, which introduces Tent chaotic mapping, Levy Flight mechanism and Adaptive weights into the bald eagle search optimization algorithm to increase initial population diversity, enhance local search and global convergence. The simulation results show that the total cost of IBES is 33.07% and 22.73% lower than that of PSO and BES, respectively.

关键词： NOMA   Autonomous aerial vehicles   Decoding   Channel estimation   Task analysis   Resource management   Wireless communication   Mobile edge computing   non-orthogonal multiple access   UAV   reconfigurable intelligence surface  

摘要： In the Internet-of-Things scenarios, unmanned aerial vehicle (UAV), as a popular aerial platform, is calling for ever-increasing computing support. This letter proposes a novel mobile edge computing (MEC) framework for UAV with the assistance of the reconfigurable intelligence surface (RIS), where a UAV offloads the computation tasks to ground access points (APs) with the assistance of an RIS, during which non-orthogonal multiple access (NOMA) scheme is employed. We maximize the UAV's computation capacity by jointly optimizing the reflecting phase shift, communication and computation (2C) resource allocation, decoding order, and UAV's deployment. Specifically, we first derive the reflecting phase shift by invoking the concave-convex procedure (CCCP) method and the semidefinite relaxation technique. Next, we obtain the 2C resource allocation by using the CCCP method. The decoding order and the UAV's deployment are finally solved via proposing a grid search (GS) method. Numerical results demonstrate that: 1) the computation capacity is greatly improved by the design of RIS;2) NOMA scheme outperforms orthogonal multiple access scheme;3) the proposed GS method achieves significant performance gains, as compared with the traditional convex approximation method.

关键词： UAV communications   Communications security   Reverse auction   Mobile edge computing   Data offloading   Jamming  

摘要： In recent years, the security of wireless communication has received more and more attention. However, with the increasing requirements for security performance in the process of wireless communication, the cost of improving security also increases. Therefore, how to improve the security of wireless communication while reducing costs has become a serious challenge. Aiming at the security problems in the UAV-assisted wireless communication link based on the premise of low cost and high profit, this paper studies how to motivate non-offloading users to send jamming power to interfere with eavesdropping UAVs, so efficiently and economically to improve the security performance of wireless communication link. Firstly, the relationship between the security performance of the communication link and the interference power is revealed through the theoretical model. Then, we propose a scenario consisting of a legitimate UAV, a ground user group, and an eavesdrop UAV. In this scenario, we divide the ground users into offloading users and non-offloading users. At the same time, the cost functions for offloading users and utility functions for non-offloading users are defined, and non-offloading users are encouraged to help to offload users unload data safely to a legitimate UAV by obtaining rewards for offloading users. Considering the selfishness of users, a reverse auction algorithm based on multi-user groups is proposed to achieve the optimal mapping between non-offloading users and offloading users. Finally, the effectiveness of the proposed scheme is verified by a large number of simulations. The research results show that the scheme innovative designs a method for users to cooperate to interfere with eavesdroppers, which can reduce costs and improve utility while maximizing the improvement of wireless communication link security.(c) 2022 Elsevier B.V. All rights reserved.

关键词： mobile edge computing   collaboration   task offloading   service caching   resource allocation   fairness   load balance  

摘要： Mobile edge computing (MEC), which sinks the functions of cloud servers, has become an emerging paradigm to solve the contradiction between delay-sensitive tasks and resource-constrained terminals. Task offloading assisted by service caching in a collaborative manner can reduce delay and balance the edge load in MEC. Due to the limited storage resources of edge servers, it is a significant issue to develop a dynamical service caching strategy according to the actual variable user demands in task offloading. Therefore, this paper investigates the collaborative task offloading problem assisted by a dynamical caching strategy in MEC. Furthermore, a two-level computing strategy called joint task offloading and service caching (JTOSC) is proposed to solve the optimized problem. The outer layer in JTOSC iteratively updates the service caching decisions based on the Gibbs sampling. The inner layer in JTOSC adopts the fairness-aware allocation algorithm and the offloading revenue preference-based bilateral matching algorithm to get a great computing resource allocation and task offloading scheme. The simulation results indicate that the proposed strategy outperforms the other four comparison strategies in terms of maximum offloading delay, service cache hit rate, and edge load balance.

关键词： Computation offloading   Mobile edge computing   Code analysis   IoT application  

摘要： Mobile Edge Computing (MEC) provides a new opportunity to reduce the latency of IoT applications significantly. It does so by offloading computation-intensive tasks in applications from IoT devices to mobile edges, which are located N-close proximity to the IoT devices. However, the prior researches focus on supporting computation offloading for a specific type of applications. Meanwhile, making multi-task and multi-server offloading decisions in highly complex and dynamic MEC environments remains intractable. To address this problem, this paper proposes a novel approach called MultiOff. First, we propose a generic program structure that supports on-demand computation offloading. Applications conforming to this structure can extract the flowcharts of program fragments via code analysis. Second, a novel cost-efficient offloading strategy based on a Multi-task Particle Swarm Optimization algorithm using the Genetic Algorithm operators (MPSO-GA) is proposed. MPSO-GA makes offloading decisions by analyzing program fragment flowcharts and context. Finally, each application can be offloaded at the granularity of services with the offloading scheme, minimizing the system cost while satisfying the deadline constraint for each application. We evaluate MultiOff on several real-world applications and the experimental results show that MultiOff can support computation offloading for different types of applications at the fine-grained granularity of services. Moreover, MPSO-GA can save about 2.11-17.51% system cost compared with other classical methods while meeting time constraints.

关键词： 5G   Mobile edge computing   Graph embedding   Blockchain   Stackelberg game   Resource management  

摘要： In the 5G scenario of the convergence of information technology (IT) and communication technology (CT), multi-operators collaborate to form edge computing, which makes the problem of resource optimization more complicated than ever. Users may access resources deployed by various MEC's operators to achieve ultra-low latency. However, traditional resource management methods consider only a single operator failure to handle profit allocation and privacy security issues among different operators. To address this problem, we proposed a resource management framework named GBRM based on graph embedding and blockchain. Specifically, we use the Stackelberg game model to solve MEC servers' cache-offloading problem;non-indexed content sharing by Deepwalk graph embedding between MECs ensures the privacy of different operators' content. Consortium blockchain assists in the trusted profit allocation of services across various operators. Experiments show in the virtual network scenario that our work performance is significantly better than the RandomSelect and the LocalIndex method in global latency and close to the global index's ideal situation. Multi-operators collaborate to form edge computing, which makes the problem of resource optimization more complicated than ever.

关键词： DNN service migration   Multi-exit DNN   Service downtime   Mobile edge computing   Model predictive control  

摘要： Edge intelligence (EI) becomes a trend to push the deep learning frontiers to the network edge, so that deep neural networks (DNNs) applications can be well leveraged at resource-constrained mobile devices with benefits of edge computing. Due to the high user mobility among scattered edge servers in many scenarios such as internet of vehicular applications, dynamic service migration is desired to maintain a reliable and efficient quality of service (QoS). However, inevitable service downtime incurred by service migration would largely degrade the real-time performance of delay-sensitive DNN inference services. To address this issue, we advocate a user-centric management for dynamic DNN inference service migration with flexible multi-exit mechanism, aiming at maximizing overall user utility (e.g., DNN model inference accuracy) with various service downtime. We first leverage dynamic programming to propose an optimal offline migration and exit point selection strategy (OMEPS) algorithm when complete future information of user behaviors is available. Amenable to a more practical application domain without complete future information, we incorporate the OMEPS algorithm into a model predictive control (MPC) framework, then construct a mobility-aware service migration and DNN exit point selection (MOMEPS) algorithm, which improves the long-term user utility within limited predictive future information. However, heavy computation overheads of MOMEPS algorithm impose burdens on mobile devices, thus we further advocate a cost-efficient algorithm, named smart-MOMEPS, which introduces a smart migration judgement based on Neural Networks to control the implementation of (MOMEPS) algorithm by wisely estimating whether the DNN service should be migrated or not. Extensive trace-driven simulation results demonstrate the superior performance of our smart-MOMEPS algorithm for achieving significant overall utility improvements with low computation overheads compared with other online

关键词： workflow scheduling   energy optimization   computing offload   mobile edge computing   multiple resources  

摘要： The energy consumption optimization of edge devices in the mobile edge computing environment is mainly based on computational offload strategy. Most of the current common computing offload strategies only consider a single computing resource and do not comprehensively consider different kinds of computing resources in mobile edge computing environments, which cannot fully reduce the energy consumption of edge devices under the condition of ensuring response time constraints. To solve this problem, a multi-resource computing unloading energy consumption model is proposed in the mobile edge computing environment, and a new fitness calculation method for evaluating the energy consumption of edge devices is designed. Combined with the workflow management system, a multi-resource computing offloading particle swarm optimization task scheduling algorithm for energy consumption optimization in mobile edge computing is proposed. The algorithm can fully reduce the energy consumption of mobile terminals under the condition of considering the response time constraint. Experiments show that, compared with the existing four algorithms, the task scheduling algorithm corresponding to the new strategy has stable convergence and optimal fitness. Under the constraint of user response time, the energy consumption of edge devices in the task scheduling scheme is better than the other four unloading strategies.

关键词： Internet of things   Mobile edge computing   Microservice   Edge caching   Deep reinforcement learning  

摘要： The ubiquitous Internet of Things (IoTs) devices spawn growing mobile services of applications with computationally-intensive and latency-sensitive features, which increases the data traffic sharply. Driven by container technology, microservice is emerged with flexibility and scalability by decomposing one service into several independent lightweight parts. To improve the quality of service (QoS) and alleviate the burden of the core network, caching microservices at the edge of networks empowered by the mobile edge computing (MEC) paradigm is envisioned as a promising approach. However, considering the stochastic retrieval requests of IoT devices and time-varying network topology, it brings challenges for IoT devices to decide the caching node selection and microservice replacement independently without complete information of dynamic environments. In light of this, a MEC-enabled di stributed cooperative m icroservice ca ching scheme, named DIMA, is proposed in this paper. Specifically, the microservice caching problem is modeled as a Markov decision process (MDP) to optimize the fetching delay and hit ratio. Moreover, a distributed double dueling deep Q-network (D3QN) based algorithm is proposed, by integrating double DQN and dueling DQN, to solve the formulated MDP, where each IoT device performs actions independently in a decentralized mode. Finally, extensive experimental results are demonstrated that the DIMA is well-performed and more effective than existing baseline schemes.

关键词： Mobile edge computing   Federated learning   Systems and statistical heterogeneity   Convergence speed   Adaptive workload assignment  

摘要： With rapid advancement of Internet of Things (IoT) and social networking applications generating large amounts of data at or close to the network edge, Mobile Edge Computing (MEC) has naturally been proposed to bring model training closer to where data is produced. However, there still exists privacy concern since typical MEC frameworks need to transfer sensitive data resources from data collection end devices/clients to MEC server. So the concept of Federated Learning (FL) has been introduced which supports privacy-preserved collaborative machine learning involving multiple clients coordinated by the MEC server without centralizing the private data. Unfortunately, FL is prone to multiple challenges: 1) systems heterogeneity between clients causes straggler issue, and 2) statistical heterogeneity between clients brings about objective inconsistency problem, both of which may lead to a significant slow-down in the convergence speed in heterogeneous MEC environment. In this paper, we propose a novel framework, FedAda (Federated Adaptive Training), that incorporates systems capabilities and data characteristics of the clients to adaptively assign appropriate workload to each client. The key idea is that instead of running a fixed number of local training iterations as in Federated Averaging (FedAvg), our algorithm adopts an adaptive workload assignment strategy by minimizing the runtime gap between clients and maximizing convergence gain in heterogeneous MEC environment. Moreover, we design a light mechanism extending FedAda to accelerate the convergence speed by further fine-tuning the workload assignment based on the global convergence status in each communication round. We evaluate FedAda on CIFAR-10 dataset to explore the performance of the algorithm in the simulated heterogeneous MEC environment. Experimental results show that FedAda is able to assign appropriate amount of workload to each client and substantially reduces the convergence time by up to 49.5% compar