关键词： Backscatter communication   Computation bits   Mobile edge computing   Wireless power transfer  

摘要： In the field of Internet-of-Things (IoT), how to enhance the computation capacity and prolong the life span of massive smart IoT nodes is becoming a more practically important but challenging problem. In this paper, we propose a backscatter-assisted wireless-powered non-orthogonal multiple access (NOMA)-mobile edge computing (MEC) network, where IoT nodes harvest energy from signals transmitted by a dedicated energy source and then offload data to the MEC server via backscattering and uplink NOMA. To exploit its potential performance, we formulate a non-convex problem to maximize the total computation bits of all the IoT nodes by jointly optimizing the communication resource of each IoT node and the computation resource of both the IoT nodes and the MEC server. By introducing a slack variable and a series of auxiliary variables as well as using the contradiction and inequality transformation approaches, we transform the original non-convex problem into a convex one and devise an iterative algorithm to optimally solve it. Simulation results are provided to demonstrate the quick convergence of the proposed iterative algorithm and to confirm the superiority of the proposed scheme over the existing benchmark schemes.

关键词： Mobile edge computing  

摘要： In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and bound (BnB) approach can be applied to solve this problem, a huge burden of computational complexity arises which limits the application of BnB. To address this issue, we propose an intelligent BnB (IBnB) approach which applies deep learning (DL) to learn the pruning strategy of the BnB approach. By using this learning scheme, the structure of the BnB approach ensures near-optimal performance and meanwhile DL-based pruning strategy significantly reduces the complexity. Numerical results verify that the proposed IBnB approach achieves optimal performance with complexity reduced by over 80%. Copyright © 2022, The Authors. All rights reserved.

关键词： Authentication   Authentication protocol   Mutual authentication   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) accommodates processing and data storage and manipulation capabilities across the scope of wireless network. In MEC environment, MEC servers along with the computing and storage capabilities are distributed at the edge of the network. However, due to the broad range of wireless communication, the fulfillment of security requirements still remain a challenging task in the for MEC environment. With the expeditious traffic expansion and growing end user requirements, the classic security protocols cannot encounter the innovative requirements of lightweightness and real-time communication. To meet these requirements, we have proposed an authentication protocol for the MEC environment. Our proposed protocol stipulates secure and efficient communication for all of the intended entities. Meanwhile, during its execution user anonymity remains intact. Moreover, our protocol is proven to be secure under the assumptions of formal security model. Additionally in this article, we have described the security properties of our protocol that it offers resistance against impersonation, session key computation and forward and backward secrecy attacks. The comparative analysis of time consumption and computation overheads are presented at the end of the paper, which is an evidence that our proposed protocol outperforms prior to various existing MEC protocols.

关键词： Mobile cloud gaming   Online provisioning   Mobile edge computing   QoE impairment  

摘要： Mobile cloud gaming (MCG), which is proposed to deliver high-quality gaming experience to users anywhere and anytime, suffers from tremendous wide-area traffic and long network delays. In order to shorten the delays and provide the gaming services in close proximity to end users, the mobile edge computing (MEC) is envisioned as a promising approach to enable related computations at the edge of the network. Unfortunately, the performance of MEC-enabled MCG highly depends on the provisioning of both gaming services and bandwidth, since the quality of gaming experience (QoE) is easily impaired by both delays and video frame rates. Furthermore, due to the erratic mobility of users, migrating the services accordingly actually decreases the QoE impairment, but incurs extra system cost, leading to the performance-cost tradeoff. In order to address these challenges, we jointly investigate adaptive service placement and bandwidth allocation for MEC-enabled MCG. Considering the system dynamics, we propose to minimize the QoE impairment in a long-term scope, under the constrained cost for migrations. We design an online two-layer iterative algorithm (OnTrial) to solve the problem. Theoretical analysis demonstrates that OnTrial achieves a near-optimal performance and the potential violation of the migration constraint is bounded. Extensive experiments with real-world traces confirm that OnTrial outperforms other algorithms regarding the long-term QoE impairment for games.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) provides physical resources closer to end users, becoming a good complement to cloud computing. The booming MEC brings many multiobjective optimization problems. The paper proposes a multiobjective optimization (MOO) algorithm called SAMOACO(MV), which provides a new choice for solving MOO problems of MEC. We improve the ACO(MV) algorithm that is only suitable for solving mixed-variable single-objective optimization (SOO) problems and propose a MOACO(MV) algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACO(MV) algorithm performance on parameter setting, we proposed the SAMOACO(MV) algorithm using a self-adaptive parameter setting scheme. Furthermore, the paper also designs some mixed-variable MOO benchmark problems for the purpose to test and compare the performance of the SAMOACO(MV) algorithm. The experiments indicate that the SAMOACO(MV) algorithm has excellent comprehensive performance and is an ideal choice for solving mixed-variable MOO problems.

关键词： Artificial intelligence   mobile edge computing   unmanned aerial vehicles   deep learning   optimization  

摘要： Recent trends in communication technologies and unmanned aerial vehicles(UAVs)find its application in several areas such as healthcare,surveillance,transportation,***,the integration of Internet of things(IoT)with cloud computing environment offers several benefits for the UAV *** the same time,aerial scene classification is one of the major research areas in UAV-enabledMEC *** UAV aerial imagery,efficient image representation is crucial for the purpose of scene *** existing scene classification techniques generate mid-level image features with limited representation capabilities that often end up in producing average ***,the current research work introduces a new DL-enabled aerial scene classificationmodel *** presented model enables theUAVs to capture aerial imageswhich are then transmitted to MEC for further ***,CapsuleNetwork(CapsNet)-based feature extraction technique is applied to derive a set of useful feature vectors from the aerial *** is important to have an appropriate hyperparameter tuning strategy,since manual parameter tuning of DL model tend to produce several configuration *** order to achieve this and to determine the hyperparameters of CapsNetmodel,Shuffled Shepherd Optimization(SSO)algorithm is ***,Backpropagation Neural Network(BPNN)classification model is applied to determine the appropriate class labels of aerial *** performance of SSO-CapsNet model was validated against two openly-accessible datasets namely,UC Merced(UCM)Land Use dataset andWHU-RS *** proposed SSO-CapsNet model outperformed the existing state-of-the-art methods and achieved maximum accuracy of 0.983,precision of 0.985,recall of 0.982,and F-score of 0.983.

关键词： mobile edge computing   probabilistic offloading strategy   multi-source fluid queue   birth-and-death process   cumulative distribution function  

摘要： Mobile edge computing (MEC) is one of the key technologies to achieve high bandwidth, low latency and reliable service in fifth generation (5G) networks. In order to better evaluate the performance of the probabilistic offloading strategy in a MEC system, we give a modeling method to capture the stochastic behavior of tasks based on a multi-source fluid queue. Considering multiple mobile devices (MDs) in a MEC system, we build a multi-source fluid queue to model the tasks offloaded to the MEC server. We give an approach to analyze the fluid queue driven by multiple independent heterogeneous finite-state birth-and-death processes (BDPs) and present the cumulative distribution function (CDF) of the edge buffer content. Then, we evaluate the performance measures in terms of the utilization of the MEC server, the expected edge buffer content and the average response time of a task. Finally, we provide numerical results with some analysis to illustrate the feasibility of the stochastic model built in this paper.

关键词： multi-UAV cooperation   mobile edge computing   Internet-of-Things   UAV assistance  

摘要： Introducing multi-UAV network with flexible deployment into mobile edge computing(MEC)can effectively improve the quality of service of Internet-of-Things services,reduce the coverage cost and resource waste rate of edge nodes,and also bring some *** paper first introduces the current situation and pain points of mobile edge computing,then analyzes the significance and value of using multi-UAV network to assist mobile edge computing,and summarizes its key technologies and typical *** the end,some open research problems and technology prospects of multi-UAV network assisted intelligent edge computing are put forward,which provide new ideas for the future development of this field.

关键词： Mobile edge computing   Edge caching   Machine learning   Mobility prediction   Interest prediction  

摘要： With the rapid development of 5G mobile networks, data traffic has increased dramatically and is putting tremendous pressure on the backhaul link. In 5G-based mobile edge computing (MEC) environment, efficient caching at the edge of the network provides a solution for satisfying the quality of experience (QoE) requirements for lower latency. An intelligent caching strategy for MEC based on machine learning has been proposed, namely EICache, which considers the user's mobility and interest preferences. It could predict user's mobility using historical trajectory based on Long Short-Term Memory (LSTM) algorithm, and predict interest using Gradient Boosting Decision Tree (GBDT) method, to obtain the content of interest in advance, and then cache the content in advance on the neighboring edge node where the user is likely to go. Performance evaluations have been conducted using public YouTube trending video datasets from Kaggle and real trajectory datasets, compared with different cache replacement methods. The metrics of the cache hit rate, and the overall request latency are used for evaluation. By training the datasets first and then predicting, the accuracy of LSTM-based location prediction is about 80%, and the accuracy of GBDT-based interest prediction reaches about 25.4%. The hit rate of the edge caching strategy is increased by 40.5% compared with the strategy of random caching without any predictions. The results have proved the efficiency of EICache, which could meet the user's QoE requirements of low request latency.

关键词： mobile edge computing   computing offloading   scheduling   dependent tasks  

摘要： Recent years have witnessed a paradigm shift from centralized cloud computing to decentralized edge computing. As a key enabler technique in edge computing, computation offloading migrates computation-intensive tasks from resource-limited devices to nearby devices, optimizing service latency and energy consumption. In this paper, we investigate the problem of offloading task graphs in edge computing scenarios. Previous work based on list-scheduling heuristics is likely to suffer from severe processor time wastage due to intricate task dependencies and data transfer requirements. To this end, we propose a novel offloading algorithm, referred to as Iterative Dynamic Critical Path Scheduling (IDCP). IDCP minimizes the makespan by iteratively migrating tasks to keep shortening the dynamic critical path. Through IDCP, what is managed are essentially the sequences among tasks, including task dependencies and scheduled sequences on processors. Since we only schedule sequences here, the actual start time of each task is not fixed during the scheduling process, which effectively helps to avoid unfavorable schedules. Such flexibilities also offer us much space for continuous scheduling optimizations. Our experimental results show that our algorithm significantly outperforms existing list-scheduling heuristics in various scenarios, which demonstrates the effectiveness and competitiveness of our algorithm.