关键词： Servers   Resource management   Task analysis   Delays   Computational modeling   Base stations   Edge computing   Vehicular networks   mobile edge computing   reinforcement learning   computation migration  

摘要： With the development of 5G technology, the requirements for data communication and computation in emerging 5G-enabled vehicular networks are becoming increasingly stringent. Computation-intensive or delay-sensitive tasks generated by vehicles need to be processed in real time. Mobile edge computing (MEC) is an appropriate solution. Wireless users or vehicles can offload computation tasks to the MEC server due to it has strong computation ability and is closer to the wireless users or vehicles. However, the communication and computation resources of the single MEC are not sufficient for executing the continuously generated computation-intensive or delay-sensitive tasks. We consider migrating computation tasks to other MEC servers to reduce the computation and communication pressure on current MEC server. In this article, we construct an MEC-based computation offloading framework for vehicular networks, which considers time-varying channel states and stochastically arriving computation tasks. To minimize the total cost of the proposed MEC framework, which consists of the delay cost, energy computation cost, and bandwidth cost, we propose a deep reinforcement learning-based computation migration and resource allocation (RLCMRA) scheme that requires no prior knowledge. The RLCMRA algorithm can obtain the optimal offloading and migration policy by adaptive learning to maximize the average cumulative reward (minimize the total cost). Extensive numerical results show that the proposed RLCMRA algorithm can adaptively learn the optimal policy and outperform four other baseline algorithms.

关键词： Computation offloading   Internet of Things   mobile edge computing   task scheduling   vehicular networks  

摘要： Resource-intensive applications on smart vehicles is posing difficulties to the use of traditional cloud computing for computation offloading in vehicular networks. In particular, the long transmission distance between the vehicles and the cloud center can cause high latency and poor reliability which may degrade application performance and quality of service. As an integration of mobile edge computing and vehicular networks, vehicular edge computing is a promising paradigm that aims to improve vehicular services by performing computation offloading in close proximity to vehicles. In this paper, the task offloading algorithm that efficiently optimizes task delay and computing resource consumption in multi-user, multi-server vehicular edge computing scenarios is studied. The offloading algorithm not only determines where the tasks are performed, but also indicates the execution order of the tasks on the server. In order to reduce the time complexity, this paper proposes a hybrid intelligent optimization algorithm based on partheno genetic algorithm and heuristic rules. Extensive simulations are conducted, and the results show that compared with the baseline algorithms, the proposed algorithm effectively improves the offloading utility of the VEC system and is suitable for task offloading in various situations.

关键词： Machine type communication   mobile edge computing   computation offloading   deep reinforcement learning   energy efficiency  

摘要： Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12 & x0025;in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically.

关键词： Mobile edge computing  

摘要： Classification of benign and malignant pulmonary nodules is a critical task for developing a Computer-Aided Diagnosis (CAD) system of lung cancer. However, the intelligent diagnosis technology is often limited by equipment and space. Therefore, a computer-aided diagnosis model is proposed running in the Mobile Edge Computing (MEC) environment. The novel lung nodule classification framework improved the Deep Convolutional Generative Adversarial Nets (DCGAN). Firstly, CT images after pre-processing are input into GAN to generate new images with similar features. Then, in the training stage, the derivative model of GAN is introduced into the classification of pulmonary nodules. The optimised function means the improved DCGAN has better anti-noise ability and achieves more accurate classification performance. The experimental results showed an accuracy of 88.88%. Compared with the existing methods, the proposed method is superior to other ones in terms of accuracy, sensitivity, specificity, and area under the ROC curve. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Mobile edge computing   unmanned aerial vehicle   computation offloading   deep reinforcement learning  

摘要： Users in heterogeneous wireless networks may generate massive amounts of data that are delay-sensitive or require computation-intensive processing. Owing to computation ability and battery capacity limitations, wireless users (WUs) cannot easily process such data in a timely manner, and mobile edge computing (MEC) is increasingly being used to resolve this issue. Specifically, data generated by WUs can be offloaded to the MEC server for processing, which has greater computing power than WUs. However, as the location of MEC servers is fixed, unmanned aerial vehicles (UAVs) have been considered a promising solution in heterogeneous wireless networks. In this study, we design an UAV-assisted computation offloading scheme in an MEC framework with renewable power supply. The proposed model considers the instability of energy arrival, stochastic computation tasks generated by WUs, and a time-varying channel state. Owing to the complexity of the state, it is difficult to use traditional Markov decision process (MDP) with complete prior knowledge for offloading optimization. Accordingly, we propose UAV-assisted computation offloading for MEC based on deep reinforcement learning (UACODRL) to minimize the total cost, which is the weighted sum of the delay, energy consumption, and bandwidth cost. We first use the K-Means algorithm for classification to reduce the dimension of the action space. Subsequently, we use UACODRL to find the near-optimal offloading scheme to minimize the total cost. Simulations demonstrate that UACODRL converges satisfactorily and performs better than four baseline schemes with different parameter configurations.

关键词： Mobile edge computing   task offloading   wireless power transfer   multi-agent   deep reinforcement learning  

摘要： Combined with wireless power transfer (WPT) technology, mobile edge computing can provide continuous energy supply and computing resources for mobile devices, and improve their battery life and business application scenarios. This article first designs the mobile edge computing (MEC) model of mobile devices with random mobility and hybrid access point (HAP) with data transmission and energy transmission. On this basis, the selection of target server and the amount of data offloading are taken as the learning objectives, and the task offloading strategy based on multi-agent deep reinforcement learning is constructed. Then combined with MADDPG algorithm and SAC algorithm, the problems of multi-agent environment instability and the difficulty of convergence are solved. The final experimental results show that the improved algorithm based on MADDPG and SAC has good stability and convergence. Compared with other algorithms, it has achieved good results in energy consumption, delay and task failure rate.

关键词： Cloud computing   Job shop scheduling   Delays   Processor scheduling   Optimization   Servers   Service function chain   mobile edge computing   scheduling optimization   deep reinforcement learning  

摘要： MEC (Mobile Edge Computing) provides both IT service environment and cloud computation on the edge of the network. This technology not only minimizes the end-to-end latency but also increases the efficiency of computing. Some latency-sensitive applications, such as cloud video, online game, and augmented reality, take advantage of the MEC system to provide fast and stable services. Several new network techniques, including the implementation of NFV (Network Function Virtualization), the placement of VNF (Virtual Network Function) and the scheduling of SFC (Service Function Chain), should be considered to be applied in the MEC system. In this paper, we focus on the research about the scheduling of SFC in the NFV enabled MEC system and propose a solution accordingly. First, we make reasonable assumptions on the settings of MEC systems and model the SFC scheduling problem into a flexible job-shop scheduling problem. Since minimizing the latency can significantly improve the quality of service (QoS) and increase the revenue of Internet Service Providers, our optimization goal is to minimize the overall scheduling latency. To solve this optimization problem, a deep reinforcement learning based algorithm DQS is proposed. DQS can detect the variation of the MEC system's environment and perform adaptive scheduling for SFC requests. As the results of the simulation indicate, DQS works better than the other off-the-shelf algorithms in two key indexes: overall scheduling latency and average resource usage. Moreover, DQS can shorten the decision time and schedule SFCs stably with high performance. It is suitable to be extended to an online scheduling algorithm.

关键词： IoT security   mobile edge computing   image steganography   evolutionary multi-objective optimization   artificial immune  

摘要： Mobile edge computing provides high computing power, data storage capacity and bandwidth requirements for Internet of Things (IoT) through edge servers that process data close to data sources or users. In practical, mobile edge computing can be used to implement image steganography in IoT. Considering imperceptibility, security and capacity are important indicators for image steganography, this paper propose an image steganography based on evolutionary multi-objective optimization (EMOsteg). The EMOsteg preprocesses the image through a high-pass filters bank to find noise and texture regions that are difficult to model. By perturbing the image on noise and texture regions in multiple directions, the embedded capacity is increased. By defining the imperceptibility and security as an antigen, defining the perturbation positions of the cover image as an antibody, the EMOsteg uses the artificial immune principle to heuristically obtain the perturbation population through feature extraction of the perturbation and adaptive evolution operations. And the Pareto optimal is used to find the optimal perturbation in the last generation population. The simulation experiments analyze the convergence of the algorithm and the diversity of the solutions. In simulation experiments, the MSE, PSNR and SSIM were adopted to evaluate the imperceptibility, and the results show that the MSE value of our algorithm is 0.000308, the PSNR is 82.7501 and the SSIM approaches 1, they are better than comparison algorithms. The average detection error P-E under SPA was adopted to detect the security, and the results show that our algorithm is more robust against anti-SPA steganalysis. In order to evaluate the performance of real-time, the embedding time of the same secret under different algorithms were compared, and the results show that our algorithm is faster than comparison algorithms in the terminal. In summary, the proposed algorithm can maintain the image quality while resist steganalysis to

关键词： Task analysis   Resource management   Edge computing   Path planning   Optimization   Processor scheduling   Mobile computing   Mobile edge computing   vehicles   computation task offloading   path planning   piecewise linear approximation  

摘要： With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the near-optimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity L-step lookahead branch scheme (referred to as L-step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed L-step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

关键词： Task analysis   Internet of Things   Servers   Security   Delays   Energy consumption   Cloud computing   Caching   energy   IoT   mobile edge computing   security  

摘要： The ever-increasing number of diverse and computation-intensive Internet of things (IoT) applications is bringing phenomenal growth in global Internet traffic. Mobile devices with limited resource capacity (i.e., computation and storage resources) and battery lifetime are experiencing technical challenges to satisfy the task requirements. Mobile edge computing (MEC) integrated with IoT applications offloads computation-intensive tasks to the MEC servers at the network edge. This technique shows remarkable potential in reducing energy consumption and delay. Furthermore, caching popular task input data at the edge servers reduces duplicate content transmission, which eventually saves associated energy and time. However, the offloaded tasks are exposed to multiple users and vulnerable to malicious attacks and eavesdropping. Therefore, the assignment of security services to the offloaded tasks is a major requirement to ensure confidentiality and privacy. In this article, we propose a green and secure MEC technique combining caching, cooperative task offloading, and security service assignment for IoT networks. The study not only investigates the synergy between energy and security issues, but also offloads IoT tasks to the edge servers without violating delay requirements. A resource-constrained optimization model is formulated, which minimizes the overall cost combining energy consumption and probable security-breach cost. We also develop a two-stage heuristic algorithm and find an acceptable solution in polynomial time. Simulation results prove that the proposed technique achieves notable improvement over other existing strategies.