关键词： Mobile edge computing   cloud offloading   delay   task drop   reinforcement learning   mobile-X architecture  

摘要： Mobile Edge Computing(MEC)assists clouds to handle enormous tasks from mobile devices in close *** edge servers are not allocated efficiently according to the dynamic nature of the *** leads to processing delay,and the tasks are dropped due to time *** researchersfind it difficult and complex to determine the offloading decision because of uncertain load dynamic condition over the edge *** challenge relies on the offload-ing decision on selection of edge nodes for offloading in a centralized *** study focuses on minimizing task-processing time while simultaneously increasing the success rate of service provided by edge ***,a task-offloading problem needs to be formulated based on the communication and *** offloading decision problem is solved by deep analysis on taskflow in the network and feedback from the devices on edge *** significance of the model is improved with the modelling of Deep Mobile-X architecture and bi-directional Long Short Term Memory(b-LSTM).The simulation is done in the Edgecloudsim environment,and the outcomes show the significance of the proposed *** processing time of the anticipated model is 6.6 *** following perfor-mance metrics,improved server utilization,the ratio of the dropped task,and number of offloading tasks are evaluated and compared with existing learning *** proposed model shows a better trade-off compared to existing approaches.

关键词： Mobile edge computing   task offloading   dependent tasks   multiple mobile applications  

摘要： With the proliferation of versatile mobile applications, offloading compute-intensive tasks to the MEC/Cloud becomes a dramatic technique due to the limited resources and high user experience requirements at mobile devices. However, most existing works design their task offloading schemes without considering the dependence of tasks and the orchestration of the MEC and Cloud, and thus may limit the system performance. In this paper, we propose a dependent task offloading framework for multiple mobile applications, named COFE, where mobile devices can offload their compute-intensive tasks with dependent constraints to the MEC-Cloud system. It can assign the offloaded tasks to the MEC and Cloud adaptively to improve the user experience. Based on COFE, we formulate the task offloading problem as an average makespan minimization problem, which is proved to be NP-hard. Then, we propose a heuristic ranking-based algorithm to assign the offloaded tasks according to their bottom levels. Theoretical analysis proves the stability of the system under the proposed algorithm and extensive simulations validate that the proposed algorithm can significantly reduce the average makespan and deadline violation probabilities of offloaded applications.

关键词： Internet of Things   NOMA   Task analysis   Millimeter wave communication   Array signal processing   Resource management   Minimization   Hybrid beamforming   massive MIMO   mobile edge computing   resource allocation  

摘要： Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.

关键词： Trajectory   Costs   Buildings   Wireless communication   Clustering algorithms   Trajectory planning   Task analysis   5G   6G networks   mobile edge computing   intelligent reflecting surface   multiunmanned aerial vehicle   distance-based clustering algorithm   differential evolution  

摘要： This article presents a multi-intelligent reflecting surface (IRS)-and multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system for 5G/6G networks. In the studied system, multiple UAVs are integrated for providing services to large-scale user equipment (UEs) with the help of multiple IRSs. This article aims to minimize the overall cost including energy consumption, completion time, and maintenance cost of UAVs by jointly optimizing the trajectories of UAVs and phase shifts of IRSs. When solving this problem, one has to count in mind the deployment of stop points (SPs) of UAVs, and consider the association among UEs, and UAVs (i.e., which UE will send data to which UAV at which SP), the order of SPs, and the phase shifts of IRSs. Therefore, traditional optimization techniques may not solve the above-mentioned problem in an efficient way. To tackle the above-mentioned problem, this article proposes an algorithm called TPaPBA that consists of four phases. The first phase optimizes the SPs' deployment via using a differential evolution algorithm having variable population size. As a result, all the SPs of UAVs can be obtained. Then, the second phase optimizes the association among UEs, SPs, and UAVs. Specifically, TPaPBA first adopts a clustering algorithm to optimize the SPs-UAVs association, and then a close criterion is introduced to optimize UEs-SPs association. Subsequently, third phase adopts a low-complexity greedy algorithm to optimize the order of SPs for UAVs. Finally, the phase shifts of IRSs are optimized to enhance the data rate between UEs and UAVs. The simulation results of TPaPBA on ten instances having UEs ranging from 100 to 1000, reveals that TPaPBA has significantly improved the system performance contribution and outperforms other approaches in terms of reducing the overall cost of UAVs.

关键词： blockchain   D2D   fifth-generation   mobile edge computing   optimization algorithm   the insurance industry  

摘要： Blockchain, a popular technology, remains the decentralized data management framework approved for use by many industries. The application to the insurance industry needs to offer mobility using the wireless network. The wireless network has many limitations to overcome. This paper focuses on such problems and introduces three levels of a solution to the problem. The first level is resolved using the edge computers as storage at the agencies and the partners. The second level of economic operation is solved by introducing a D2D network solution. The third level of high transactions over the network is considered using a two-stage optimization method. The introduced optimization algorithms are simulated, and results are compared with a classical step-by-step calculation method that is not feasible under real-time application. The optimization methods successfully determine the maximum channel rate with the interferences influencing the operation of such a system.

关键词： decision tree pruning   mobile edge computing   Monte Carlo tree search algorithm   unmanned aerial vehicle (UAV)  

摘要： Thanks to the high mobility, unmanned aerial vehicles (UAVs) can quickly help mobile users deploy edge cloud in the environment that lacks basic communication facilities. In order to reduce UAVs' cost of edge computing and improve their energy utilization efficiency, we propose a task offloading scheme of mobile edge computing (MEC). First, a multi-user edge computing model based on the queuing theory model is constructed, and the cost function of the MEC system is constructed by introducing the waiting probability. Second, the multi-objective optimization problem is transformed into a single objective nonlinear programming problem for solving the offloading problem through weight coefficients. Finally, the Monte Carlo pruning algorithm is used to solve the problem. Specially, the decision pruning algorithm is introduced to reduce the computational complexity. The experimental results show that compared with traditional schemes, the proposed offloading scheme can effectively reduce the cost with a low CPU performance and a high task arrival rate, and provide a low-cost solution of edge computing for UAVs. © 2023 Journal of Nanjing Institute of Posts and Telecommunications. All rights reserved.

关键词： Wavelet analysis   Multiscale model   Medical image   Image denoising   Mobile edge computing   Autonomous unmanned analysis  

摘要： Image is the basis of human vision and the most commonly used information carrier in social activities. In the field of medicine, medical imaging obtains the image of a certain part of tissue in the body. There are many ways of medical imaging and acquisition systems, such as X-ray, magnetic resonance imaging, computed tomography, endoscopy, ultrasound and positron emission tomography and microscopic imaging. Medical image provides a more intuitive visual means, which provides a reliable basis for disease diagnosis, clinical treatment and teaching research. In real life, many different kinds of noise will affect the image collection process, these factors will make the quality of the image to a large extent. In the process of image denoising, white noise is a very common kind of noise. Even a high-resolution image will have some noise, but some simple denoising algorithms can be used for these noises. However, in this era of high resolution, the real-time performance of denoising is not good, and the real-time denoising is still the subject of many studies. In order to enhance the visual sense of the image by filtering out the noise in the image, we need to filter the two-dimensional image, and then make the operation of the image easier. In this paper, we study the real-time medical image denoising and information hiding model based on deep wavelet multiscale autonomous unmanned analysis. The experimental results show that this method can effectively deal with the noise points in the medical image, and the result graph is better.

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   blockchain   stackelberg game   resource allocation  

摘要： Computation-intensive vehicle tasks sharply increase with the rapid growth of intelligent vehicles. The technology of Mobile Edge Computing (MEC) has the possibility of assisting vehicles with computation offloading. To solve the problem of computation resource management and guarantee the security of resource transaction, we jointly combine the MEC network and the blockchain networks to build a blockchain based MEC offloading model. The non-cooperative interactions between MEC server and vehicles formulate a two-stage Stackelberg game in an aim to maximize their benefits and information security. We theoretically demonstrate the unique existence of Nash equilibrium, which enables participants to decide their optimal strategies. Finally, the performance of the proposed model is analyzed by conducting simulation experiments. Our proposed model optimizes resource allocation and also improves the security of the whole network.

关键词： mobile edge computing   multi-terminals offloading   mechanical fault diagnosis   reinforcement learning  

摘要： Artificial intelligence and distributed algorithms have been widely used in mechanical fault diagnosis with the explosive growth of diagnostic data. A novel intelligent fault diagnosis system framework that allows intelligent terminals to offload computational tasks to Mobile edge computing (MEC) servers is provided in this paper, which can effectively address the problems of task processing delays and enhanced computational complexity. As the resources at the MEC and intelligent terminals are limited, performing reasonable resource allocation optimization can improve the performance, especially for a multi-terminals offloading system. In this study, to minimize the task computation delay, we jointly optimize the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection under a dynamic environment with stochastic task arrivals. The challenging dynamic joint optimization problem is formulated as a reinforcement learning (RL) problem, which is designed as the computational offloading policies to minimize the long-term average delay cost. Two deep RL strategies, deep Q-learning network (DQN) and deep deterministic policy gradient (DDPG), are adopted to learn the computational offloading policies adaptively and efficiently. The proposed DQN strategy takes the MEC selection as a unique action while using the convex optimization approach to obtain the local content splitting ratio and the transmission/computation power allocation. Simultaneously, the actions of the DDPG strategy are selected as all dynamic variables, including the local content splitting ratio, the transmission/computation power allocation, and the MEC server selection. Numerical results demonstrate that both proposed strategies perform better than the traditional non-learning schemes. The DDPG strategy outperforms the DQN strategy in all simulation cases exhibiting minimal task computation delay due to its ability to learn all variables online.