关键词： Mobile edge computing   APP software   Environment space   Graphic products   Art design  

摘要： The design of a green city needs to improve energy efficiency and urban greening rate and plan public green spaces in places with good natural conditions and reasonable structures. With the rapid development of embedded technology and embedded APP software services, the application range of software maintenance systems has become more and more extensive, and it has also stimulated the increasing demand for embedded software. Improve the efficiency of embedded software development, reduce complexity, cost and development cycle, and apply component-based software development technology. These are very suitable for embedded system development. Component technology is the main form of current software development, which has the characteristics of reusability, packaging and assembly. It also expands Lw CCM, component model and component container. This article analyzes the product graphic design process, and designers choose appropriate design software to improve work efficiency when performing product graphic design. The application of design software in the product design process is mainly according to the specific characteristics of forward design and reverse design. The difference is that a product with a relatively simple structure uses 3D software to design the product in the direct design process, while a product with a relatively complex and cumbersome structure uses plane software to design the product in the reverse design process. Therefore, this article uses mobile edge technology to learn the application of APP software and the art design of graphic products.

关键词： Mobile edge computing   Resource allocation   Internet   Rural economy  

摘要： Currently, the rural economy is developing better and better with the advantages of equality and low cost of Internet platforms. However, inevitably, the rural Internet economy also faces some challenges in its actual development. Entrepreneurship is the main driving force for promoting the development of the national economy and the main method for improving people's living standards and quality. China highly recognizes the positive role of entrepreneurship in regional and even national economic development, and has introduced a number of policies and measures to provide practical support such as financial subsidies for entrepreneurial activities. Mobile edge computing technology not only provides a new economic environment for the information age, but also plays a key role in developing innovation and entrepreneurship and improving business efficiency due to its cross platform and multi-level characteristics. Mobile edge computing technology can process the data generated by resource allocation and provide technical support for resource allocation at the network edge. This paper studies the calculation of big data resource allocation in mobile edge computing systems. Through an analysis of the operation of rural economy based on the Internet, we will deeply explore the advantages that the Internet platform brings to the operation of rural economy. Based on local conditions, the use of Internet resources has strengthened the entrepreneurial capacity of the region, achieved rapid development of the rural economy, strengthened the construction of rural infrastructure, improved the construction of assistance systems, established a business service platform, and introduced relevant incentive mechanisms to ensure the implementation of rural entrepreneurship assistance policies.

关键词： Brain Tumor Classification   Deep Learning Models   Mobile Edge Computing   Resource-Constrained Mobile Devices   Task Offloading  

摘要： The increasing prevalence of brain tumors necessitates accurate and efficient methods for their identification and classification. While deep learning (DL) models have shown promise in this domain, their computational demands pose challenges when deploying them on resource-constrained mobile devices. This paper investigates the potential of Mobile Edge Computing (MEC) and Task Offloading to improve the performance of DL models for brain tumor classification. A comprehensive framework was developed, considering the computational capabilities of mobile devices and edge servers, as well as communication costs associated with task offloading. Various factors influencing task offloading decisions were analyzed, including model size, available resources, and network conditions. Results demonstrate that task offloading effectively reduces the time and energy required to process DL models for brain tumor classification, while maintaining accuracy. The study emphasizes the need to balance computation and communication costs when deciding on task offloading. These findings have significant implications for the development of efficient mobile edge computing systems for medical applications. Leveraging MEC and Task Offloading enables healthcare professionals to utilize DL models for brain tumor classification on resource-constrained mobile devices, ensuring accurate and timely diagnoses. These technological advancements pave the way for more accessible and efficient medical solutions in the future. © 2023, Intellectual Research and Development Education Foundation (YRPI). All rights reserved.

关键词： mobile edge computing   rate-splitting multiple access   time-division multiple access   offloading delay  

摘要： Rate-splitting multiple access (RSMA) has recently received attention due to its benefits in both spectral and energy efficiencies. In this paper, we propose a hybrid RSMA-time-division multiple access (TDMA) scheme for a mobile edge computing (MEC) system, where two edge users need to offload their task data to a MEC server. In the proposed scheme, the offloading time is divided into two time phases. Specifically, we design a cognitive radio (CR)-inspired RSMA scheme, in which two users, namely the primary user and secondary user, offload their task data to the MEC server in the first time phase, while only a single user can offload task data in the second time phase. With the aim of minimizing the overall offloading delay, we formulate the offloading delay minimization problem subject to the transmit power and total energy constraints. We transform the original fractional programming non-convex problem to a convex one by using the Dinkelbach transform and propose Dinkelbach and Newton iterative algorithms to determine the optimal transmit power allocation. Specifically, we establish the optimization criteria for the three offloading schemes and derive the corresponding closed-form expressions for the optimal power allocation. Compared to the existing offloading schemes, the numerical results show that the proposed hybrid RSMA-TDMA scheme in scenarios where having a limited energy budget is superior in offloading delay compared to other offloading schemes and the sum offloading delay tends to a constant with the increase in the energy budget.

关键词： mobile edge computing   vehicular edge computing   offloading strategy   gray-wolf optimization  

摘要： With the development of intelligent transportation and the rapid growth of application data, the tasks of offloading vehicles in vehicle-to-vehicle communication technology are continuously increasing. To further improve the service efficiency of the computing platform, energy-efficient and low-latency mobile-edge-computing (MEC) offloading methods are urgently needed, which can solve the insufficient computing capacity of vehicle terminals. Based on an improved gray-wolf algorithm designed, an adaptive joint offloading strategy for vehicular edge computing is proposed, which does not require cloud-computing support. This strategy first establishes an offloading computing model, which takes task computing delays, computing energy consumption, and MEC server computing resources as constraints;secondly, a system-utility function is designed to transform the offloading problem into a constrained system-utility optimization problem;finally, the optimal solution to the computation offloading problem is obtained based on an improved gray-wolf optimization algorithm. The simulation results show that the proposed strategy can effectively reduce the system delay and the total energy consumption.

关键词： Mobile edge computing  

摘要： The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multitask learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing   Intelligent reflective surface   Task offloading   Multiple resource allocation   Non-orthogonal multiple access  

摘要： Effective resource allocation can exploit the advantage of intelligent reflective surface(IRS)assisted mobile edge computing(MEC)***,it is challenging to balance the limited energy of MTs and the strict delay requirement of their *** this paper,in order to tackle the challenge,we jointly optimize the offloading delay and energy consumption of mobile terminals(MTs)to realize the delay-energy tradeoff in an IRS-assisted MEC network,in which non-orthogonal multiple access(NOMA)and multiantenna are applied to improve spectral *** achieve the optimal delay-energy tradeoff,an offloading cost minimization model is proposed,in which the edge computing resource allocation,signal detecting vector,uplink transmission power,and IRS phase shift coefficient are needed to be jointly *** optimization of the model is a multi-level fractional problem in complex fields with some coupled high dimension *** solve the intractable problem,we decouple the original problem into a computing subproblem and a wireless transmission subproblem based on the uncoupled relationship between different variable *** computing subproblem is proved convex and the closed-form solution is obtained for the edge computing resource ***,the wireless transmission subproblem is solved iteratively through decoupling the residual *** each iteration,the closed-form solution of residual variables is obtained through different successive convex approximation(SCA)*** verify the proposed algorithm can converge to an optimum with polynomial *** results indicate the proposed method achieves average saved costs of 65.64%,11.24%,and 9.49%over three benchmark methods respectively.

关键词： Mobile edge computing   Mobile augmented reality   Deep reinforcement learning   Resource allocation  

摘要： Mobile Edge Computing(MEC)and 5G technology allow clients to access computing resources at the network frontier,which paves the way for applying Mobile Augmented Reality(MAR)*** the MEC paradigm,MAR clients can offload complex tasks to the MEC server and enhance the human perception of the world by merging the received virtual information with the real ***,the resource allocation problem arises as a critical challenge in circumstances where several MAR clients compete for limited resources at the network *** this paper,we aim to design an online resource allocation scheme on the MEC server that takes both high quality of experience and good fairness performance for MAR clients into *** first formulate this problem as a Markov decision process and tackle the challenge of applying the deep reinforcement learning ***,we propose DRAM,a Deep reinforcement learning-based Resource allocation scheme for mobile Augmented reality service in *** also propose a self-adaptive algorithm on the MAR client that is derived based on the analysis of the MAR service to tackle client adaptation *** simulation results demonstrated that DRAM can provide high quality of experience and simultaneously achieve good fairness performance by coordinating with clients’adaptation algorithms.

关键词： Peer-to-peer computing   Wireless communication   Task analysis   Games   Reinforcement learning   Multi-access edge computing   Heuristic algorithms   Mobile edge computing   edge node grouping   game theory   reinforcement learning  

摘要： In mobile edge computing networks, densely deployed access points are empowered with computation and storage capacities. This brings benefits of enlarged edge capacity, ultra-low latency, and reduced backhaul congestion. This paper concerns edge node grouping in mobile edge computing, where multiple edge nodes serve one end user cooperatively to enhance user experience. Most existing studies focus on centralized schemes that have to collect global information and thus induce high overhead. Although some recent studies propose efficient decentralized schemes, most of them did not consider the system uncertainty from both the wireless environment and other users. To tackle the aforementioned problems, we first formulate the edge node grouping problem as a game that is proved to be an exact potential game with a unique Nash equilibrium. Then, we propose a novel decentralized learning-based edge node grouping algorithm, which guides users to make decisions by learning from historical feedback. Furthermore, we investigate two extended scenarios by generalizing our computation model and communication model, respectively. We further prove that our algorithms converge to the Nash equilibrium with upper-bounded learning loss. Simulation results show that our mechanisms can achieve up to 96.99% of the oracle benchmark.

关键词： Next-generation IoT   Mobile edge computing   Task offloading   Reinforcement learning   Graph convolutional network  

摘要： As the Internet of Things (IoT) keeps evolving, next-generation IoT (NG-IoT) scenarios empower various applications which require low-latency connections or high bandwidth support. Mobile edge computing (MEC) is then proposed to provide users with low-latency computing services. However, the massive and heteroge-neous nature of user devices and MEC servers also brings some new challenges for resource management and task offloading. Existing works have some shortcomings because of adopting a coarse-grained task model or neglecting task graph information. The booming of artificial intelligence (AI) provides us with a more robust approach to addressing these issues. In this paper, we propose an NG-IoT user task offloading and resource scheduling architecture in the MEC scenario. We formulate our problem objective as minimizing average user task completion time (TCT). To solve the problem, we propose a Reinforcement Learning based algorithm for Container Scheduling (RLCS) and cooperating with the graph convolutional network (GCN) technique. We perform RLCS training and evaluate RLCS performance in the simulated environment. Evaluation results indicate that RLCS outperforms other baselines (e.g., reinforcement learning based algorithm, heuristic algorithm) in multiple experimental settings.