关键词： computational resource efficiency   deep reinforcement learning   mobile edge computing   software-defined networking   unmanned aerial vehicles  

摘要： The limited coverage extension of mobile edge computing (MEC) necessitates exploring cooperation with unmanned aerial vehicles (UAV) to leverage advanced features for future computation-intensive and mission-critical applications. Moreover, the workflow for task offloading in software-defined networking (SDN)-enabled 5G is significant to tackle in UAV-MEC networks. In this paper, deep reinforcement learning (DRL) SDN control methods for improving computing resources are proposed. DRL-based SDN controller, termed DRL-SDNC, allocates computational resources, bandwidth, and storage based on task requirements, upper-bound tolerable delays, and network conditions, using the UAV system architecture for task exchange between MECs. DRL-SDNC configures rule installation based on state observations and agent evaluation indicators, such as network congestion, user equipment computational capabilities, and energy efficiency. This paper also proposes the training deep network architecture for the DRL-SDNC, enabling interactive and autonomous policy enforcement. The agent learns from the UAV-MEC environment through experience gathering and updates its parameters using optimization methods. DRL-SDNC collaboratively adjusts hyperparameters and network architecture to enhance learning efficiency. Compared with baseline schemes, simulation results demonstrate the effectiveness of the proposed approach in optimizing resource efficiency and achieving satisfied quality of service for efficient utilization of computing and communication resources in UAV-assisted networking environments.

关键词： Cost efficiency   deep neural network   mobile edge computing   model sharing   online algorithm  

摘要： With the fast growth of mobile edge computing (MEC), the deep neural network (DNN) has gained more opportunities in application to various mobile services. Given the tremendous number of learning parameters and large model size, the DNN model is often trained in cloud center and then dispatched to end devices for inference via edge network. Therefore, maximizing the cost-efficiency of learned model dispatch in the edge network would be a critical problem for the model serving in various application contexts. To reach this goal, in this article we focus mainly on reducing the total model dispatch cost in the edge network while maintaining the efficiency of the model inference. We first study this problem in its off-line form as a baseline where a sequence of n requests can be pre-defined in advance and exploit dynamic programming techniques to obtain a fast optimal algorithm in time complexity of O (m(2)n) under a semi-homogeneous cost model in a m-sized network. Then, we design and implement a 2.5-competitive algorithm for its online case with a provable lower bound of 2 for any deterministic online algorithm. We verify our results through careful algorithmic analysis and validate their actual performance via a trace-based study based on a public open international mobile network dataset.

关键词： Missing traffic data reconstruction   Stacked denoising autoencoder   Mobile edge computing  

摘要： Traffic sensing system requires to periodically collect spatial-temporal traffic data distributed among road networks, which results in overhigh bandwidth consumption and storage cost in a large-scale road network. Several compressive sensing-based algorithms are proposed to reconstruct missing traffic data with limited traffic observation. However, there still exist great challenges to be addressed. First, these existing algorithms are always iteration-based, whose time complexity will explosively increase with the growth of network scale. Furthermore, these algorithms have to be re-executed even if only a small fraction of data changes, which is not suitable for dynamic traffic environments. To overcome these issues, we investigate a novel service architecture of traffic sensing based on mobile edge computing where collected data is pre-processed at the edge node and reconstructed at cloud servers, respectively. On this basis, we formulate the problem of Missing Traffic Data reconstruction (MTDR), which aims at maximizing data reconstruction accuracy within limited observation data. Further, we develop a deep-learning-based algorithm called stacked denoising autoencoder for MTDR (SDAE-MTDR), where three denoising autoencoders are trained in order and then stacked together for parameter fine-tuning based on cross-entropy-based loss function. Finally, we conduct comprehensive performance evaluation based on realistic vehicular traces and the simulation results demonstrate the superiority of the proposed algorithm compared with competitive solutions.

关键词： Mobile crowdsensing   user recruitment   mobile edge computing   combinatorial multi-armed bandit  

摘要： Most of the existing user recruitment methods are designed for the two-tier structure. However, the computational consumption of the cloud platform rises rapidly as data volumes increase. In recent years, edge computing has been introduced into mobile crowdsensing. In this article, we study dynamic user recruitment with edge-aided mobile crowdsensing. Specifically, we consider the scenarios for different sensing tasks. One is the long-duration task, and the other is the short-duration task. For the former, it is time-insensitive, while the latter is time-sensitive. In the scenario with the long-duration task, we consider a simple offline scenario and propose an Edge-Node User Recruitment (ENUR) algorithm to recruit users statically. Then, we consider the online scenario and propose a Budget Re-Distribution algorithm for Edge Nodes User Recruitment (BRD-ENUR). It dynamically redistributes the budget according to the task completion ratio of different edge nodes. In the scenario with the short-duration task, the whole sensing process consists of multiple rounds. In each round, edge nodes need to recruit users between selected users (exploitation) and unselected users (exploration) under the budget. We model such a user recruitment process as a novel combinatorial multi-armed bandit problem. Then, we propose an Edge Nodes User Recruitment algorithm based on combinatorial Multi-armed Bandit (EN-URMB). Extensive experiments show the validity and reliability of our user recruitment algorithms based on the real data set.

关键词： Optimization   Task analysis   Autonomous aerial vehicles   Energy consumption   Servers   Computational modeling   Resource management   Mobile edge computing   unmanned aerial vehicle   computation offloading   alternating direction method of multipliers  

摘要： Unmanned aerial vehicles (UAVs) with integrated computing platforms can be used to provide computing offloading services for ground user equipments (UEs) with limited local computing capabilities, especially in remote areas. In this paper, we focus on the task offloading in an aerial edge network (AEN) assisted by a UAV. We aim at minimizing the sum energy consumption of all UEs by the joint optimization of the task offloading decisions and the UAV position under the constraints of the latency and the total energy of UAV. The formulated optimization problem is a mixed-integer nonconvex problem and involves coupling of many optimization variables. To address this challenge, we first transform the original optimization problem into a linear convex optimization problem via reformulation linearization technology, and then the alternating direction method of multipliers (ADMM) algorithm is proposed to achieve the approximate optimal solution. Numerical results confirm that the proposed ADMM algorithm can effectively reduce the total of energy consumption of UEs and ensure the continuous operation of the UEs.

关键词： Mobile Edge Computing   Wireless power transfer   Local computing   Offload computing   Resource allocation   Problem decomposition   Alternate optimization algorithm  

摘要： With the rapid integration of Intelligent Computing and Internet of Things technology (IoT), Iot sensor network devices are expected not only gather signal but also handle data computing. However, the intrinsic shortcomings of IoT devices on limited computing capacity and battery energy are the bottlenecks. Recently, Wireless Power Transmission (WPT) integrated with Mobile Edge Computing (MEC) provides an effective method for energy and computing resource allocation of IoT devices. However,the offloading ratio strategy and computing and energy resources allocation problems are challenging because the coupling of WPT and MEC, especially when the cost of feedback cannot be ignored. In this paper, we consider simultaneous power transfer and wireless information (SWIPT) strategy for an MEC powered IoT network, which allows the hybrid access point (HAP) to transfer energy by the feedback signal so that IoT devices can capture energy and receive feedback data simultaneously Firstly,the WP-MEC network model, local computation model and computation offloading model of the edge wireless devices (WDs) were built. Secondly, the causal relationship among computational rate ,network channel gain, energy transmission time and transmission power of HAP, CPU computing cycle frequency of WDs and offloading ratio allocation of local computing tasks were analyzed. Then,the above resource allocation problem was modeled as a non-convex problem and was decomposed into two convex sub-problems of offloading ratio strategy and computing and energy allocation. Finally, an alternative optimization methods were proposed to solve these two sub-problems in a sequential and the optimal solution was derived. The proposed algorithm and strategy were effective with the simulation results.

关键词： Unmanned aerial vehicle   Mobile edge computing   Internet of Things   Matching theory  

摘要： Unmanned aircraft vehicles (UAVs)-enabled mobile edge computing (MEC) can enable Internet of Things devices (IoTD) to offload computing tasks to them. Considering this, we study how multiple aerial service providers (ASPs) compete with each other to provide edge computing services to multiple ground network operators (GNOs). An ASP owning multiple UAVs aims to achieve the maximum profit from providing MEC service to the GNOs, while a GNO operating multiple IoTDs aims to seek the computing service of a certain ASP to meet its performance requirements. To this end, we first quantify the conflicting interests of the ASPs and GNOs by using different profit functions. Then, the UAV scheduling and resource allocation is formulated as a multi-objective optimization problem. To address this problem, we first solve the UAV trajectory planning and resource allocation problem between one ASP and one GNO by using the Lagrange relaxation and successive convex optimization (SCA) methods. Based on the obtained results, the GNOs and ASPs are then associated in the framework based on the matching theory, which results in a weak Pareto optimality. Simulation results show that the proposed method achieves the considerable performance.

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： 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   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.