关键词： Mobile web augmented reality   Mobile edge computing   Model data computing service   Collaborative computing  

摘要： Mobile web-based augmented reality (MWAR) provides users with quick richer information interaction forms due to its convenience and universality. However, the 3D model data service for MWAR is a type of centralized file-based data service, which cannot simultaneously meet diverse user and response delay requirements in large-scale and complex applications. This affects the application and promotion of large-scale and complex MWAR application. To this end, this paper proposes a collaborative model data computing service framework (CMCSF) for MWAR between Mobile Edge Servers (MES), Cloud Servers (CS), and mobile devices. The main contributions of this paper include: (1) The CMCSF converts the file-based 3D model data service in MWAR to a computing-based and interfaced data service to meet the diverse service requirements of users;(2) The CMCSF establishes a collaborative computing model data service between the MES and the CS, and it computes a control and deployment strategy for the collaborative computing model data service in order to reduce the response delay of 3D model data;and (3) The CMCSF optimizes the loading method of the WebGL engine (for example, ***) and applies the asynchronous loading method of the JSON interface data on the http protocol for browsers to get persistent model data services. An experimental evaluation shows the CMCSF improves the response efficiency of the 3D model data file Significantly when compared with the original centralized file-based 3D model file service.

关键词： Mobile edge computing  

摘要： Rural cooperative financial organization is a new type of cooperative financial organization in recent years. It is a community financial institution created by farmers and small rural enterprises to voluntarily invest in shares in order to meet the growing demand for rural financing. However, this financial organization has many flaws in the design of the system;it has not promoted the better development of rural mutual fund assistance. In addition, mobile edge computing (MEC) can be used as an effective supplement to mobile cloud computing and has been proposed. However, most of the current literature studies on cloud computing provide computing offload just to propose a network architecture, without modeling and solving to achieve. In this context, this paper focuses on the practical application of MEC in the risk control of new rural cooperative financial organizations. This paper proposes a collaborative LECC mechanism based on machine learning under the MEC architecture. The experimental simulation shows that the HR under the LECC mechanism is about 17%-23%, 46%-69%, and 93%-177% higher than that of LENC, LRU, and RR, respectively. It is unrealistic to want to rely on meager loan interest for long-term development. The most practical way is to increase the income level of the organization itself.

关键词： Mobile edge computing  

摘要： —This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, multiple base stations, and a malicious eavesdropper. Specifically, the users adopt the partial offloading strategy by partitioning the computation task into several parts. One is executed locally and the others are securely offloaded to multiple MEC servers integrated into the base stations by leveraging the physical layer security to combat the eavesdropping. We jointly optimize power allocation, task partition, subcarrier allocation, and computation resource to maximize the secrecy offloading rate of the users, subject to communication and computation resource constraints. Numerical results demonstrate that our proposed scheme can respectively improve the secrecy offloading rate 1.11%–1.39% and 15.05%–17.35% (versus the increase of tasks’ latency requirements), and 1.30%–1.75% and 6.08%–9.22% (versus the increase of the maximum transmit power) compared with the two benchmarks. Moreover, it further emphasizes the necessity of conducting computation offloading over multiple MEC servers. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing   Trust management   Service level agreement   Processing performance Fuzzy logic  

摘要： Mobile edge computing (MEC) is introduced as part of edge computing paradigm, that exploit cloud computing resources, at a nearer premises to service users. Cloud service users often search for cloud service providers to meet their computational demands. Due to the lack of previous experience between cloud service providers and users, users hold several doubts related to their data security and privacy, job completion and processing performance efficiency of service providers. This paper presents an integrated three-tier trust management framework that evaluates cloud service providers in three main domains: Tier I, which evaluates service provider compliance to the agreed upon service level agreement;Tier II, which computes the processing performance of a service provider based on its number of successful processes;and Tier III, which measures the violations committed by a service provider, per computational interval, during its processing in the MEC network. The three-tier evaluation is performed during Phase I computation. In Phase II, a service provider total trust value and status are gained through the integration of the three tiers using the developed overall trust fuzzy inference system (FIS). The simulation results of Phase I show the service provider trust value in terms of service level agreement compliance, processing performance and measurement of violations independently. This disseminates service provider's points of failure, which enables a service provider to enhance its future performance for the evaluated domains. The Phase II results show the overall trust value and status per service provider after integrating the three tiers using overall trust FIS. The proposed model is distinguished among other models by evaluating different parameters for a service provider.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is proposed to boost high-efficient and time-sensitive 5G applications. However, the "microburst" may occur even in lightly-loaded scenarios, which leads to the indeterministic service latency (i.e., unpredictable delay or delay variation), hence hindering the deployment of MEC. Deterministic IP networking (DIP) has been proposed that can provide bounds on latency, and high reliability in the large-scale networks. Nevertheless, the direct migration of DIP into the MEC network is non-trivial owing to its original design for the Ethernet with homogeneous devices. Meanwhile, DIP also faces the challenges on the network throughput and scheduling flexibility. In this paper, we delve into the adoption of DIP for the MEC networks and some of the relevant aspects. A deterministic MEC (D-MEC) network is proposed to deliver the deterministic service (i.e., providing the MEC service with bounded service latency). In the D-MEC network, two mechanisms, including the cycle mapping and cycle shifting, are designed to enable: (i) seamless and deterministic transmission with heterogeneous underlaid resources;and (ii) traffic shaping on the edges to improve the resource utilization. We also formulate a joint configuration to maximize the network throughput with deterministic QoS guarantees. Extensive simulations verify that the proposed D-MEC network can achieve a deterministic MEC service, even in the highly-loaded scenarios. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing   Nonorthogonal multiple access   Computation offloading   Resource allocation  

摘要： Due to explosive growth of computation-intensive applications, multi-access mobile edge computing (MEC) has been proposed as an efficient technology to relieve the burden on mobile users by offloading computation tasks to MEC servers. Meanwhile, nonorthogonal multiple access (NOMA) has been proposed to improve spectrum efficiency by allowing multiple users transmitting on the same time and spectrum resource. Thus, NOMA can be naturally adopted by the MEC systems to improve the offloading performance. In this paper, we consider a NOMA-based multi-access MEC system with multiple MEC servers and multiple users. It is assumed that each user has multiple computation tasks, where each task shall be offloaded to one MEC server for computing. NOMA is applied for uploading multiple tasks to MEC servers as well as downloading multiple task results. The problem of jointly optimizing the MEC server allocation, the transmit power allocation of the users, the transmit power allocation of the MEC servers, the computation resource allocation of the MEC servers, the time allocation and the channel allocation, for minimizing the overall delay of all tasks is investigated. We then propose a heuristic algorithm to solve the problem, where the computation resource allocation of the MEC servers and the time allocation are optimized by the bisection search method, the transmit power allocations are obtained in closed-form, and the MEC server allocation and the channel allocation are optimized by the swap matching algorithm. Extensive simulations demonstrate that the proposed algorithm significantly outperforms various benchmark algorithms.

关键词： Mobile edge computing  

摘要： Recently, the hybrid relay-reflecting intelligent surface (HRRIS) has been introduced as a spectral- and energyefficient architecture to assist wireless communication systems. In the HRRIS, a single or few active relay elements are deployed along with a large number of passive reflecting elements, allowing it to not only reflect but also amplify the incident signals. In this work, we investigate the potential of the HRRIS in aiding the computation offloading in a single-user mobile edge computing system. The objective is to minimize the offloading latency while ensuring the secrecy of user data against a malicious eavesdropper. We develop efficient solutions to this latency minimization problem based on alternating optimization. Through numerical results, we show that the deployment of the HRRIS can result in a considerable reduction in latency. Furthermore, the latency reduction gain offered by the HRRIS is much more significant than that of the conventional reconfigurable intelligent surface (RIS). © 2021, CC0.

关键词： Mobile edge computing  

摘要： To mitigate the computational power gap between the network core and edges, mobile edge computing (MEC) is poised to play a fundamental role in future generations of wireless networks. In this correspondence, we consider a non-orthogonal multiple access (NOMA) transmission model to maximize the worst task to be offloaded among all users to the network edge server. A provably convergent and efficient algorithm is developed to solve the considered non-convex optimization problem for maximizing the minimum number of offloaded bits in a multi-user NOMA-MEC system. Compared to the approach of optimized orthogonal multiple access (OMA), for given MEC delay, power and energy limits, the NOMA-based system considerably outperforms its OMA-based counterpart in MEC settings. Numerical results demonstrate that the proposed algorithm for NOMA-based MEC is particularly useful for delay sensitive applications. © 2021, CC BY.

关键词： Asynchronous federated learning   Internet of Things(IoT)   Mobile edge computing  

摘要： The advancement of the Internet of Things(IoT)brings new opportunities for collecting real-time data and deploying machine learning ***,an individual IoT device may not have adequate computing resources to train and deploy an entire learning *** the same time,transmitting continuous real-time data to a central server with high computing resource incurs enormous communication costs and raises issues in data security and *** learning,a distributed machine learning framework,is a promising solution to train machine learning models with resource-limited devices and edge ***,the majority of existing works assume an impractically synchronous parameter update manner with homogeneous IoT nodes under stable communication *** this paper,we develop an asynchronous federated learning scheme to improve training efficiency for heterogeneous IoT devices under unstable communication ***,we formulate an asynchronous federated learning model and develop a lightweight node selection algorithm to carry out learning tasks *** proposed algorithm iteratively selects heterogeneous IoT nodes to participate in the global learning aggregation while considering their local computing resource and communication *** experimental results demonstrate that our proposed asynchronous federated learning scheme outperforms the state-of-the-art schemes in various settings on independent and identically distributed(i.i.d.)and *** distribution.

关键词： Mobile edge computing   Service placement   Request routing   Linear programming   Heuristic algorithm  

摘要： Mobile edge computing (MEC) is envisioned as a prospective technology that supports latency-critical and computation-intensive applications by using storage and computation resources in network edges. The advantages of this technology are trapped in limited edge cloud resources, and one of the prime challenges is how to allocate available edge cloud resources to satisfy user requests. However, previous works usually optimize service (data&code) placement and request routing simultaneously within the same timescale, ignoring the fact that frequent service replacement will incur expensive operating expenses. In this paper, we jointly optimize service placement and request routing in the MEC network for data analysis applications, under the constraints of computation and storage resource. In particular, the Cloud Radio Access Network (C-RAN) architecture is applied to pool available resources and realize load balancing among edge clouds. In addition, we adopt a two timescale framework to reduce high operating expenses caused by frequent cross-cloud service replication and replica deletion. Then, we develop a greedy-based approximation algorithm for service placement subproblem and a linear programming (LP) relaxation-based heuristic algorithm for request routing subproblem, respectively. Finally, the numerical results demonstrate that our proposed solution reaches 90% of the optimal performance in services homogeneous case and 76% in services heterogeneous case.