关键词： Mobile edge computing  

摘要： The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing   Partial computation offloading   Game theory   NOMA  

摘要： Mobile edge computing provides powerful computing for mobile device users to broaden their computation capability. However, due to the limited bandwidth of the wireless channel, both the energy consumption for mobile device's computation offloading and the service latency maybe increase. In this paper, we focus on the partial computation offloading problem for multi-user in mobile edge computing environment with multi-wireless channel. The computation overhead model is built based on game theory. Then, the existence of Nash equilibrium is proven. Furthermore, the partial computation offloading algorithm with low time complexity is given to achieve the Nash equilibrium. In addition, another partial computation task offloading mechanism for mobile device users, who has enough energy and only pay attention to the computation time overhead, is given to reduce the computation overhead. Finally, extensively numerical experiments are conducted. The experimental results show that our proposed algorithm can achieve better performance than the compared algorithms in the mobile edge computing environment.

关键词： Unmanned aerial vehicle   Mobile edge computing   Channel estimation error   Computation task offloading   Resource allocation  

摘要： In this paper, we develop task offloading and resource allocation scheme for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system with channel estimation errors over Rician fading channels. The objective is to maximize the system utility with constrained network stability, transmit power, and data arrival rate. We consider a general multi-user UAV-assisted MEC system based on frequency division multiple access (FDMA), and we assume that the computation tasks are split into separate tasks and offloaded to the server for computing. We study stochastic computational resource management based on the Lyapunov optimization algorithm. The optimal transmit power and bandwidth allocation for computation offloading are obtained alternately, and the optimal computation task admission at each time slot and the optimal value of the auxiliary variable are derived. Simulation results verify the effectiveness of the proposed scheme in the paper and evaluate the influence of various parameters to the system performance.

关键词： Internet of Things   Mobile edge computing   Convergence computing   IoT data transmission  

摘要： The convergence of mobile edge computing (MEC) to the current Internet of Things (IoT) environment enables a great opportunity to enhance massive IoT data transmission. In the narrowband Internet of Things (NB-IoT), the huge number of IoT devices sends the information to the remote network. Simultaneously, the massive IoT devices from the heterogeneous locations access to the radio remote head (RRH) resources to communicate with the remote servers (e.g., mobile cloud computing), while there are limited resources in NB-IoT environments. Therefore, many IoT traffic may be discarded and may have too long queue times for each traffic where it can cause the battery life reduction of the IoT devices. To cope with the mentioned issues, this paper proposed a novel and efficient IoT data transmission scheme based on dynamic resource sharing of RRHs called efficient dynamic resource sharing scheme (E-DRSS). The proposed E-DRSS computes the heterogeneous IoT device with different resources into new groups, and then each group of IoT devices is selected for accessing its appropriate RRH. The evaluation of performance shows that the proposed scheme enhances quality of service (QoS) outperform on the current IoT scheme in terms of the jitters, end-to-end (E2E) latency, throughputs, and number of packets drop during transmission. Therefore, the proposed scheme is suitable to handle more IoT devices simultaneously and increase the communication reliability.

关键词： Mobile edge computing   Collaborative resource allocation   Optimized deployment of edge servers   Average access delay  

摘要： In order to realize the collaborative resource allocation optimization of mobile edge computing (MEC) and reduce the delay of edge server in the transmission process, based on software-defined network (SDN) technology, two optimal edge server deployment schemes of Enumeration-Based Optimal Edge Server Placement Algorithm (EOESPA) and Ranking-based Near-optimal Edge Server Placement Algorithm (RNOESPA) are proposed. Performance comparison experiment simulation is conducted with K-Means cluster algorithm (KMCA) to verify the minimum access delay of edge server under different conditions. After the deployment of edge servers, three collaborative resource allocation optimization algorithms of Optimal Enumeration Service Deployment Algorithm (OESDA), Latency Aware Heuristic Service Deployment Algorithm (LAHSDA), and Clustering Enhanced Heuristic Service Deployment Algorithm (CEHSDA) are proposed, and simulation experiments are carried out to verify the performance of the proposed algorithm under different conditions. The results show that, under different conditions, when the number of deployments increases from 1 to 4, the average access delay of EOESPA can be at least 1ms, and the average access delay obtained by RNOESPA is close to the best performance obtained by EOESPA and better than that obtained by KMCA. When the number of network nodes increases to 50, the minimum average access delay obtained by RNOESPA is closer to the optimal value, which is about 1.42ms. The same performance is shown in relation to the average number of requests, the number of mobile devices, and the average access delay. Among the three collaborative resource allocation optimization algorithms, the minimum average response delay obtained by LAHSDA is close to the optimal average response delay obtained by OESDA, but all of them are lower than CEHSDA, and CEHSDA has the best performance in minimizing the total allocation cost. When the number of service types increases to 8, the total servic

关键词： Artificial Intelligence   6G   Communication networks   Mobile edge computing   Intelligent transportation system  

摘要： With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by 2030, many institutes worldwide have started to explore various issues and challenges in 6G communication networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many applications currently not viable by today's 4G and 5G communication standards. The current 5G technology has security and privacy issues which makes its usage in limited applications. In such an environment, we believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications. In this article, we explore how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption of AI techniques in intelligent transport system.

关键词： Deep learning   Multitask parallel processing architecture   Mobile edge computing   Network model   Loading technology  

摘要： As a mainstream computing and storage strategy for mobile communications, Internet of Things and large data applications, mobile edge computing strategy mainly benefits from the deployment and allocation of small base stations. Mobile edge computing mainly helps users to complete complex, intensive and sensitive computing tasks. However, the algorithm has many problems in practical application, such as complex user needs, complex user mobility, numerous services and applications. Therefore, under the above background, it is of great significance to solve the computational pressure of current mobile edge algorithm and optimize its algorithm architecture. This paper creatively proposes a deep learning architecture based on tightly connected network, and transplants it into mobile edge algorithm to realize the payload sharing process of edge computing, so as to establish an efficient network model. At the same time, we creatively propose a multi-task parallel scheduling algorithm, which realizes the mobile edge algorithm in the face of complex computing and algorithm efficiency. Finally, the above algorithms are simulated and tested. The experimental results show that under the same task, the time consumed by the proposed algorithm is 3.5-4, while the time consumed by the traditional algorithm is 4.5-8, and the corresponding time is standardized time, so the practice shows that the algorithm has obvious overall efficiency advantages.

关键词： Task analysis   Delays   Edge computing   Collaboration   Cloud computing   Servers   Optimization   Computation offloading   Industrial IoT   latency sensitive   mobile edge computing   fog computing   resource allocation   offloading decision  

摘要： Fog computing leverages the computational resources at the network edge to meet the increasing demand for latency-sensitive applications in large-scale industries. In this article, we study the computation offloading in a fog computing network, where the end users, most of the time, offload part of their tasks to a fog node. Nevertheless, limited by the computational and storage resources, the fog node further simultaneously offloads the task data to the neighboring fog nodes and/or the remote cloud server to obtain the additional computing resources. However, meanwhile, the offloaded tasks from the neighboring node incur burden to the fog node. Moreover, the task offloading to the remote cloud server can suffer from limited communication resources. Thus, to jointly optimize the amount of tasks offloaded to the neighboring fog nodes and communication resource allocation for the offloaded tasks to the remote cloud, we formulate a latency-driven task data offloading problem considering the transmission delay from fog to the cloud and service rate that includes the local processing time and waiting time at each fog node. The optimization problem is formulated as a quadratically constraint quadratic programming. We solve the problem by semidefinite relaxation. The simulation results demonstrate that the proposed strategy is effective and scalable under various simulation settings.

关键词： mobile edge computing   multi-cloud multi-edge cloud   formal concept analysis   skyline  

摘要： The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service-provider lattice and provider-cloud lattice, and select the candidate cloud composition that satisfies the user's requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.

关键词： federated learning   optimization   mobile edge computing  

摘要： Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of mobile edge computing. In this paper, we propose a novel control algorithm for high-performance and stabilized queue in FEEL system. We consider that the FEEL environment includes the clients transmit data to associated federated edges;these edges then locally update the global model, which is downloaded from the central server via a backhaul. Obtaining greater quantities of local data from the clients facilitates more accurate global model construction;however, this may be harmful in terms of queue stability in the edge, owing to substantial data arrivals from the clients. Therefore, the proposed algorithm varies the number of clients selected for transmission, with the aim of maximizing the time-averaged federated learning accuracy subject to queue stability. Based on this number of clients, the federated edge selects the clients to transmit on the basis of resource status.