关键词： mobile edge computing   distributed matrix multiplication   coded computing   cooperative transmission  

摘要： This paper exploits coding to speed up computation offloading in a multi-server mobile edge computing(MEC)network with straggling servers and channel *** specific task we consider is to compute the product between a user-generated input data matrix and a large-scale model matrix that is stored distributively across the multiple edge *** key idea of coding is to introduce computation redundancy to improve robustness against straggling servers and to create communication redundancy to improve reliability against channel *** utilize the hybrid design of maximum distance separable(MDS)coding and repetition *** on the hybrid coding scheme,we conduct theoretical analysis on the average task uploading time,average edge computing time,and average output downloading time,respectively and then obtain the end-to-end task execution *** results demonstrate that when the task uploading phase or the edge computing phase is the performance bottleneck,the hybrid coding reduces to MDS coding;when the downlink transmission is the bottleneck,the hybrid coding reduces to repetition *** hybrid coding also outperforms the entangled polynomial coding that causes higher uplink and downlink communication loads.

关键词： Mobile edge computing   energy efficiency   task allocation   dynamic frequency scaling  

摘要： As an emerging computing paradigm, mobile edge computing (MEC) can improve users' service experience by provisioning the cloud resources close to the mobile devices. With MEC, computation-intensive tasks can be processed on the MEC servers, which can greatly decrease the mobile devices' energy consumption and prolong their battery lifetime. However, the highly dynamic task arrival and wireless channel states pose great challenges on the computation task allocation in MEC. This paper jointly investigates the task allocation and CPU-cycle frequency, to achieve the minimum energy consumption while guaranteeing that the queue length is upper bounded. We formulate it as a stochastic optimization problem, and with the aid of stochastic optimization methods, we decouple the original problem into two deterministic optimization subproblems. An online Task Offloading and Frequency Scaling for Energy Efficiency (TOFFEE) algorithm is proposed to obtain the optimal solutions of these subproblems concurrently. TOFFEE can obtain the close-to-optimal energy consumption while bounding the applications' queue length. Performance evaluation is conducted which verifies TOFFEE's effectiveness. Experiment results indicate that TOFFEE can decrease the energy consumption by about 15 percent compared with the RLE algorithm, and by about 38 percent compared with the RME algorithm.

关键词： Task analysis   Wireless communication   Resource management   Trajectory   Optimization   Wireless sensor networks   Unmanned aerial vehicles   Ubiquitous network accessibility   Beyond 5G   Unmanned aerial vehicle   Mobile edge computing   Wireless power transmission   Task offloading  

摘要： Providing the ubiquitous network accessibility is the key goal for 5G and Beyond 5G (B5G) networks. As the number of devices and data increase, ensuring the Quality of Service(QoS) by the existing network with a singular focus could be challenging. Meanwhile, it always is of the utmost importance to perform the computation-intensive or delay-sensitive tasks welland provide long-term services for the B5G networks. To tackle these challenges, we present an air-ground integrated network in B5G wireless communications, where an UAV is deployed as an aerial radio access platform to formulate system strategy intelligently, as well as provide task offloading and energy harvesting opportunities for terrestrial devices. To get more insight of it, we propose an intelligent charging-offloading scheme and formulate the joint multi-taskcharging-offloading scheduling as an optimization problem aiming to minimizing the system servicelatency of all devices by jointly optimizing the task offloading decisions, connection scheduling, charging and computation resources allocation of UAV. However, the formulated optimization problem is a Mixed-Integer Nonlinear Programming (MINLP) problem which is challenging to solve in general. Therefore, we decompose it into multiple convex sub-problems based on Block-Coordinate Descent (BCD) method and develop an improved greedy algorithm to obtain a feasible optimal solution. To validate the proposed algorithm, it is comprehensively compared with several existing schemes. Performance evaluation demonstrates that our scheme outperforms the benchmarks in terms of the system service latency of all UDs. Moreover, we represent the system working process and the paradigm of industrial applications.

关键词： Relays   Internet of Things   Delays   Servers   Unmanned aerial vehicles   Cloud computing   Mathematical model   Internet of things   mobile edge computing   unmanned aerial vehicle   relay communication  

摘要： At present, the Internet of things (IoT) is widely employed in various systems. The operation of these systems requires intelligent data processing, which needs to be performed rapidly at the network edge. Hence, studies on mobile edge computing (MEC) have been increasingly conducted in recent years. In MEC, edge servers perform data processing to reduce service delay. To meet the demand for computational and communication resources in infrastructureless areas or during large-scale disasters, the use of unmanned aerial vehicles (UAVs) in providing computational resources has attracted considerable attention. However, in MEC using UAVs, it is necessary to consider the workload balance and communication range of vehicles used. In this study, we highlight problems associated with such systems from a new perspective. Furthermore, we propose a relay control method equalizing the workload balance between UAVs and reducing network delay. We modeled the proposed method as a complex system, mathematically formulating each delay element. Additionally, experimental results highlight the effectiveness of the proposed method in various environments. The reduction in delay considerably improved the feasibility of the system. This study is expected to help achieve a significant advancement in MEC using UAVs by establishing a foundation for further research.

关键词： Servers   Cloud computing   Robustness   Delays   Edge computing   Partitioning algorithms   Cellular networks   Mobile edge computing   partitioning   geographical awareness  

摘要： The success of a mobile edge computing network depends critically on how to assign the edge servers to the user cells. The criterion for this assignment is application-specific. In many practical applications, the workload demanded by each cell is unknown and time-varying. So are the effective capacities of the servers. We need an assignment incurring minimum backhaul cost that is robust to these uncertainties. We also want to make the cells assigned to the same server form a geographically compact cluster. This challenge motivates us to introduce a novel stochastic geo-aware partitioning problem. As it is computationally hard, we propose a heuristic algorithm that can produce a range of solutions representing different tradeoffs between cost minimization versus geographical awareness. We evaluate the proposed algorithm using a real-world dataset.

关键词： QoS   Mobile edge computing   Recommendation   Time-aware   Location  

摘要： With the rapid development of Internet of Things, mobile edge computing which provides physical resources closer to end users has gained considerable popularity in academic and industrial field. As the number of edge server increases, accessing effective edge services fast is an urgent problem to be solved. In this paper, we mainly focus on the cold-start problem for service recommendation based on location of users and services. Address this conundrum, we propose a service recommendation method based on collaborative filtering (CF) and location, by comprehensively considering the characteristic of services at the edge, mobility and demands of users at different time periods. In detail, we synthesize the service characteristics of each dimension in different time slices through multidimensional weighting method at first. Then We further introduce the idea of Inverse CF Rec to the traditional CF and predict the lost quality of service (QoS) to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on time-aware collaborative filtering (MDITCF) outperforms Inverse CF Rec in terms of the accuracy of recommendation.

关键词： Low-energy base station group   Game algorithm   Computation offloading   Energy control   Mobile edge computing  

摘要： The research on mobile edge computing generally ignores task energy control and optimization between base station groups. To solve this problem, this paper proposes a low-latency and efficient task offloading algorithm for low-energy base station groups. Firstly, the low-load base station group model and energy consumption model for edge devices are proposed, which focuses on how base stations handle tasks cooperatively in the group. Secondly, based on the division of base station group, a total energy model for computation and communication loss is established respectively. Then, we analyze energy consumption constraints and optimization goal, clarify the goal and evaluation indicators of offloading tasks. Based on the game theory, a game model of the base station unloading task is established to meet the delay, load and interference constraints, and the updating process of the unloading algorithm is completed by iteration. Finally, experiments verify the convergence ability of the proposed algorithm compared with joint optimization and distributed offloading strategies. The effectiveness of the proposed algorithm is verified from the perspectives of long-term energy constraints, unit data transmission energy consumption, computing data size, number of edge devices and average system delay. Experiments show that the control algorithm proposed in this paper not only effectively reduces the energy consumption of base station group, but also has good execution performance and convergence.

关键词： Non-orthogonal multiple access   Mobile edge computing   Outage probability  

摘要： To satisfy the fast growth of Internet of Things (IoT) or development of applications in the fifth generation (5G) wireless networks, the expected systems need two main functions such as massive connectivity of IoT devices and the low latency. Fortunately, non-orthogonal multiple access (NOMA) has been recommended as a promising approach for 5G networks to satisfy concerned requirements and to significantly improve the network capacity. Together with advances of NOMA scheme, mobile edge computing (MEC) is recognized as one of the key emerging approaches to improve the quality of service and reduce the latency for 5G networks. In order to capture the potential gains of NOMA-aided MEC systems, this paper proposes design of multiple antennas and full-duplex for an edge computing aware NOMA architecture. As expected, such NOMA-aided MEC systems can enjoy the benefits of uplink NOMA in reducing MEC users' uplink outage probability. To this end, we derive expression of outage probability for signals corresponding two-user model at uplink. Especially, degraded performance might happens in practice under impact of hardware impairment.

关键词： Mobile edge computing   Evaluation index   Factor analysis   Cluster analysis   Index weight   Judgment matrix   Online and offline hybrid teaching  

摘要： In order to enhance the hybrid teaching of physical education online and offline, a quality evaluation method for online and offline hybrid teaching based on mobile edge computing is proposed. Through the evaluation target, index, weight of index and evaluation standard, this paper first constructs the quality evaluation index system of hybrid physical education online and offline. Then, factor and cluster analysis on the importance evaluation grade data of each index to simplify the index items are carried out. By combined with the mobile edge calculation, the weights of quality evaluation indexes for hybrid teaching are calculated respectively, and the fuzzy comprehensive evaluation model is used to effectively realize the quality evaluation of hybrid physical education online and offline. The simulation results show that the proposed method can effectively reduce the cost and error of quality evaluation for hybrid teaching online and offline, improve the evaluation efficiency, and enhance the quality of online and offline hybrid teaching.

关键词： blockchain   mobile edge computing   mobile services   resource management  

摘要： In recent decades, newly emerging mobile applications and services are becoming increasingly resource hungry and computation intensive. Portable size mobile devices fall short in providing such services. Cloud computing has been a core computation technology to provide visualized resources in a scalable way for mobile services. However, the unpredictable network transmission latency makes cloud computing not efficient enough for time-sensitive mobile services, which requires major changes in underlying computing platform. Mobile edge computing is widely known as one of the novel technologies has emerged in recent years to address this issue. For being impractical to construct huge edge cloud in network edge, edge clouds can still be overloaded in rush time. Offloading to near-user facilities using device-to-device (D2D) or other technologies becomes an augmentation approach. However, how to manage the facilities/resources effectively become a new issue. In this paper, we proposed a resource management scheme named BCEdge based on blockchain in D2D-assisted mobile edge computing. BCEdge is a reliable scheme that operates in a distributed way to relieve the load of edge clouds. We illustrate the advantages and technical details of BCEdge using flow charts and interaction charts. The experiment results validate the effectiveness of our scheme. Finally, we discuss the possible future extensions.