关键词： Mobile edge computing   Caching   User preference   Context-aware   Content popularity  

摘要： With the explosive growth in wireless data traffic, it is difficult to support the massive consumption of backhaul link bandwidth. Mobile edge computing (MEC) uses edge caching to store content closer to the end user in case to reduce the computational overhead and latency. Caching decisions depend on user demand, which is associated with user behavior. Popular content shows the preferences of user behavior. Combining user context behavior to benefit caching, our optimization objective is to maximize the hit rate of contents within limited time. This optimization problem can be modeled as a knapsack problem. Even if the context and content popularity are known for all circumstances, it is still NP-hard. We thus propose a heuristic Smart caching algorithm (Smart-caching) within the MEC paradigm. Smart-caching constructs the user behavior preferences to reflect different user demands for content. Based on this construct, Smart-caching employs contextual zoom caching to find the context-aware content popularity with diversity via partitioning and exploring the context space. Our experiments are implemented with a real large-volume dataset, i.e., China Mobile User Detail Record (UDR). The results show that Smart-caching achieves a better cache hit rate and stability with low overhead compared with other related caching algorithms. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Virtual reality   mobile edge computing   wireless caching   low latency   transmission rate  

摘要： Virtual reality (VR) over wireless is emerging as an important use case of 5G networks. Fully-immersive VR experience requires the wireless delivery of huge data at ultra-low latency, thus leading to ultra-high transmission rate requirement for wireless communications. This challenge can be largely addressed by the recent network architecture known as mobile edge computing (MEC) network, which enables caching and computing capabilities at the edge of wireless networks. This paper presents a novel MEC-based mobile VR delivery framework that is able to cache parts of the field of views (FOVs) in advance and compute certain post-processing procedures on demand at the mobile VR device. To minimize the average required transmission rate, we formulate the joint caching and computing optimization problem to determine which FOVs to cache, whether to cache them in 2D or 3D as well as which FOVs to compute at the mobile device under cache size, average power consumption as well as latency constraints. When FOVs are homogeneous, we obtain a closed-form expression for the optimal joint policy which reveals interesting communications-caching-computing tradeoffs. When FOVs are heterogeneous, we obtain a local optima of the problem by transforming it into a linearly constrained indefinite quadratic problem and then applying concave convex procedure. Numerical results demonstrate the proposed mobile VR delivery framework can significantly reduce communication bandwidth while meeting low latency requirement.

关键词： Multi-source data fusion   mobile edge computing   multi-leader-follower game   equilibrium program with equilibrium constraints   block coordinate descent method   variational inequality  

摘要： Multi-source data fusion to support vehicle on-road analysis is a promising service offered by mobile edge computing (MEC) for vehicles. With the fusion results delivered in near real-time, vehicle users (VUs) can peek around the corner, extend sensing range, reinforce and validate local observations. Consequently, there has emerged a new market between smart mobility service providers (SMSPs) and VUs in offering and purchasing multi-source data fusion results to support vehicle on-road analysis. Each SMSP and each VU compete with their peers to maximize their own profits. Also, VUs can receive multiple fusion results from several SMSPs and combine them to achieve a better inference. In this paper, we develop a multi-leader-follower game to model this complicated coupled problem. For the single-SMSP scenario, we analyze the properties of the leader-follower (L/F) Nash equilibrium and then reformulate the game as a mathematical program with equilibrium constraints (MPEC) to obtain the equilibrium. For the multi-SMSP scenario, the game is reinterpreted as an equilibrium problem with equilibrium constraints (EPEC), for which we analyze the local Nash equilibrium (LNE) with the assistance of variational inequality (VI) theory. Then, the block coordinate descent (BCD) method, which is low-complexity, is applied to solve the EPEC. Finally, numerical results are provided to validate the theoretical analysis and show that our proposed strategies maximize the utilities for both SMSPs and VUs.

关键词： Edge computing   mobile edge computing   cloud computing   5G wireless networks   fog computing and cloudlet computing   computation offloading  

摘要： Modern applications such as augmented reality, connected vehicles, video streaming and gaming have stringent requirements on latency, bandwidth and computation resources. The explosion in data generation by mobile devices has further exacerbated the situation. Mobile Edge Computing (MEC) is a recent addition to the edge computing paradigm that amalgamates the cloud computing capabilities with cellular communications. The concept of MEC is to relocate the cloud capabilities to the edge of the network for yielding ultra-low latency, high computation, high bandwidth, low burden on the core network, enhanced quality of experience (QoE), and efficient resource utilization. In this paper, we provide a comprehensive overview on different traits of MEC including its use cases, architecture, computation offloading, security, economic aspects, research challenges, and potential future directions.

关键词： Mobile edge computing  

摘要： Computation-efficient resource allocation strategies are of crucial importance in mobile edge computing networks. However, few works have focused on this issue. In this letter, weighted sum computation efficiency (CE) maximization problems are formulated in a mobile edge computing (MEC) network with orthogonal frequency division multiple access (OFDMA). Both partial offloading mode and binary offloading mode are considered. The closed-form expressions for the optimal subchannel and power allocation schemes are derived. In order to address the intractable non-convex weighted sum-of ratio problems, an efficiently iterative algorithm is proposed. Simulation results demonstrate that the CE achieved by our proposed resource allocation scheme is better than that obtained by the benchmark schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Replica placement   Data availability   Edge computing environment  

摘要： As edge devices that make up the Internet become more powerful, these devices in edge computing environment will even perform better in improving the computing and storage capabilities of system. In this paper, to enhancing data availability and improve cloud storage performance in edge computing environment, an adaptive replica placement strategy in edge computing environment is proposed to study the relationship among user access characteristics, the number of replicas, and the replicas location. To deal with the volatility of user access, the strategy in this paper takes data block as the data unit and the dynamic replica creation algorithm (DRC-GM) is proposed. DRC-GM uses the relationship between access frequency of data blocks and the number of replicas to dynamically adjust the number of replicas to ensure data availability requirements. To find the optimal location for replica distributed placement, in this paper, the replica placement algorithm (RP-FNSG) is proposed. And replica number, node performance and replica consistency are considered. Experiment results show that DRC-GM and RP-FNSG algorithms in edge computing environment can greatly improve system performance in terms of better prediction accuracy, shorter access response, higher effective network and storage space usage, which enhancing the data availability.

关键词： Mobile edge computing  

摘要： In the mobile edge computing (MEC) platform, tasks that are being performed often change due to mobile device migration. In order to improve the energy utilization of the MEC platform and the migration process of the mobile terminal and to ensure effective and continuous operation of services, dynamic service migration strategy with energy optimization is required. Aiming at the problem of energy consumption optimization of dynamic service migration with the far-near effect in mobile networks, this article proposes a dynamic service migration strategy with energy optimization, which ensures the performance requirements of the service by considering the minimum energy cost of the relevant equipment during the dynamic migration process. First, by analyzing the relationship between migration distance and equipment transmit power, the energy consumption model associated with the migration distance is established. Then, according to the task dynamic service migration scenario, the dynamic service migration energy consumption model is constructed, so as to obtain the reward function for migrating energy consumption. Finally, the dynamic service migration strategy with energy optimization is realized through the optimal migration energy consumption expectation, which is obtained by the optimal stopping theory. The experimental results show that the optimization strategy proposed in this article can effectively reduce the energy consumption of dynamic service migration in different simulation environments and can improve the dynamic migration performance.

关键词： Mobile edge computing  

摘要： Large-scale mobile edge computing (MEC) systems require scalable solutions to allocate communication and computing resources to the users. In this letter we address this challenge by applying dynamic spectrum sharing among the base stations (BSs), together with local resource allocation in the cells. We show that the network-wide resource allocation can be transformed into a convex optimization problem, and propose a distributed, hierarchical solution with limited information exchange among the BSs. Numerical results demonstrate that the proposed solution is superior to other baseline algorithms, when wireless and computing resource allocation is not jointly optimized, or the wireless resources allocated to the BSs are fixed. Copyright © 2019, The Authors. All rights reserved.

关键词： Sentiment analysis   Mobile edge computing   CNN   LSTM   Recommendation system  

摘要： Sentiment analysis techniques used on texts play an important role in many fields including decision making systems. A variety of research has been actively conducted on sentiment analysis techniques such as an approach using word frequency or morphological analysis, and the method of using a complex neural network. In this paper, we apply sentiment analysis technology using a deep neural network to sightseeing reviews, add ratings to reviews which had not included them, supplement data to enable various classification by weather or season, and design a system that enables custom recommendations based on data. Finally, we examine the contextual features of tourist attractions and design an efficient pre-processing procedure based on the results, and describe the overall process such as building a suitable learning environment, combining review and weather information, and final recommendation method.

关键词： mobile edge computing   cognitive computing   deep learning   computation offloading   mobility   optimization  

摘要： Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.