关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is a promising technology that provides cloud and IT services within the proximity of the mobile user. With the increasing number of mobile applications, mobile devices (MD) encounter limitations of their resources, such as battery life and computation capacity. The computation offloading in MEC can help mobile users to reduce battery usage and speed up task execution. Although there are many solutions for offloading in MEC, most usually only employ one MEC server for improving mobile device energy consumption and execution time. Instead of conventional centralized optimization methods, the current paper considers a decentralized optimization mechanism between MEC servers and users. In particular, an assignment mechanism called school choice is employed to assist heterogeneous users to select different MEC operators in a distributed environment. With this mechanism, each user can benefit from minimizing the price and energy consumption of executing tasks while also meeting the specified deadline. The present research has designed an efficient mechanism for a computation offloading scheme that achieves minimal price and energy consumption under latency constraints. Numerical results demonstrate that the proposed algorithm can attain efficient and successful computation offloading. Copyright © 2019, The Authors. All rights reserved.

关键词： mobile computing   wireless LAN   resource allocation   optimisation   computational complexity   distributed processing   optimal access point selection   simple resource allocation scheme   offloading strategy optimisation scheme   mobile edge computing   multiple access points   innovative computing paradigm   mobile devices   computation-intensive tasks   MEC servers   wireless local area networks   resource allocation optimisation scheme   multiple wireless access points network   optimal computation offloading strategy   transmission power allocation   computation resource scheduling   bandwidth assignment   NP-hard problem   Lagrange multiplier  

摘要： Mobile edge computing (MEC) is an innovative computing paradigm to enhance the computing capacity of mobile devices (MDs) by offloading computation-intensive tasks to MEC servers. With the widespread deployment of wireless local area networks, each MD can offload computation task to server via multiple wireless access points (WAPs). However, computation offloading can bring a higher system cost if all users select the same access points to offload their tasks. This study proposes a computation offloading strategy and resource allocation optimisation scheme in a multiple wireless access points network with MEC, which aims to minimise the system cost by providing the optimal computation offloading strategy, transmission power allocation, bandwidth assignment, and computation resource scheduling. The proposed scheme decouples the optimisation problem into subproblems of offloading strategy and resource allocation since the problem is NP-hard. The offloading strategy involves the optimal access point selection, which is analysed by the potential game. The resource allocation is obtained using Lagrange multiplier. The authors' analysis and simulation results verify the convergence performance of the proposed scheme, and the proposed scheme outperforms the simple resource allocation scheme and the offloading strategy optimisation scheme in terms of the system cost.

关键词： Mobile edge computing  

摘要： Recent years have disclosed a remarkable proliferation of compute-intensive applications in smart cities. Such applications continuously generate enormous amounts of data which demand strict latency-aware computational processing capabilities. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Subsequently, we critically review the state-of-the-art literature focusing on edge computing applications in smart cities. Later, we categorize and classify the literature by devising a comprehensive and meticulous taxonomy. Furthermore, we identify and discuss key requirements, and enumerate recently reported synergies of edge computing enabled smart cities. Finally, several indispensable open challenges along with their causes and guidelines are discussed, serving as future research directions. Copyright © 2019, The Authors. All rights reserved.

关键词： vehicular networks   fog computing   intelligent transport system   mobile edge computing  

摘要： Frustrations, monetary losses, lost time, high fuel consumption and CO2 emissions are some of the problems caused by traffic jams in urban centers. In an attempt to solve this problem, this article proposes a traffic service to control congestion, named FOXS-Fast Offset Xpath Service. FOXS aims to reduce the problems generated by a traffic jam in a distributed way through roads classification and the suggestion of new routes to vehicles. Unlike the related works, FOXS is modeled using the Fog computing paradigm. Therefore, it is possible to take advantage of the inherent aspects of this paradigm, such as low latency, processing load balancing, scalability, geographical correlation and the reduction of bandwidth usage. In order to validate FOXS, our performance evaluation considers two realistic urban scenarios with different characteristics. When compared with related works, FOXS shows a reduction in stop time by up to 70%, the CO2 emissions by up to 29% and, the planning time index by up to 49%. When considering communication evaluation metrics, FOXS reaches a better result than other solutions on the packet collisions metric (up to 11.5%) and on the application delay metric (up to 30%).

关键词： Mobile edge computing  

摘要： In this article, we consider the problem of relay assisted computation offloading (RACO), in which user A aims to share the results of computational tasks with another user B through wireless exchange over a relay platform equipped with mobile edge computing capabilities, referred to as a mobile edge relay server (MERS). To support the computation offloading, we propose a hybrid relaying (HR) approach employing two orthogonal frequency bands, where the amplify-and-forward scheme is used in one band to exchange computational results, while the decode-and-forward scheme is used in the other band to transfer the unprocessed tasks. The motivation behind the proposed HR scheme for RACO is to adapt the allocation of computing and communication resources both to dynamic user requirements and to diverse computational tasks. Within this framework, we seek to minimize the weighted sum of the execution delay and the energy consumption in the RACO system by jointly optimizing the computation offloading ratio, the bandwidth allocation, the processor speeds, as well as the transmit power levels of both user A and the MERS, under practical constraints on the available computing and communication resources. The resultant problem is formulated as a non-differentiable and nonconvex optimization program with highly coupled constraints. By adopting a series of transformations and introducing auxiliary variables, we first convert this problem into a more tractable yet equivalent form. We then develop an efficient iterative algorithm for its solution based on the concave-convex procedure. By exploiting the special structure of this problem, we also propose a simplified algorithm based on the inexact block coordinate descent method, with reduced computational complexity. Finally, we present numerical results that illustrate the advantages of the proposed algorithms over state-of-the-art benchmark schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing   tandem queue   effective capacity   resource allocation   statistical QoS provisioning  

摘要： In the mobile edge computing (MEC) network, the applications of devices can be offloaded to the MEC server via the wireless link and then processed through the computation resource, to satisfy the computation and latency demand. Thus, a two-stage tandem queue is formed in the MEC network, consisting of the transmission queue and computation processing queue. However, the fluctuating wireless channel environment not only leads to the stochasticity of service in the first transmission queue, but also brings random computation task arrival in the second computation processing queue, which makes it difficult to guarantee the end-to-end quality of service (QoS) requirement. In this paper, we firstly derive the effective capacity of MEC with the two-stage tandem queue. Further, we formulate the joint bandwidth and computation resource allocation problem under the statistical QoS guarantee, to maximize the total revenue of network. This problem is proven to be NP-hard by the reduction to the two-dimensional knapsack problem. Then we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to reduce the computation complexity, where the complicated problem can be decomposed and transformed into some convex subproblems. Simulation results reveal the inherent relationship between the required bandwidth and computation resource in terms of the supported arrival rate and end-to-end delay, and also demonstrate the proposed scheme can achieve better performance than other schemes.

关键词： Mobile edge computing   Energy harvesting   Radio and computing resource allocation  

摘要： Mobile edge computing (MEC) has been recognized as a promising technique for the Internet of Things (IoT) era which allow the booming mobile devices offload their highly demanding tasks to the MEC server thus release these resource-limited devices from poor computational efficiency. With the development of wireless energy transfer (WET), the combination of wireless devices and energy harvesting technique has attracted much more interests. Energy harvesting enabled devices can be powered by the wireless energy signal to prolong their lifetime. For an MEC system with energy harvesting devices, how to make appropriate computation and communication resources allocation policy is still challenging. In this paper, we proposed a time average computation rate maximization (TACRM) algorithm that allows the joint allocation of radio and computation resources. In each time block, optimal bandwidths, transmit powers, time allocations for local computing and task offloading, and the local CPU frequencies are dynamically decided by the proposed algorithm. Theoretical performance analysis and extensive simulation verified that the proposed algorithm can effectively improve the system performance and outperforms the benchmark schemes.

关键词： mobile edge computing   multi-user edge server selection   resource contention   total waiting time  

摘要： In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user's mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.

关键词： pricing   transcoding   resource allocation   cache storage   scheduling   cellular radio   game theory   MEC server   BSs   caching   computing space   storage   computational capacities   mobile edge networks   computation resource allocation   mobile edge computing   fifth generation networks   computing resources   mobile networks   joint caching   computing resource allocation mechanism  

摘要： To cope with the explosion of video traffic, mobile edge computing (MEC) has been introduced as a promising technology in fifth generation networks to enable caching and computing resources to transcode video contents and cache them within the edge of mobile networks. In this study, a joint caching and computing resource allocation mechanism is proposed to effectively schedule the resources of the mobile edge networks. The authors formulate a Stackelberg game to analyse the allocation problem between the MEC server and multiple base stations (BSs). The MEC server aims to impose the prices to the BSs to maximise its revenue, while the BSs compete to determine the caching/computing space that they can occupy at the MEC server to improve the quality of experience of their service users. The competition between the BSs is formulated as a non-cooperative subgame where each BS is designed to maximise the request rate that can be served by the MEC server. Then, the proposed games are proved to converge to a unique Stackelberg equilibria. Numerical simulations are presented to verify the proposed scheme. It is shown that the proposed schemes are effective in resource allocation for both storage and computational capacities in mobile edge networks.

关键词： Multimedia applications   Mobile edge computing   IoT   Context-aware computing   Cloud computing   Mobile sensors  

摘要： The enormous growth of the Internet of Things (IoT) devices gave governments, businesses, and individual users new means to accomplish their missions. Several IoT applications require deployment at a large scale such as smart cities. Large-scale IoT applications help achieve better monitoring services and more efficient cities. IoT multimedia (IoTMM) applications provide a unique level of intelligence that cannot be obtained with traditional IoT applications. IoTMM applications intensely consume bandwidth, processing, and storage resources compared to traditional IoT applications. IoT nodes vary in their capabilities, where some nodes have high processing capabilities while others do not. Cloud services models (IaaS, PaaS, and SaaS) provide a perfect solution for applications of high demand for resources. However, such models are inefficient when dealing with multimedia applications due to the high bandwidth requirements before reaching the cloud. Mobile edge computing (MEC) model creates a new level of providers according to the proximity of the end users to the network resources. Edge providers can offload some processing that is usually done at the cloud, which improves the overall performance of cloud-based applications. In this paper, we propose a new distributed structure for processing multimedia applications in the cloud, where different layers of processing and providers are involved. Every layer is responsible for a specific role depending on the type of the multimedia application and multimedia device. The major contribution of the proposed architecture includes two main elements: the support of scalable IoTMM applications in large deployment scenarios and the support of effective multimedia information sharing. The proposed architecture supports a scalable architecture for IoTMM applications with minimum additional resources compared to the traditional models. The proposed model allows for effective and practical sharing of multimedia information (raw mu