关键词： Streaming media   Quality of experience   Bit rate   Servers   Video recording   Quality assessment   Collaboration   DASH   MEC   Mobile edge computing   QoE   Video streaming   5G  

摘要： Today's Internet traffic is dominated by video streaming applications transmitted through wireless/cellular interfaces of mobile devices. Although ultrahigh-definition videos are now easily transmitted through mobile devices, video quality level that users perceive is generally lower than expected due to distance-based high latency between sources and end-users. Mobile edge computing (MEC) paradigm is expected to address this issue and provide users with higher perceived quality of experience (QoE) for latency-critical applications, deploying MEC servers at edges. However, due to capacity concerns on MEC servers, a more comprehensive approach is needed to meet users' expectations applying all possible operations over the resources such as caching, prefetching, and task offloading policies depending on the data repetition or memory/CPU utilization. To address these issues, this article proposes a novel collaborative QoE-based mobility-aware video streaming scheme deployed at MEC servers. Throughout the article, we demonstrate how the proposed scheme can be implemented so as to preserve the desired QoE level per user during entire video sessions. Performance of the proposed scheme has been investigated by extensive simulations. In comparison to existing schemes, the results illustrate that high efficiency is achieved through collaboration among MEC servers, utilizing explicit window size adaptation, collaborative prefetching, and handover among the edges.

关键词： Virtual networks   Name-object networking   Mobile edge computing   Network slicing  

摘要： Achieving advanced Mobile Edge Computing (MEC) services such as dynamic resource assignment and slicing, maintaining Quality of Service (QoS), and enabling heterogeneous virtual functions are some of the technical challenges associated with edge-cloud enhanced 5G architectures now under consideration. This paper proposes a named-object based virtual network (NOVN) architecture to support low-latency applications in the MEC. Software router implementation running on the ORBIT testbed validates the named-object approach, showing low VN processing and control overhead, and making it possible to achieve low latency. A latency performance improvement of 30% is achieved as compared to the baseline implementation without NOVN. The results also validate feasibility of using the advanced MEC services for an example latency constrained edge cloud scenario. (C) 2020 Elsevier B.V. All rights reserved.

关键词： 5G   telemedicine   mobile edge computing   artificial intelligence   internet of things  

摘要： Telemedicine over Internet of Things (IoT) generates an unprecedented amount of data, which further requires transmission, analysis, and storage. Deploying cloud computing to handle data of this magnitude will introduce unacceptable data analysis latency and high storage costs. Thus, mobile edge computing (MEC) deployed between the cloud and users, which is close to the nodes of data generation, can tackle these problems in 5G scenarios with the help of artificial intelligence. This paper proposes a telemedicine system based on MEC and artificial intelligence for remote health monitoring and automatic disease diagnosis. The integration of different technologies such as computers, medicine, and telecommunications will significantly improve the efficiency of patient treatment and reduce the cost of health care.

关键词： Mobile edge computing   network function virtualization   service function chaining   placement   linear programming  

摘要： Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming-(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization.

关键词： Edge computing   Task analysis   Wireless communication   Resource management   Minimization   Mobile handsets   Antennas   Intelligent reflecting surface   mobile edge computing   latency minimization  

摘要： Computation off-loading in mobile edge computing (MEC) systems constitutes an efficient paradigm of supporting resource-intensive applications on mobile devices. However, the benefit of MEC cannot be fully exploited, when the communications link used for off-loading computational tasks is hostile. Fortunately, the propagation-induced impairments may be mitigated by intelligent reflecting surfaces (IRS), which are capable of enhancing both the spectral- and energy-efficiency. Specifically, an IRS comprises an IRS controller and a large number of passive reflecting elements, each of which may impose a phase shift on the incident signal, thus collaboratively improving the propagation environment. In this paper, the beneficial role of IRSs is investigated in MEC systems, where single-antenna devices may opt for off-loading a fraction of their computational tasks to the edge computing node via a multi-antenna access point with the aid of an IRS. Pertinent latency-minimization problems are formulated for both single-device and multi-device scenarios, subject to practical constraints imposed on both the edge computing capability and the IRS phase shift design. To solve this problem, the block coordinate descent (BCD) technique is invoked to decouple the original problem into two subproblems, and then the computing and communications settings are alternatively optimized using low-complexity iterative algorithms. It is demonstrated that our IRS-aided MEC system is capable of significantly outperforming the conventional MEC system operating without IRSs. Quantitatively, about 20 % computational latency reduction is achieved over the conventional MEC system in a single cell of a 300 m radius and 5 active devices, relying on a 5-antenna access point.

关键词： Mobile edge computing   Security-critical tasks   Scheduling algorithms   Particle swarm optimization  

摘要： Mobile edge computing (MEC) allows mobile devices to offload computation tasks to nearby MEC servers for achieving low latency and energy efficiency. This paper aims at scheduling security-critical tasks, which require data encryption and thus incur extra runtime and energy costs, in a MEC system consisting of multiple resource-limited MEC servers. The scheduling objective is to minimize task completion time as well as the mobile device's energy consumption. We propose two slow-movement particle swarm optimization algorithms to solve the resultant NP-hard problem. Specifically, we develop a position-based mapping scheme to map particles onto scheduling solutions. The mapping method relies on the current best solution and a position-based probability model to generate high-quality solutions that can inherit the good schemata from the current best solution. To prevent the significant change in particles' positions, we further propose a novel particle updating strategy to slow down particles' movements, in order to explore more high-quality solutions under the guide of personal best particle and global best particle. Experimental results demonstrate that, the proposed algorithms significantly outperform the conventional particle swarm optimization algorithm in terms of both effectiveness and efficiency. Performance of the mapping method and the particle updating strategy are also investigated. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Task analysis   Wireless communication   Fading channels   Servers   Energy harvesting   Downlink   Uplink   Mobile edge computing   wireless power transfer   non-linear energy harvesting  

摘要： Mobile-edge computing (MEC) and wireless power transfer are technologies that can assist in the implementation of next generation wireless networks, which will deploy a large number of computational and energy limited devices. In this letter, we consider a point-to-point MEC system, where the device harvests energy from the access point's (AP's) transmitted signal to power the offloading and/or the local computation of a task. By taking into account the non-linearities of energy harvesting, we provide analytical expressions for the probability of successful computation and for the average number of successfully computed bits. Our results show that a hybrid scheme of partial offloading and local computation is not always efficient. In particular, the decision to offload and/or compute locally, depends on the system's parameters such as the distance to the AP and the number of bits that need to be computed.

关键词： Mobile edge computing  

摘要： In Unmanned Aerial Vehicle (UAV)-enabled mobile edge computing (MEC) systems, UAVs can carry edge servers to help ground user equipment (UEs) offloading their computing tasks to the UAVs for execution. This paper aims to minimize the total time required for the UAVs to complete the offloaded tasks, while optimizing the three-dimensional (3D) deployment of UAVs, including their flying height and horizontal positions. Although the formulated optimization is a mixed integer nonlinear programmming, we convert it to a convex problem and develop a successive convex approximation (SCA) based algorithm to effectively solve it. The simulation results show that the joint optimization of the horizontal and the vertical position of a group of UAVs can achieve better performance than the traditional algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing   IoT   Blockchain   Bernoulli distribution convergence   Data management   Data processing  

摘要： With the creation of a mobile edge computing environment in which IoT technologies are converged on cloud services, the importance of high-capacity data processing technologies is increasing. In this paper, we propose a block chain-based convergence data management technique to efficiently handle different kinds of data processed in mobile edge computing environment. The proposed technique minimized data loss by connecting information from specific IoT devices to multiple hash boxes and adding electronic signatures to the first and last information. In addition, the similarity of IoT data is applied stochastically to respond flexibly to changes in the system. Since the proposed technique links IoT data with different probabilities, it has distributed and deployed IoT data ofnbits according to certain rules to reduce the load that can occur on the server. As a result of performance evaluation, the proposed technique has improved the conversion performance assessment of average transcoding propit because block password hash increases depending on AP density and scope when managing blockchain-based IoT data. In addition, the time to generate IoT data was improved because the cumulative use of transactions processed through similarity of blockchain-based IoT data was managed in a group of certain sizes to link the blocks consistently.

关键词： Mobile edge computing   Energy efficiency   User selection   Resource allocation   Convex optimization  

摘要： Mobile edge computing (MEC) as a new type of computing model can expand the computing power of cloud computing to the edge of radio access network (RAN), which brings a large number of applications close for end user. Compared to traditional cloud computing, computation tasks being offloaded to edge clouds nearby to execute can reduce transmission delay and energy consumption. However, how to select the best edge cloud in a dense cell to execute tasks remains challenging. To address this challenge, in this paper we propose joint user selection and resource allocation algorithm in MEC to maximize the user's energy efficiency, defined as the ratio of user throughput to its energy consumption. We formulate the energy efficiency maximization problem as a mixed integer fractional nonlinear optimization problem, which involves both users' offloading selection and uplink transmission power. To solve this non-convex optimization problem, we transform it into an equivalent subtractive convex optimization problem by relaxation transformation method, and furthermore provide the corresponding optimal solution of user selection and power allocation. Numerical results show that compared with other selection schemes, the proposed optimal scheme has a significant improvement in energy efficiency. (c) 2020 Elsevier B.V. All rights reserved.