Mobile Edge Computing Platform Research Articles

Key security measurement method of authentication based on mobile edge computing in urban rail transit communication network

In the rail transit industry, the communication network is also widely used in various professions. Rail transit stations have the characteristics of narrow space, high degree of airtightness, limited sight lines, and uneven distribution of personnel in various periods. Although the security degree of rail transit entrances and exits is relatively reliable compared with that of stations, the anti-risk ability is fragile, and the consequences are often disastrous. To solve the problems of low-security access rate, long response time and ineffective identification of attack components of urban rail transit communication platform users, a security measurement method based on mobile edge computing (MEC) for the authentication key of urban rail transit communication network is proposed. Firstly, the wireless communication service of the urban rail transit platform is analyzed, and the data key protocol is constructed to obtain the hyper-chaos key pair of wireless communication information. Then, the double chaotic mapping model is applied to determine the key form and input parameters, and the bit key stream is obtained, which is subjected to exclusive operation with the plaintext for key decryption. Next, the block matching structure is used to analyze the associated eigenvalues that affect the security risk of urban rail transit communication networks. Meanwhile, the MEC server is used to accelerate the extraction of its feature values. Finally, in the environment of MEC, the paper constructs a risk identification model, evaluates the security strength of the authentication key, controls the threshold value, analyzes the impact factors and weights of communication network security, and completes the traceability measurement of communication network security risk. Experimental results show that the security access times of the MEC platform are senior, the maximum attack component obtained is 23500, which is consistent with the actual value, and the global maximum response time is 0.30 ms.

Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems

The recent development of unmanned aerial vehicle (UAV) and mobile edge computing (MEC) technologies provides flexible and resilient computation services to mobile users out of the terrestrial computing service coverage. In this paper, we consider a UAV-enabled MEC platform that serves multiple mobile ground users with random movements and task arrivals. We aim to minimize the average weighted energy consumption of all users subject to the average UAV energy consumption and data queue stability constraints. We formulate the problem as a multi-stage stochastic optimization, and adopt Lyapunov optimization to convert it into per-slot deterministic problems with fewer optimizing variables. We design two reduced-complexity methods that solve the resource allocation and the UAV movement either in two sequential steps or jointly in one step. Both methods can guarantee to satisfy the average UAV energy and queue stability constraints, meanwhile achieving a tradeoff between the user energy consumption and the length of queue backlog. Simulation results show that the two methods significantly outperform the other benchmark methods including a learning-based method in reducing the energy consumption of ground users. In between, the proposed joint optimization method achieves better performance than the two-stage method at the cost of higher computational complexity.

Joint Edge Server Placement and Service Placement in Mobile-Edge Computing

There have been many studies focusing on edge server deployment and service placement in mobile-edge computing (MEC), respectively, but rare works took both of them into consideration. However, edge server deployment and service placement are coupling issues in practice, where the former affects the latter. Besides, the economic benefit of the MEC platform is also a consideration. Due to different service request rates and prices, appropriate service placement solutions are needed to increase the overall profit. In this article, we propose a complete process combining edge server and service placement, where service placement explicitly takes into account the structure of current edge server placement and different service request rates and prices. We design a joint edge server deployment and service placement model with the goal of maximizing the overall profit of all edge servers under the constraints of the number of edge servers, the relationship among edge servers and base stations, the storage capacity, and the computing capacity of each edge server. We propose a two-step method including the clustering algorithm and nonlinear programming to solve the formulated problem. Extensive evaluations based on the real-world data set demonstrate that the proposed algorithm outperforms the baseline methods.

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform.

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

Multi-UAV Mobile Edge Computing and Path Planning Platform Based on Reinforcement Learning

Unmanned Aerial vehicles (UAVs) are widely used as network processors in mobile networks, but more recently, UAVs have been used in Mobile Edge Computing as mobile servers. However, there are significant challenges to use UAVs in complex environments with obstacles and cooperation between UAVs. We introduce a new multi-UAV Mobile Edge Computing platform, which aims to provide better Quality-of-Service and path planning based on reinforcement learning to address these issues. The contributions of our work include: 1) optimizing the quality of service for mobile edge computing and path planning in the same reinforcement learning framework; 2) using a sigmoid-like function to depict the terminal users’ demand to ensure a higher quality of service; 3) applying synthetic considerations of the terminal users’ demand, risk and geometric distance in reinforcement learning reward matrix to ensure the quality of service, risk avoidance, and the cost-savings. Simulations have shown the effectiveness and feasibility of our platform, which can help advance related researches. The source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bczhangbczhang</uri> .

Joint hierarchical placement and configuration of edge servers in C-V2X

To accommodate the high mobility and large-scale interconnection requirements of the Internet of Vehicles (IoVs) and to realize low-latency and high-reliability communication in IoVs, Cellular Vehicle-to-Everything (C-V2X) intelligent converged networks based on Mobile Edge Computing (MEC) is proposed. Various types of C-V2X applications impose diverse requirements on the network in terms of communication, computation, and storage, thus the MEC platform needs to be flexibly deployed in C-V2X. We can divide the MEC platform into Roadside-edge and Area-edge layers according to the service scope and business functions, and strategically select Road Side Units (RSUs) and Macro Base Stations (MBSs) with the optimal location in the Roadside-edge and Area-edge layer to place different types of Edge Servers (ESs). In the scenario of MEC and C-V2X convergence, we consider the trade-off between the response latency of computation requests and the placement cost, and modelled the hierarchical placement and configuration of ESs based on queuing theory. To reduce the complexity of the problem and improve the efficiency of the solution, heuristic algorithms are proposed to realize the joint hierarchical placement and configuration of ESs in C-V2X from the aspects of delay-aware, suitability evaluation, and spatial clustering. Finally, the real C-V2X scenarios are simulated based on the Poisson Line Process (PLP) and Poisson Point Process (PPP), and sufficient experimental results verify the effectiveness and superiority of the proposed algorithms.

A Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform

Largescale IoT applications with thousands of geo distributed IoT gadgets making huge volumes of data impose immense challenges to designing transmission mechanisms that offer data transfer has less latency and great scalability. In this work, an investigation of a hierarchical Edge-Cloud publishes or subscribe brokers method was performed with the help of an effective two-tier routing structure for alleviating such problems whenever sending event notices in large scale IoT mechanisms. In this technique, IoT gadgets use the benefits of nearby edge brokers deliberately positioned in edge network for data supplying services for minimizing latency. This manuscript introduces a Novel Metaheuristic Optimization based Clustering with Routing Scheme for IoT Mobile Edge Computing Platform, named MOCRS-IoTMEC model. The projected MOCRS-IoTMEC model is mainly concentrated on the identification of optimal routes in the IoT assisted MEC environment by the use of pigeon inspired optimization (PIO) algorithm. Also, the LEACH protocol is applied to initially cluster the IoT devices. The PIO algorithm is applied to determine the fitness function to choose optimal routes. To depict the enhanced performance of the MOCRS-IoTMEC model, a detailed comparison study is made. The experimental outcomes reported the enhanced execution of the MOCRS-IoTMEC method over other approaches.

Hybrid Beamforming Design and Resource Allocation for UAV-Aided Wireless-Powered Mobile Edge Computing Networks With NOMA

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

Application Loading and Computing Allocation for Collaborative Edge Computing

The emergence of Mobile Edge Computing (MEC) provides a near-field tasking platform to meet the latency requirements of the growing number of compute-intensive mobile applications. However, physical memory constraints limit the number of application services that can be loaded on the edge server (ES) simultaneously, and the non-uniform distribution of traffic in the mobile network makes it difficult to fully utilize the resources in the edge network. In addition, for MEC platform operators, how to achieve the expected profit of application service providers (ASP) to attract more ASPs is also important. To address these issues, in this paper we propose an ASP profit-aware solution for jointly optimizing application loading, task allocation, and compute resource allocation across multi-ES, minimizing system latency while maintaining ASP profitability. We first formulate the problem as a long-term stochastic optimization problem with ASP profit constraints, transform it into a single time slot optimization problem using the Lyapunov optimization framework, and then, using the power of genetic algorithms (GA), we propose an online heuristic algorithm to obtain a near-optimal strategy for each time slot. Simulation results show that our algorithm is effective in reducing system latency in the long term, while demonstrating performance that ensures more ASPs with desired profits.

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

Lightweight Convolution Neural Networks for Mobile Edge Computing in Transportation Cyber Physical Systems

Cloud computing extends Transportation Cyber-Physical Systems (T-CPS) with provision of enhanced computing and storage capability via offloading computing tasks to remote cloud servers. However, cloud computing cannot fulfill the requirements such as low latency and context awareness in T-CPS. The appearance of Mobile Edge Computing (MEC) can overcome the limitations of cloud computing via offloading the computing tasks at edge servers in approximation to users, consequently reducing the latency and improving the context awareness. Although MEC has the potential in improving T-CPS, it is incapable of processing computational-intensive tasks such as deep learning algorithms due to the intrinsic storage and computing-capability constraints. Therefore, we design and develop a lightweight deep learning model to support MEC applications in T-CPS. In particular, we put forth a stacked convolutional neural network (CNN) consisting of factorization convolutional layers alternating with compression layers (namely, lightweight CNN-FC). Extensive experimental results show that our proposed lightweight CNN-FC can greatly decrease the number of unnecessary parameters, thereby reducing the model size while maintaining the high accuracy in contrast to conventional CNN models. In addition, we also evaluate the performance of our proposed model via conducting experiments at a realistic MEC platform. Specifically, experimental results at this MEC platform show that our model can maintain the high accuracy while preserving the portable model size.

Study on Dynamic Service Migration Strategy with Energy Optimization in Mobile Edge Computing

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.

Mobile Edge Computing Platform with Container-Based Virtualization Technology for IoT Applications

The next generation of fifth generation (5G) network, implementing mobile edge computing (MEC), network function virtualization (NFV) and software defined networking technologies, establishes a flexible and resilient network in line with various internet of things (IoT) devices. While NFV adds flexibility scale in or out networks by allowing network functions to be dynamically deployed and inter-connected, MEC provide intelligence at the edge of a mobile network; reduces latency, and increases capacity. With the diverse development of networking applications, the proposed MEC with container-based virtualization technology (CVT) as IoT gateway with IoT devices for flow control mechanism in scheduling and analysis methods will effectively enhance the quality of service. In this work, the proposed IoT gateway will be analyzed to elucidate the combined effect of simultaneously deploying virtual network functions and MEC applications on the same network infrastructure. Low latency, high bandwidth and high agility, supporting the connection of large-scale devices, and the efficient combination of resources from network edge and cluster clouds, account for real-time network conditions, reducing the IoT applications and services to indicate that a number is the average of 30% of the latency, that could get more suitable service quality to develop such as both augmented reality and virtual reality application intelligence in coming 5G network.

Mobile edge computing towards 5G: Vision, recent progress, and open challenges

Mobile Edge Computing (MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers. It allows the availability of the cloud servers inside or adjacent to the base station. The end-to-end latency perceived by the mobile user is therefore reduced with the MEC platform. The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC. The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers. This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features. The relevant state-of-the-art research efforts are then surveyed. The paper finally discusses and identifies the open research challenges of MEC.