关键词： Computing capabilities   Hybrid firefly algorithm   Mobile edge computing   Mobile switching center  

摘要： Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of MSC deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisation techniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. A game base theoretical model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the Hybrid Firefly Algorithm (HFA) for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model. © 2019, Institute of Advanced Scientific Research, Inc. All rights reserved.

关键词： Intelligent transportation systems   vehicular and wireless technologies   Internet of Things   5G   DSRC   SDN   mobile cloud computing   mobile edge computing  

摘要： Internet of Vehicles is a specific application of Internet of Things technology in intelligent transportation systems, and has attracted attention of relevant research institutions, automobile manufacturers and communication technology suppliers all over the world. The US government has spent hundreds of millions of dollars on DSRC development, which is a technology that can help achieve V2V and V2I communications, and enable vehicles to communicate with intelligent traffic lights to mitigate traffic congestion and accidents or bad weather on the road. Automakers such as GM and Toyota have well deployed Wi-Fi-based DSRC technology. But with the continuous development of 5G and D2D communications, cellular technology tends to be a strong candidate for V2X communications..Although 5G communication technology does not yet have a unified standard, its related concepts and trends have been recognized by industry area. Automobile manufacturers represented by Ford and BMW expect to achieve a leap in automotive networking through 5G. In the face of the 5G era, the dispute between DSRC and LIE standards has become a hot topic in the field of car networking. DSRC, a dedicated short-range communication developed and tested by automotive technology suppliers is under threat, and the next generation of 5G technology might be the final answer. Based on this point, this paper firstly introduces the development history of the Internet of Vehicles communication standard, and analyzes the advantages and disadvantages of DSRC and cellular network communication technology. Then, the three core elements of the Internet of Vehicles (Node Performance, local Network, and Internet of Things) are discussed for the development trend of vehicle networking communication technology in the context of 5G. Finally, this chapter explains the impact of the development of 5G communication technology on the future development of vehicle networking, and proposes the possible development directions of fu

关键词： Computation offloading   heterogeneous networks   mobile edge computing   resource allocation   wireless backhaul  

摘要： Considered as a key technology in 5G networks, mobile edge computing (MEC) can support intensive computation for energy-constrained and computation-limited mobile users (MUs) through offloading various computation and service functions to the edge of mobile networks. In addition to MEC, wireless heterogeneous networks will play an important role in providing high transmission capacity for MUs in 5G, where wireless backhaul is a cost-effective and viable solution to solve the expensive backhaul deployment issue. In this paper, we consider a setting, where MUs can offload their computations to the MEC server through a small cell base station (SBS), the SBS connects to the macro BS through a wireless backhaul, and computation resource at the MEC server is shared among offloading MUs. First, we formulate a joint optimization problem with the goal of minimizing the system-wide computation overhead. This is a mixed-integer problem and hard to derive the optimal solution. To solve this problem, we propose to decompose it into two subproblems, namely the offloading decision subproblem and the joint backhaul bandwidth and computation resource allocation subproblem. An algorithm, namely JOBCA, is proposed to obtain a feasible solution to the original problem by solving two subproblems iteratively. Finally, numerical results are conducted to verify the performance improvement of the proposed algorithm over two baseline algorithms and the close performance of the proposed algorithm compared with the centralized exhaustive search.

关键词： Mobile edge computing   communication and computation cooperation   joint optimization   convex optimization   block coordinate descent  

摘要： The advantages of mobile edge computing (MEC) in lower energy consumption, improved bandwidth and reduced delay have attracted extensive studies. We consider a multiple smart wearable devices (SWDs) single smart mobile device (SMD) MEC system where the computation-intensive tasks can be offloaded to SMD and MEC server to improve resources utilization in this paper. To further minimize the energy consumption and satisfy the delay constraints of SWDs, SMD cooperation is introduced and we jointly optimize two-tier computation and communication resources in this system to achieve our objective. The formulated problem is a non-convex optimization problem where we design an efficient iterative algorithm by integrating the block coordinate descent and convex optimization techniques together. The proposed algorithm ensures convergence to a suboptimal solution based on the properties of convex optimization problem. In particular, the original non-convex optimization problem is decomposed into two convex optimization subproblems. Furthermore, we simplify the first subproblem using Lagrange method which is transformed into solving a set of non-linear equations. The extensive simulation results indicate that the proposed algorithm by jointly optimizing two-tier computation and communication resources can reduce energy consumption compared with other baseline methods. The solution of proposed algorithm is close to the optimal solution and can be generated in a shorter time.

关键词： Task analysis   Servers   Resource management   Mobile handsets   Computational modeling   Edge computing   Optimization   Mobile edge computing   resource allocation   optimization  

摘要： Due to explosive growth in mobile applications and services with different requirements, the concept of mobile edge computing (MEC) has emerged. For MEC, a mobile user (MU) and a MEC server need to exchange tasks using limited radio resources. Furthermore, when multiple MUs possess tasks, the MEC server has to handle multiple tasks simultaneously. Thus, the radio and computing resources need to be allocated to MUs by taking into account the wireless channel condition and the computing power of MUs and the MEC server. In this paper, a radio resource and computing resource allocation scheme is proposed to minimize the total processing completion time of all the tasks. Each task is assumed to be divided into local task and offload task. The local task is processed by each MU while the offload task is processed by a MEC server. We first formulate the optimization problem to minimize the total processing completion time of all tasks. To solve the formulated optimization problem, we propose a two-step radio and computing resources allocation scheme which iteratively performs bisection search method and Johnson's algorithm. The numerical results elucidate that the proposed scheme can reduce the total processing completion time by about 25% on average compared to the conventional schemes when multiple MUs have divisible tasks.

关键词： feature extraction   mobile edge computing   image recognition   mobile cloud computing  

摘要： With the explosive growth of mobile devices, it is feasible to deploy image recognition applications on mobile devices to provide image recognition services. However, traditional mobile cloud computing architecture cannot meet the demands of real time response and high accuracy since users require to upload raw images to the remote central cloud servers. The emerging architecture, Mobile Edge Computing (MEC) deploys small scale servers at the edge of the network, which can provide computing and storage resources for image recognition applications. To this end, in this paper, we aim to use the MEC architecture to provide image recognition service. Moreover, in order to guarantee the real time response and high accuracy, we also provide a feature extraction algorithm to extract discriminative features from the raw image to improve the accuracy of the image recognition applications. In doing so, the response time can be further reduced and the accuracy can be improved. The experimental results show that the combination between MEC architecture and the proposed feature extraction algorithm not only can greatly reduce the response time, but also improve the accuracy of the image recognition applications.

关键词： Task scheduling   smart city   mobile edge computing   Internet of Vehicle   alternating direction method of multipliers (ADMM) algorithm  

摘要： The smart city is increasingly gaining worldwide attention. It has the potential to improve the quality of life in convenience, at work, and in safety, among many others' utilizations. Nevertheless, some of the emerging applications in the smart city are computation-intensive and time-sensitive, such as real-time vision processing applications used for public safety and the virtual reality classroom application. Both of them are hard to handle due to the quick turnaround requirements of ultra-short time and large amounts of computation that are necessary. Fortunately, the abundant resource of the Internet of Vehicles (IoV) can help to address this issue and improve the development of the smart city. In this paper, we focus on the problem that how to schedule tasks for these computation-intensive and time-sensitive smart city applications with the assistance of IoV based on multi-server mobile edge computing. Task scheduling is a critical issue due to the limited computational power, storage, and energy of mobile devices. To handle tasks from the aforementioned applications in the shortest time, this paper introduces a cooperative strategy for IoV and formulates an optimization problem to minimize the completion time with a specified cost. Furthermore, we develop four evolving variants based on the alternating direction method of multipliers (ADMM) algorithm to solve the proposed problem: variable splitting ADMM, Gauss-Seidel ADMM, distributed Jacobi ADMM, and distributed improved Jacobi (DIJ)-ADMM algorithms. These algorithms incorporate an augmented Lagrangian function into the original objective function and divide the large problem into two sub-problems to iteratively solve each sub-problem. The theoretical analysis and simulation results show that the proposed algorithms have a better performance than the existing algorithms. In addition, the DIJ-ADMM algorithm demonstrates optimal performance, and it converges after approximately ten iterations and improves the

关键词： Mobile edge computing   mobile edge caching   5G   content management   content delivery  

摘要： With the widespread adoption of various mobile applications, the amount of traffic in wireless networks is growing at an exponential rate, which exerts a great burden on mobile core networks and backhaul links. Mobile edge caching, which enables mobile edges with cache storages, is a promising solution to alleviate this problem. In this paper, we aim to review the state-of-the-art of mobile edge caching. We first present an overview of mobile edge caching and its advantages. We then discuss the locations where mobile edge caching can be realized in the network. We also analyze different caching criteria and their respective effects on the caching performances. Moreover, we compare several caching schemes and discuss their pros and cons. We further present a detailed and in-depth discussion on the caching process, which can be delineated into four phases including content request, exploration, delivery, and update. For each phase, we identify different issues and review related works in addressing these issues. Finally, we present a number of challenges faced by current mobile edge caching architectures and techniques for further studies.

关键词： Mobile edge computing   migration strategy   quality of services   vehicle to network services  

摘要： In 5G mobile networks, the convergence of cloud computing and communication leads to mobile edge computing, benefiting vehicular networks. However, the advent of a wide variety of new services and devices has changed the vehicular network landscape, challenging vehicle-to-network services' migrations. In this paper, we focus on optimizing long-term average latency of multiple services with a different quality of services (QoS). We first introduce an offline algorithm, which can be used to find the optimal migration strategy of services. Then, we analyze the negative effect of trajectory prediction and suggest an optimizing method to reduce this effect by partial updating. Finally, based on this method, we propose a partial dynamic optimization algorithm to approximate the optimal solution, by integrating the priority queue, which utilizes QoS information. We simulate the average service latency, confirming that the proposed partial dynamic optimization algorithm keeps a stable service latency and performs better than other existing algorithms, considering the negative effect of trajectory prediction. We also verify that the proposed algorithm can meet the low latency requirement of the vehicles and the different demands of different services. Besides, the partial dynamic optimization algorithm has a lower time complexity.

关键词： Mobile edge computing   cognitive radio   imperfect spectrum sensing   wireless power transfer   semi-closed form  

摘要： Most mobile edge computing (MEC) works assume that the mobile devices (MDs) can offload their tasks to the MEC-severs at anytime, which may not be a practical assumption due to the tension between a large number of MDs and the scarce spectrum resources. In this paper, a framework for wireless powered cognitive radio (CR)-based MEC-enabled networks is proposed, which integrates three technologies: MEC, CR, and wireless power transfer (WPT). CR technology with imperfect spectrum sensing is adopted by the MD to find the spectrum access opportunities. An optimization problem is formulated to maximize the average calculated number of bits (CNoB) of the MD, which is non-convex and hard to solve. A two-loop procedure using a one-dimensional line search method is proposed. The time for spectrum sensing, WPT, energy harvesting (EH) and offloading, the central processing unit (CPU) frequency, and the transmit power of the MD are jointly optimized. Some semi-closed form solutions are obtained through Lagrangian dual decomposition and successive pseudo-convex approximation (SPCA) methods. Simulation results are presented to show the effectiveness of the proposed CR-based MEC scheme with different parameters.