关键词： Task analysis   Resource management   Edge computing   Path planning   Optimization   Processor scheduling   Mobile computing   Mobile edge computing   vehicles   computation task offloading   path planning   piecewise linear approximation  

摘要： With the rapid development of mobile applications, mobile edge computing (MEC), which provides various cloud resources (e.g., computation and storage resources) closer to mobile and IoT devices for computation offloading, has been broadly studied in both academia and industry. However, due to the limited coverage of static edge servers, the traditional MEC technology performs badly in a nowadays environment. To adapt the diverse demands, in this paper, we propose a novel mobile edge mechanism with a vehicle-mounted edge (V-edge) deployed. Aiming at maximizing completed tasks of V-edge with sensitive deadline, the problem of joint path planning and resource allocation is formulated into a mixed integer nonlinear program (MINLP). By utilizing the piecewise linear approximation and linear relaxation, we transform the MINLP into a mixed integer linear program (MILP). To obtain the near-optimal solution, we further develop a gap-adjusted branch & bound algorithm, also called GA-B&B algorithm. Moreover, we propose a low-complexity L-step lookahead branch scheme (referred to as L-step scheme) for efficient scheduling in large-scale scenarios. Extensive evaluations demonstrate the superior performance of the proposed scheme compared with the traditional static edge mechanism. Furthermore, the proposed L-step scheme achieves close performance to the near-optimal solution, and significantly improves the task completion percentage of state-of-the-art schemes by over 10 percent.

关键词： Mobile edge computing   offloading decision   resource allocation   charge to UEs  

摘要： Mobile edge computing (MEC) brings a breakthrough for Internet of Things (IoT) for its ability of offloading tasks from user equipments (UEs) to nearby servers which have rich computation resource. 5G network brings a huge breakthrough on transmission rate. Together with MEC and 5G, both execution delay of tasks and time delay from downloading would be shorter and the quality of experience (QoE) of UEs can be improved. Considering practical conditions, the computation resource of an MEC server is finite to some extent. Therefore, how to prevent the abuse of MEC resource and further allocate the resource reasonably becomes a key point for an MEC system. In this paper, an MEC system with multi-user is considered where a base station (BS) with an MEC server, which can not only provide computation offloading service but also data cache service. Especially, we take the charge for both data transmission and task computation as one part of total cost of UEs, and then explore a joint optimization for downlink resource allocation, offloading decision and computation resource allocation to minimize the total cost in terms of the time delay and the charge to UEs. The proposed problem is formulated as a mixed integer programming (MIP) one which is NP-hard. Therefore, we decouple the original problem into two subproblems which are downlink resource allocation problem and joint offloading decision and computation resource allocation problem. Then we address these two subproblems by using convex and nonconvex optimization techniques, respectively. An iterative algorithm is proposed to obtain a suboptimal solution in polynomial time. Simulation results show that our proposed algorithm performs better than benchmark algorithms.

关键词： Mobile edge computing   D2D communications   collaborative content distribution   device caching   device matching   and channel allocation  

摘要： While mobile networks are evolving rapidly, the battle between ever-growing traffic demands and out-paced network capacities will continue and require more efficient solutions. Emerging techniques such as mobile edge computing and device-to-device (D2D) communications can help relieve traffic at the mobile edge and accommodate surging traffic demands from various content-centric services. In this work, we focus on exploiting device caching and user collaboration to offload content distribution traffic. Specifically, we investigate the request offloading problem, which aims to appropriately select caching devices and maximize the content requests that can be fulfilled through D2D communications. Given the constraints of individual transmission and caching capacities, the number of available D2D channels, and information privacy with social-awareness, we can decouple the request offloading problem into two subproblems, i.e., the device caching and matching problem, and the D2D channel allocation problem. As we prove that both problems are NP-hard, we propose efficient algorithms that iteratively make a best local decision in each step. Simulation results show that the proposed algorithms perform fairly closely to optimal solutions in small-scale instances and outperform the reference schemes under various situations.

关键词： Cloud computing   Computer architecture   Edge computing   Industries   Servers   Quality of service   Security   Mobile edge computing   mobile cloud computing   fog computing   connected vehicles   V2X communication   architectural design  

摘要： As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can provide enabling technologies to realize these requirements and have attracted widespread attentions from both academia and industry. These requirements ask for a well-designed computing architecture to support the Quality-of-Service (QoS) of CV applications. Computation offloading techniques, such as cloud, edge, and fog computing, can help CVs process computation-intensive and large-scale computing tasks. Additionally, different cloud/edge/fog computing architectures are suitable for supporting different types of CV applications with highly different QoS requirements, which demonstrates the importance of the computing architecture design. However, most of the existing surveys on cloud/edge/fog computing for CVs overlook the computing architecture design, where they (i) only focus on one specific computing architecture and (ii) lack discussions on benefits, research challenges, and system requirements of different architectural alternatives. In this article, we provide a comprehensive survey on different architectural design alternatives based on cloud/edge/fog computing for CVs. The contributions of this article are: (i) providing a comprehensive literature survey on existing proposed architectural design alternatives based on cloud/edge/fog computing for CVs, (ii) proposing a new classification of computing architectures based on cloud/edge/fog computing for CVs: computation-aided and computation-enabled architectures, (iii) presenting a holistic comparison among different cloud/edge/fog computing architectures for CVs based on functional requirements of CV systems, including advantages, disadvantages, and research challenges, (iv) presenting a holistic overview on the design of CV systems from both academia and industry perspectives, including activities in industry, functional requirem

关键词： Cloud computing   Load management   Resource management   Servers   Heuristic algorithms   Edge computing   Mobile handsets   Mobile edge computing   cloudlet placement   affinity propagation algorithm   load balancing  

摘要： Cyber-Physical-Social Systems (CPSS) integrates the cyber, physical and social spaces together. There are a large number of mobile users in CPSS that need low latency services. Fortunately, mobile edge computing (MEC) is a novel technology which can provide such services. The edge server plays a key role in MEC, but how to manage the edge server is an important challenge. For one thing, the number of cloudlets and the resource are limited. For another, the number of mobile devices (MDs) is very large and randomly distributed. And thus, how to determine the suitable number of cloudlets while serving the maximum number of MDs is significant. To this end, a new cloudlet placement method based on improved Affinity Propagation (AP) algorithm is proposed to solve the above problems. More specially, the improved AP algorithm can obtain the least number of cloudlets while covering the largest number of MDs. In addition, the load balancing strategy is used to ensure that the load of each cloudlet maintains a balanced state. Last but not the least, our proposed method can be used in scenarios where users move.

关键词： Task analysis   Power system reliability   Probability   Edge computing   System performance   Wireless communication   Internet of Things   Mobile edge computing   latency   opportunistic selection   performance evaluation  

摘要： In this paper, we investigate intelligent mobile edge computing networks (MEC) with multiple computational access points (CAPs) for Internet of Things (IoT), where the user with limited computational capability has some tasks to be computed with the help of the CAPs. We use the communication and computational latency to measure the system outage probability. In order to enhance the system performance, two CAP relay selection criteria are proposed, where one is based on the computational capability at the CAPs while the other criterion is based on the instantaneous channel parameters from the user to the CAPs. To further improve the system performance, the offloading ratio is optimized through minimizing the system outage probability, which determines how many parts of tasks to be computed at the CAPs. Numerical and simulation results are finally presented to verify the effectiveness of the proposed CAP selection criteria and offloading ratio strategy.

关键词： Delay effects   Servers   Phase change materials   Global Positioning System   Autonomous vehicles   Earth   Calibration   Automatic driving   high precision positioning   one-way time delay   the transformation of a coordinate system   mobile edge computing  

摘要： Connected autonomous driving is considered as a potential way to improve traffic safety. However, when positioning information is delivered through network, the network time delay could result in additional positioning errors, which has been proved by the experiment done in this paper in XuChang automatic driving center that the influence of one-way time delay will lead to meter-scale positioning error. In this paper, we propose a positioning calibration mechanism for connected autonomous vehicles called PCM. PCM firstly estimates the network time delay and then saves the calibrated location information in the MEC server to reduce the positioning error caused by network time delay. Experiments in FangShan and XuChang automatic driving centers verify the performance of PCM, and the result shows that PCM can reduce the positioning error to centimeter-level. For example, under 100ms time delay, the average positioning error without PCM is 119.67cm, while the average positioning error with PCM is 3.03cm. Experimental results show that PCM can effectively reduce the positioning error caused by a one-way time delay to centimeter-level, which is strictly required for positioning autonomous vehicles in automatic driving.

关键词： Servers   NOMA   Task analysis   Energy consumption   Optimization   Delays   Resource management   Mobile edge computing   NOMA   server cooperation   full-duplex   joint computation and communication resources optimization  

摘要： Mobile edge computing (MEC) is fast becoming a key communication technique by enabling mobile users to offload their computation tasks to the edge servers. However, the computation resource of each MEC server is limited which may lead to a worse offloading experience of dense edge users. Besides, the communication and computation resources are usually unevenly distributed among different MEC servers which affect the computational efficiency of the MEC network. In this paper, we propose a non-orthogonal multiple access (NOMA) assisted MEC system with two near-far edge servers performing cooperative communication, i.e., the edge user employs NOMA to offload partial computation workloads to a nearer MEC server and a farther MEC server, then the nearer server decodes and forwards the farther server's task data by full-duplex relaying mode. Based on the above system model, we formulate an optimization problem of the total system energy consumption minimization by jointly optimizing the local CPU frequency, the power allocation for the user and nearer MEC server, the system time assignment and the task partition. Due to the optimization problem is non-convex, a joint communication and computation resource iterative optimization (JCCRIO) algorithm based on approximation and alternation is designed. Firstly, the local CPU frequency is optimized so as to transform the original problem into a simplified equivalent form. In this way, then the simplified minimization problem can be solved iteratively in two steps. Finally, we obtain the closed-form solutions to the optimization variables at each step. Numerical results show that the proposed NOMA-assisted cooperative MEC scheme is more effective against the terms of energy consumption reduction than comparable schemes.

关键词： Mobile edge computing   age of information   task offloading   resource allocation   Lyapunov optimization  

摘要： To improve the operational efficiency of smart city, smart devices extract informative status updates from sampled image and video data to intelligently monitor the surroundings. Mobile edge computing (MEC) is considered as an emerging technology to provide energy-constrained devices with enhanced computation capability by offloading tasks to nearby servers. In such circumstance, the freshness of obtained status updates is critical to system performance, which can be characterized by the concept of age of information (AoI). Due to resource contention among multiple devices, the problem of how to maintain the timeliness of task executing is not trivial. In this paper, we are interested in minimizing the age of obtained status updates by jointly optimizing task generation, computation offloading as well as communication and computational resource allocation under the average energy constraint at each device. To tackle the time couplings of task generation and computation offloading decisions, we leverage the Lyapunov optimization technique to convert the long-term stochastic optimization problem into a per-time slot deterministic optimization problem. In each time slot, an online algorithm is proposed to determine the task offloading and computation offloading strategy. Moreover, we theoretically prove that the proposed algorithm can be arbitrarily close to the optimal performance with the gap of O(1/V). Simulation results show that our proposed scheme achieves better performance when compared with existing schemes.

关键词： Mobile edge computing  

摘要： Classification of benign and malignant pulmonary nodules is a critical task for developing a Computer-Aided Diagnosis (CAD) system of lung cancer. However, the intelligent diagnosis technology is often limited by equipment and space. Therefore, a computer-aided diagnosis model is proposed running in the Mobile Edge Computing (MEC) environment. The novel lung nodule classification framework improved the Deep Convolutional Generative Adversarial Nets (DCGAN). Firstly, CT images after pre-processing are input into GAN to generate new images with similar features. Then, in the training stage, the derivative model of GAN is introduced into the classification of pulmonary nodules. The optimised function means the improved DCGAN has better anti-noise ability and achieves more accurate classification performance. The experimental results showed an accuracy of 88.88%. Compared with the existing methods, the proposed method is superior to other ones in terms of accuracy, sensitivity, specificity, and area under the ROC curve. Copyright © 2020 Inderscience Enterprises Ltd.