关键词： Mobile edge computing  

摘要： By offloading computing tasks to the edge of mobile network, mobile edge computing(MEC) solves the shortcomings of mobile devices in computing capacity and storage capacity. In order to reduce the computing delay of mobile terminal devices and the degree of workload balancing in MEC servers, a computing offloading solution based on information entropy immune algorithm is proposed. Firstly, the system model, delay model, load model and objective optimization model are constructed. Then, a task offloading scheme based on immune optimization is proposed .Finally, this scheme is compared with the Benchmark scheme and the offloading scheme in the literature. Simulation experiments show that the proposed scheme can effectively reduce the average offloading delay, and can effectively balance the load between MEC servers. At the same time, compared with the schemes in the literature, the performance of the scheme is improved to a certain extent. © 2021, CC BY.

关键词： fog computing   edge computing   mobile edge computing   resource allocation,offloading   software defined networks   smart city   smart grid  

摘要： Smart cities improve the quality of life of people by utilizing the benefits of Information and communication technology(ICT)and the Internet of things(IoT).The applications of the smart city often rely on the cloud for *** doubt cloud provides an ample amount of resources as a service but still it has limitations in terms of unreliable latency,mobility,and location awareness due to their multi-hop distance from the IoT *** computing avoids these limitations by providing services nearer to the *** this work we investigate the already proposed IoT-Fog based application specific smart city architectures and review them based on scalability,heterogeneity,mobility,energy conservation,latency,and ***,we discuss the applications and highlight the challenges that fog computing *** also present a case study of a smart city scenario with multiple applications of IoT.

关键词： e-commerce   customer behavior   Pareto/NBD model   SVR model   ARIMA model   mobile edge computing  

摘要： Mobile edge computing is trending nowadays for its computation efficiency and *** rapid development of e-commerce show great interest in mobile edge computing due to numerous rise of small and middle-sized enterprises(SMEs)in the *** paper predicts the overall sales volume of the enterprise through the classic ARIMA model,and notes that the behavior and arrival differences between the new and old customer groups will affect the accuracy of our forecasts,so we then use Pareto/NBD to explore the repeated purchases of customers at the individual level of the old customer and the SVR model to predict the arrival of new customers,thus helping the enterprise to make layered and accurate marketing of new and old customers through machine *** general,machine learning relies on powerful computation and storage resources,while mobile edge computing typically provides limited computation resources ***,it is essential to combine machine learning with mobile edge computing to further promote the proliferation of data analysis among SMEs.

关键词： Mobile edge computing   Multicast   Stochastic optimization   Resource allocation  

摘要： Mobile edge computing (MEC) is rising as a key technology for computation-intensive and delay-sensitive applications. Many works have focused on MEC, but most of them only consider unicast scenarios, and ignore multicast issues. The reason is that MEC aiming at personalized computing of users conflicts with multicast which demands the same data stream. This makes MEC and multicast seem inconsistent. However, in fact, there will be lots of services, whose computation process is different but the result may be same (e.g. media push), which can greatly benefit from multicast over MEC and make the combination of multicast and MEC meaningful. In this paper, we first consider challenges and propose multicast-aware resource allocation for MEC, which jointly optimizes computing and caching in multicast scenarios. We formulate the problem by considering user request, network communication, service caching and service computing. But this model is knotty because it is an optimization problem with mixed discrete and continuous variables. Besides its optimization objective is the average value over a long time. Considering the complexity, we first transform the problem into an online optimization, which jointly minimizes the average time delay and energy consumption, by stochastic optimization. Then we separate the discrete variables and continuous variable, and decompose the problem into two subproblems. By solving subproblems, an efficient online algorithm called MA-ECC is proposed. Finally, we compare it with other three baseline methods, and result shows that MA-ECC can effectively reduce service latency while still keeping energy consumption low.

关键词： UAV   mobile edge computing   differential evolution algorithm   K-Means   edge data collection  

摘要： In the IoT(Internet of Things)system,the introduction of UAV(Unmanned Aerial Vehicle)as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery ***,the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the *** this work,to deal with the problem,a deployment model of a mobile edge computing(MEC)system based on multi-UAV is *** goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of *** DEVIPSK(differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means)is proposed to solve the *** DEVIPSK,the population is initialized by K-Means to obtain better initial positions of ***,considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm,a mutation strategy pool is used to update the positions of *** experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system.

关键词： Mobile Edge Computing   Vehicular cloud   Vehicular micro cloud   Data management  

摘要： Edge computing is becoming a major building block of next generation 5G/6G networks. However, infrastructure might not always be available because of slow deployment. At the same time, vehicular networks are becoming a reality now and cars are being equipped with a variety of short-range communication devices. The idea of vehicular micro clouds is to turn cars into (virtual) edge computing infrastructure. One of the challenging questions in this domain is to maintain data within and among such micro clouds. In this paper, we focus on this task and present a novel solution for such data exchange between vehicular micro clouds. For efficient operation, the dwell times of cars in such a micro cloud need to be known or accurately predicted. In an extensive study based on trace data, we investigate the distribution of dwell times of cars at intersections. We make use of this distribution as an input for designing an improved data exchange algorithm. As not all intersections are the same, adding additional variance further benefits the solution. We evaluated our algorithm in different vehicular densities, and we observed that we could maintain data 22-208% longer within the micro clouds using our new algorithm. Overall, our results show that our algorithm clearly outperforms previous solutions.

关键词： Mobile edge computing   task offloading   service caching   dependency   approximation  

摘要： In Mobile Edge Computing (MEC), many tasks require specific service support for execution and in addition, have a dependent order of execution among the tasks. However, previous works often ignore the impact of having limited services cached at the edge nodes on (dependent) task offloading, thus may lead to an infeasible offloading decision or a longer completion time. To bridge the gap, this article studies how to efficiently offload dependent tasks to edge nodes with limited (and predetermined) service caching. We formally define the problem of offloading dependent tasks with service caching (ODT-SC), and prove that there exists no algorithm with constant approximation for this hard problem. Then, we design an efficient convex programming based algorithm (CP) to solve this problem. Moreover, we study a special case with a homogeneous MEC and propose a favorite successor based algorithm (FS) to solve this special case with a competitive ratio of O(1). Extensive simulation results using Google data traces show that our proposed algorithms can significantly reduce applications' completion time by about 21-47 percent compared with other alternatives.

关键词： Edge computing   Mobile edge computing   Edge server   Placement   NSGA-II  

摘要： With the advent of mobile Internet and IoT era, various smart terminals generate a large amount of data at the edge of the network, and how to transmit and process these data at high speed poses a challenge to the traditional communication networks. Edge computing, as an emerging framework, can improve the communication capability and data processing capacity of traditional communication networks by improving their architecture. Edge server placement (ESP) technology is one of the key technologies of edge computing, which can effectively reduce data transmission delay and improve data processing efficiency by placing edge servers (ESs) with computing and data storage functions at base stations to sink some functions of the core network to the edge of the network. In this paper, we study the k edge servers placement problem (KESP problem) in smart cities. We first elaborate it as a multi-objective optimization problem for optimal workload balancing and system delay under constraints. Then a modified multi-objective non-dominated sorting genetic algorithm with elite policy (MNSGA-II) is proposed to optimize this problem. Finally, simulations are performed based on real network datasets. The simulation results show that MNSGA-II reduces the system overhead by about 38.4%, 40.6%, and 59.3% on average compared to Random, K-Means, and Top-K.

关键词： Task analysis   Unmanned aerial vehicles   Fading channels   Minimization   Resource management   Computational modeling   Approximation algorithms   Internet of Things   mobile edge computing   trajectory optimization   UAV communication  

摘要： The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs' trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms.

关键词： Mobile edge computing   Handover authentication   Mobility patterns  

摘要： With the advancement in technology and the exponential growth of mobile devices, network traffic has increased manifold in cellular networks. Due to this reason, latency reduction has become a challenging issue for mobile devices. In order to achieve seamless connectivity and minimal disruption during movement, latency reduction is crucial in the handover authentication process. Handover authentication is a process in which the legitimacy of a mobile node is checked when it crosses the boundary of an access network. This paper proposes an efficient technique that utilizes mobility patterns of the mobile node and mobile Edge computing framework to reduce handover authentication latency. The key idea of the proposed technique is to categorize mobile nodes on the basis of their mobility patterns. We perform simulations to measure the networking latency. Besides, we use queuing model to measure the processing time of an authentication query at an Edge servers. The results show that the proposed approach reduces the handover authentication latency up to 54% in comparison with the existing approach.