关键词： Mobile edge computing   latency minimization   physical layer security   joint optimization  

摘要： This letter proposes a low-latency secure mobile edge computing (MEC) system where multiple users offload computing tasks to a base station in the presence of an eavesdropper. We jointly optimize the users' transmit power, computing capacity allocation, and user association to minimize the computing and transmission latencies over all users subject to security and computing resource constraints. Numerical results show that our proposed algorithm outperforms baseline strategies. Furthermore, we highlight a novel trade-off between the latency and security of MEC systems.

关键词： mobile edge computing   auction theory   resource allocation   incentive mechanism  

摘要： Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.

关键词： Data allocation   Differential evolution   loT   Mobile edge computing   Request processing  

摘要： Internet of Things (IoT) is an emergent communication technology connecting heterogeneous users, services and applications for granting seamless support for end-user demands. The heterogeneous nature of the devices and intelligent computing, storage, and processing paradigms are exploited for serving different application requests of the end-user devices. Edge computing inherits the functions of IoT platform to the brim of the user network to grant tardiness-free service/ communication support. This manuscript discusses a novel delay-aware concurrent data management (DCDM) method for IoT collaborative mobile edge computing (MEC) environment. This method categorizes request and response data independently in the edge layer through timeout and request density based optimization. A differential evolution (DE) based optimization is introduced for optimizing the constraints of the framework in favor of concurrent request processing and precise data allocation. This method is tested through appropriate experiments and the corresponding results are verified to prove the consistency of the method. The metrics such as serviced and processed request, response time, resource utilization, and communication span are validated using the experimental results. The performance of the proposed DCDM is compared with the existing methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Computation offloading   Deep learning   Mobile edge computing   Energy and performance optimization  

摘要： With the benefit of partially or entirely offloading computations to a nearby server, mobile edge computing gives user equipment (UE) more powerful capability to run computationally intensive applications. However, a critical challenge emerged: how to select the optimal set of components to offload considering the UE performance as well as its battery usage constraints. In this paper, we propose a novel energy and performance efficient deep learning based offloading algorithm. The optimal offloading schemes of components based on remaining energy and its performance can be determined by our proposed algorithm. All of these considerations are modeled as a cost function;then, a deep learning network is trained to compute the solution by which the optimal offloading scheme can be determined. Experimental results show that the proposed method is superior to existing methods in terms of energy and performance constraints.

关键词： ATSA algorithm   Cloud computing   Data center   Mobile cloud computing   Mobile edge computing  

摘要： Mobile Edge Computing (MEC) is a developing technology in which Cloud Computing advantages were prolonged towards the edge of networks making use of mobile base stations. In Mobile Cloud Computing (MCC), the multiple numbers of data are produced by mobile users then that data is sent to the remote cloud to make an additional process around the Internet. Due to this process delay occurs because of long-distance. In this paper, Server Efficiency based Scheduling Algorithm for Mobile Edge Computing (SESA), is proposed to reduce the time consumption of running or scheduling the tasks. The Data Center has the scheduler to schedule the task based on the priority of the incoming task. Priority can be measured using Ageing based Task Scheduling Algorithm for Mobile Edge Computing (ATSA) algorithm. © 2020 SERSC.

关键词： Mobile edge computing  

摘要： Network function Virtualization (NFV) and Mobile Edge Computing (MEC) are promising 5G technologies to support resource-demanding mobile applications. In NFV, one must process the service function chain (SFC) in which a set of network functions must be executed in a specific order. Moreover, the MEC technology enables computation offloading of service requests from mobile users to remote servers to potentially reduce energy consumption and processing delay for the mobile application. This paper considers the optimization of the computation offloading, resource allocation, and SFC placement in the multi-site MEC system. Our design objective is to minimize the weighted normalized energy consumption and computing cost subject to the maximum tolerable delay constraint. To solve the underlying mixed integer and non-linear optimization problem, we employ the decomposition approach where we iteratively optimize the computation offloading, SFC placement and computing resource allocation to obtain an efficient solution. Numerical results show the impacts of different parameters on the system performance and the superior performance of the proposed algorithm compared to benchmarking algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Wireless powered mobile edge computing (WP-MEC) has been recognized as a promising technique to provide both enhanced computational capability and sustainable energy supply to massive low-power wireless devices. However, its energy consumption becomes substantial, when the transmission link used for wireless energy transfer (WET) and for computation offloading is hostile. To mitigate this hindrance, we propose to employ the emerging technique of intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of providing an additional link both for WET and for computation offloading. Specifically, we consider a multi-user scenario where both the WET and the computation offloading are based on orthogonal frequency-division multiplexing (OFDM) systems. Built on this model, an innovative framework is developed to minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing the power allocation of the WET signals, the local computing frequencies of wireless devices, both the sub-band-device association and the power allocation used for computation offloading, as well as the IRS reflection coefficients. The major challenges of this optimization lie in the strong coupling between the settings of WET and of computing as well as the unit-modules constraint on IRS reflection coefficients. To tackle these issues, the technique of alternative optimization is invoked for decoupling the WET and computing designs, while two sets of locally optimal IRS reflection coefficients are provided for WET and for computation offloading separately relying on the successive convex approximation method. The numerical results demonstrate that our proposed scheme is capable of monumentally outperforming the conventional WP-MEC network without IRSs. Quantitatively, about 80% energy consumption reduction is attained over the conventional MEC system in a single cell, where 3 wireless devices are served via 16 sub-bands, with the aid of an IRS comprising of 50 elements. C

关键词： autonomous vehicles   tactile internet   mobile edge computing   5G   mobility  

摘要： With the recent advances and development of autonomous control systems of cars, the design and development of reliable infrastructure and communication networks become a necessity. The recent release of the fifth-generation cellular system (5G) promises to provide a step towards reliability or a panacea. However, designing autonomous vehicle networks has more requirements due to the high mobility and traffic density of such networks and the latency and reliability requirements of applications run over such networks. To this end, we proposed a multilevel cloud system for autonomous vehicles which was built over the Tactile Internet. In addition, base stations at the edge of the radio-access network (RAN) with different technologies of antennas are used in our system. Finally, simulation results show that the proposed system with multilevel clouding can significantly reduce the round-trip latency and the network congestion. In addition, our system can be adapted in the mobility scenario.

关键词： Industrial Internet   Reference architectures   Cloud computing   Mobile edge computing   Fog computing  

摘要： The Industrial Internet is a promising technology combining industrial systems with Internet connectivity to significantly improve the product efficiency and reduce production cost by cooperating with intelligent devices,in which the advanced computing,big data analysis and intelligent perception techniques have been *** paper comprehensively surveys the recent advances of the Industrial Internet,including reference architectures,key technologies,relative applications and future *** architectures which have been proposed for different application scenarios and their corresponding characteristics are *** technologies,such as cloud computing,mobile edge computing,fog computing,which are classified according to different layers in the architecture,are presented to support a variety of applications in the Industrial ***,future challenges and research trends are discussed as well to promote further research of the Industrial Internet.