Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance

查看全文 作　　者：Lanlan [1]Rui;Yabin [1]Qin;Biyao [1]Li;Zhipeng [1]Gao

机构地区：[1]State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing,100876,China

出　　处：《Computer Modeling in Engineering & Sciences》索引2019年第2期,共25页

摘　　要：Maintenance is an important aspect in the lifecycle of communication network devices.Prevalent problems in the maintenance of communication networks include inconvenient data carrying and sub-optimal scheduling of work orders,which significantly restrict the efficiency of maintenance work.Moreover,most maintenance systems are still based on cloud architectures that slow down data transfer.With a focus on the completion time,quality,and load balancing of maintenance work,we propose in this paper a learning-based virus evolutionary genetic algorithm with multiple quality-ofservice(QoS)constraints to implement intelligent scheduling in an edge network.The algorithm maintains the diversity of the population and improves the speed of convergence using a fitness function and a learning-based population generation mechanism.The test results demonstrate that the algorithm delivers good performance in terms of load balancing and QoS guarantee.We also propose a knowledge push algorithm based on a context model for intelligently pushing relevant knowledge according to the given conditions.The simulation results demonstrate that our scheme can improve the efficiency of on-site maintenance. 关键词：Internet of things(IoT) edge computing AUGMENTED Reality(AR) maintenance communication network.

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Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance

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