@Article{cmes.2018.04240, AUTHOR = {Lanlan Rui, Yabin Qin, Biyao Li, Zhipeng Gao}, TITLE = {Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance}, JOURNAL = {Computer Modeling in Engineering \& Sciences}, VOLUME = {118}, YEAR = {2019}, NUMBER = {2}, PAGES = {291--315}, URL = {http://www.techscience.com/CMES/v118n2/27461}, ISSN = {1526-1506}, ABSTRACT = {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-of-service (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.}, DOI = {10.31614/cmes.2018.04240} }