@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}
}