TY - EJOU AU - Rui, Lanlan AU - Qin, Yabin AU - Li, Biyao AU - Gao, Zhipeng TI - Context-Based Intelligent Scheduling and Knowledge Push Algorithms for AR-Assist Communication Network Maintenance T2 - Computer Modeling in Engineering \& Sciences PY - 2019 VL - 118 IS - 2 SN - 1526-1506 AB - 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. KW - Internet of things (IoT) KW - edge computing KW - Augmented Reality (AR) KW - maintenance KW - communication network DO - 10.31614/cmes.2018.04240