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

• 作者: Biyao Li Lanlan Rui Yabin Qin Zhipeng Gao
• 来源期刊: 工程与科学中的计算机建模(英文)
• 学科: 工学
• 关键词: Internet of things(IoT) edge COMPUTING AUGMENTED Reality(AR) maintenance communication network.
• 年，卷（期）: 2019,（2）
• 页码范围: 291-315
• 页数: 25页
• 分类号: TN9

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.