Minimizing the Age-of-Critical-Information: An Imitation Learning-Based Scheduling Approach Under Partial Observations
- 21 January 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Mobile Computing
- Vol. 21 (9), 3225-3238
- https://doi.org/10.1109/tmc.2021.3053136
Abstract
Age of Information (AoI) has become an important metric to evaluate the freshness of information, and studies of minimizing AoI in wireless networks have drawn extensive attention. In mobile edge networks, changes in critical levels for distinct information is important for users’ decision making, especially when merely partial observations are available. However, existing research has not yet addressed this issue, which is the subject of this paper. To address this issue, we first establish a system model, in which the information freshness is quantified by changes in its critical levels. We formulate Age-of-Critical-Information (AoCI) minimization as an optimization problem, with the purpose of minimizing the average relative AoCI of mobile clients to help them make timely decisions. Then, we propose an information-aware heuristic algorithm that can reach optimal performance with full obsevations in an offline manner. For online scheduling, an imitation learning-based scheduling approach is designed to choose update preferences for mobile clients under partial observations, where policies obtained by the above heuristic algorithm are utilized for expert policies. Finally, we demonstrate the superiority of our designed algorithm from both theoretical and experimental perspectives.Keywords
Funding Information
- Hong Kong RGC Research Impact Fund (R5060-19, R5034-18)
- General Research Fund (152221/19E, 15220320/20E)
- Collaborative Research Fund (C5026-18G)
- National Natural Science Foundation of China (61872310, 61971084, 62001073)
- Chongqing Talent Program (CQYC2020058659)
- Fundamental Research Funds for the Central Universities (2019SJ02)
- National Science Foundation (CCF-1908308)
This publication has 28 references indexed in Scilit:
- Privacy-Preserving Content Dissemination for Vehicular Social Networks: Challenges and SolutionsIEEE Communications Surveys & Tutorials, 2018
- Scheduling Policies for Minimizing Age of Information in Broadcast Wireless NetworksIEEE/ACM Transactions on Networking, 2018
- Distributed Scheduling Algorithms for Optimizing Information Freshness in Wireless NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- End-to-End Driving Via Conditional Imitation LearningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Age of information in a network of preemptive serversPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Offloading in Internet of Vehicles: A Fog-Enabled Real-Time Traffic Management SystemIEEE Transactions on Industrial Informatics, 2018
- Optimal Status Update for Age of Information Minimization With an Energy Harvesting SourceIEEE Transactions on Green Communications and Networking, 2017
- Optimal Link Scheduling for Age Minimization in Wireless SystemsIEEE Transactions on Information Theory, 2017
- Adaptive Information Gathering via Imitation LearningPublished by Robotics: Science and Systems Foundation ,2017
- Minimizing age of information in vehicular networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011