Enhancing Information Freshness: An AoI Optimized Markov Decision Process
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Department of Computer Science, University of Oxford（牛津大学计算机系）；Department of Data Science, New Jersey Institute of Technology（新泽理工学院数据科学系）
Ocean exploration utilizing autonomous underwater vehicles (AUVs) via reinforcement learning (RL) has emerged as a significant research focus. However, underwater tasks have mostly failed due to the observation delay caused by information limitation in the information updating networks. In this study, we present an AoI optimized Markov decision process (AoI-MDP) to improve the performance of underwater tasks. Specifically, AoI-MDP models observation delay as timing delay through statistical delay formulation, and includes this delay as a new component in the state space. Additionally, we introduce wait time in the action space, and integrate AoI with reward functions to achieve joint optimization of information freshness and decision-making for AUVs leveraging RL for training. Finally, we apply this approach to the multi-AUV data collection task scenario as an example. Simulation results highlight the feasibility of AoI-MDP, which effectively minimizes AoI while showcasing superior performance in the task. To accelerate relevant research in this field, we have made the simulation codes available as open-source.
A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning
Department of Electrical and Computer Engineering, Illinois Institute of Technology（伊利诺伊理工学院电气与计算机工程系）；School of Computer Science and Engineering, California State University（加州州立大学计算机科学与工程学院）
AoI-MDP: An AoI Optimized Markov Decision Process (Student Abstract)
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院，清华大学）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；Department of Data Science, New Jersey Institute of Technology（新泽西理工学院数据科学系）
