Extension

Background

Information freshness has become an increasingly important performance metric in this era of the Internet of Things. The concept of the age of information (AoI) has been proposed to measure information freshness from a destination’s perspective. This metric is defined as the time elapsed from the moment when the freshest received status update (by a destination) arrived at its source.

In multi-user uplink systems, the scheduling problems of minimizing the network-wide AoI are challenging, especially in the presence of stochastic status update arrivals. This is mainly because destinations may not know if new status updates have arrived at sources. Most previous work assumed that sources utilized extra feedback to report their status update arrivals. Such feedback may lead to considerable overhead, thereby complicating the implementation of scheduling policies.

To address this limitation, we assume that there is no extra feedback, which results in a scheduling problem under partial system information. A partially observable Markov decision process can be further formulated to characterize the behavior of such a system. Specifically, a destination fully observes the destination AoI of sources and partially observes their source age.

Related Work

2020

• Age-of-Information-based Scheduling in Multiuser Uplinks with Stochastic Arrivals: A POMDP Approach
  A. Gong, T. Zhang, H. Chen, and Y. Zhang, in Proc. IEEE GLOBECOM, 2020
  Remark: fully observable destination AoI / partially observable source age

2021

• AoI Optimal Dynamic Power Control for IoT networks: A DRDPG Approach
  K. Chi, M. Wu, Y. Chen, and C. Xu, in Proc. IEEE ICEICT, 2021
  Remark: fully observable destination AoI / partially observable source age

• Age-based Scheduling in Internet of Things with Bursty Traffic over Time Varying Channels
  B. Yan, X. Wang, C. Xu, and X. Sun, in Proc. ComComAp, 2021
  Remark: fully observable destination AoI / partially observable source age and channel states

2022

• Partially Observable Minimum-Age Scheduling: The Greedy Policy
  Y. Shao, Q. Cao, S. C. Liew, and H. Chen, IEEE Trans. Commun., 2022
  Remark: partially observable source age

• Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge
  M. Hatami, M. Leinonen, and M. Codreanu, in Proc. IEEE SPAWC, 2022
  Remark: fully observable requests, destination battery level, and source age / partially observable source battery level

• Timely Monitoring of Dynamic Sources With Observations From Multiple Wireless Sensors
  A. E. Kalør and P. Popovski, IEEE/ACM Trans. Netw., 2022
  Remark: partially observable source states and source age

• On the Age-Optimality of Relax-then-Truncate Approach under Partial Battery Knowledge in Energy Harvesting IoT Networks
  M. Hatami, M. Leinonen, and M. Codreanu, arXiv preprint arXiv:2212.05979, 2022
  Remark: fully observable requests, destination battery level, and source age / partially observable source battery level

2023

• Status Updating under Partial Battery Knowledge in Energy Harvesting IoT Networks
  M. Hatami, M. Leinonen, and M. Codreanu, arXiv preprint arXiv:2303.18104, 2023
  Remark: fully observable requests, destination battery level, and source age / partially observable source battery level

• Optimizing Age of Information in Wireless Uplink Networks With Partial Observations
  J. Liu, R. Zhang, A. Gong, and H. Chen, IEEE Trans. Commun., 2023
  Remark: fully observable destination AoI / partially observable source age

• AoI Minimization for Multi-user Networks with Delayed Feedback
  Y. Lu, X. Xu, and X. Huang, in Proc. IEEE/CIC ICCC, 2023
  Remark: fully observable source age / partially observable destination AoI

• Optimizing Age of Information in Uplink Multiuser MIMO Networks with Partial Observations
  J. Liu, Q. Wang, and H. Chen, in Proc. WiOpt, 2023
  Remark: fully observable destination AoI / partially observable source age