Collaborative Optimization of the Age of Information under Partial Observability
This addresses network congestion issues for sensor and control data freshness in IoT or communication systems, though it is incremental as it builds on existing AoI optimization methods.
The paper tackles the problem of minimizing Age of Information (AoI) in a network with multiple sensors sharing capacity-limited, non-FIFO channels under partial observability, resulting in a decentralized policy that reduces expected AoI without explicit inter-agent communication.
The significance of the freshness of sensor and control data at the receiver side, often referred to as Age of Information (AoI), is fundamentally constrained by contention for limited network resources. Evidently, network congestion is detrimental for AoI, where this congestion is partly self-induced by the sensor transmission process in addition to the contention from other transmitting sensors. In this work, we devise a decentralized AoI-minimizing transmission policy for a number of sensor agents sharing capacity-limited, non-FIFO duplex channels that introduce random delays in communication with a common receiver. By implementing the same policy, however with no explicit inter-agent communication, the agents minimize the expected AoI in this partially observable system. We cater to the partial observability due to random channel delays by designing a bootstrap particle filter that independently maintains a belief over the AoI of each agent. We also leverage mean-field control approximations and reinforcement learning to derive scalable and optimal solutions for minimizing the expected AoI collaboratively.