Modeling AoII in Push- and Pull-Based Sampling of Continuous Time Markov Chains

Ismail Cosandal, Nail Akar, Sennur Ulukus
Computer Science, Information Theory, Information Theory (cs.IT), Networking and Internet Architecture (cs.NI), Signal Processing (eess.SP), Systems and Control (eess.SY)
2024-01-08 00:00:00
Age of incorrect information (AoII) has recently been proposed as an alternative to existing information freshness metrics for real-time sampling and estimation problems involving information sources that are tracked by remote monitors. Different from existing metrics, AoII penalizes the incorrect information by increasing linearly with time as long as the source and the monitor are de-synchronized, and is reset when they are synchronized back. While AoII has generally been investigated for discrete time information sources, we develop a novel analytical model in this paper for push- and pull-based sampling and transmission of a continuous time Markov chain (CTMC) process. In the pull-based model, the sensor starts transmitting information on the observed CTMC only when a pull request from the monitor is received. On the other hand, in the push-based scenario, the sensor, being aware of the AoII process, samples and transmits when the AoII process exceeds a random threshold. The proposed analytical model for both scenarios is based on the construction of a discrete time MC (DTMC) making state transitions at the embedded epochs of synchronization points, using the theory of absorbing CTMCs, and in particular phase-type distributions. For a given sampling policy, analytical models to obtain the mean AoII and the average sampling rate are developed. Numerical results are presented to validate the analytical model as well as to provide insight on optimal sampling policies under sampling rate constraints.
PDF: Modeling AoII in Push- and Pull-Based Sampling of Continuous Time Markov Chains.pdf
Empowered by ChatGPT