Interplay between VAoI, Packet Error Rate, and Delay for Energy-Efficient Remote Monitoring
For wireless sensor networks requiring energy-efficient status updates, this work provides a practical optimization framework that balances freshness and energy, though it is an incremental extension of existing age-of-information methods.
This paper optimizes energy consumption for event-triggered remote monitoring with finite-blocklength links, using Version Age of Information (VAoI) as a freshness metric. The proposed power allocation reduces long-term energy by up to 50% compared to per-transmission optimization under a VAoI constraint.
This letter studies energy optimization of short-packet transmission for event-triggered remote monitoring over finite-blocklength wireless links. A wireless sensor node generates updates only when the source state changes, and freshness is measured by the Version Age of Information (VAoI). We model the VAoI evolution as a Markov chain and show its coupling with the packet error rate, characterized by decoding error probability, and average delay. Then, we formulate a transmit-power allocation problem that minimizes the long-term average energy consumption under a VAoI constraint and solve it using a low-complexity search method. Numerical results show that the update arrival probability and blocklength strongly affect the energy--VAoI tradeoff, and that optimizing long-term energy consumption can substantially reduce energy compared with minimizing the energy per transmission.