Age of information(AoI) is a performance metric that captures the freshness of data from the destination's perspective. In the energy constrained real-time sensing Internet of Things scenario, this paper proposed a joint sampling and hybrid backscatter communication updating policy to improve the AoI performance of the system. The policy minimized the long-term average AoI of the system by allowing the source to select state sampling actions and transmission modes of updating processes. Specifically, this paper modeled the problem as an average cost Markov decision process(MDP). Then, when the system realized the dynamic environment information, the paper adopted optimal strategy by relative value iterative algorithm. When the system lacked the dynamic environment information, the paper applied Q-learning algorithm and exploration exploitation technique to learn the optimal strategy through trial-and-error interactions with the environment. Simulation results show that compared with the two reference policies, the proposed policy significantly improves the AoI performance of the system, and the AoI performance of the system increases with the decrease of the update package size or the increase of battery capacity.

[2022-03-15] Accepted Paper /doc/2021.12.0691/v1

[2022-08-05] Printed Article /doc/2021.12.0691/v2