  As the demand on artificial intelligence (AI)-based applications increases,
the freshness of sensed data becomes crucial in the wireless sensor networks.
Since those applications require a large amount of computation for processing
the sensed data, it is essential to offload the computation load to the edge
computing (EC) server. In this paper, we propose the sensing and computing
decision (SCD) algorithms for data freshness in the EC-enabled wireless sensor
networks. We define the {\eta}-coverage probability to show the probability of
maintaining fresh data for more than {\eta} ratio of the network, where the
spatial-temporal correlation of information is considered. We then propose the
probability-based SCD for the single pre-charged sensor case with providing the
optimal point after deriving the {\eta}-coverage probability. We also propose
the reinforcement learning (RL)- based SCD by training the SCD policy of
sensors for both the single pre-charged and multiple energy harvesting (EH)
sensor cases, to make a real-time decision based on its observation. Our
simulation results verify the performance of the proposed algorithms under
various environment settings, and show that the RL-based SCD algorithm achieves
higher performance compared to baseline algorithms for both the single
pre-charged sensor and multiple EH sensor cases.
