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We study a real-time tracking problem in an energy harvesting status updatesystem with a Markov source and an imperfect channel, considering both samplingand transmission costs. The problem primary challenge stems from thenon-observability of the source due to the sampling cost. By using the age ofincorrect information (AoII) as a semantic-aware performance metric, our maingoal is to find an optimal policy that minimizes the time average AoII subjectto an energy-causality constraint. To this end, a stochastic optimizationproblem is formulated and solved by modeling it as a partially observableMarkov decision process (POMDP). More specifically, to solve the main problem,we use the notion of a belief state and cast the problem as a belief MDPproblem. Then, for the perfect channel setup, we effectively truncate thecorresponding belief space and solve the MDP problem using the relative valueiteration method. For the general setup, a deep reinforcement learning policyis proposed. The simulation results show the efficacy of the derived policiesin comparison to an AoI-optimal policy and an opportunistic baseline policy.

Optimal Semantic-aware Sampling and Transmission in Energy Harvesting Systems Through the AoII