Risk-Aware Optimization of Age of Information in the Internet of Things
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For time-sensitive Internet of Things (IoT) applications, a risk-neutral approach for age of information (AoI) optimization which focuses only on minimizing the expected value of the AoI based cost function, cannot capture rare yet critical events with potentially very large AoI. Thus, in this paper, in order to quantify such rare events, an effective coherent risk measure, called the conditional value-at-risk (CVaR), is studied for the purpose of minimizing the AoI of real-time IoT status updates. Particularly, a real-time IoT monitoring system is considered in which an IoT device monitors a physical process and sends the status updates to a remote receiver with an updating cost. The optimal status updating process is designed to jointly minimize the AoI at the receiver, the CVaR of the AoI at the receiver, and the energy cost. This stochastic optimization problem is formulated as an infinite horizon discounted risk-aware Markov decision process (MDP), which is computationally intractable due to the time inconsistency of the CVaR. By exploiting the special properties of coherent risk measures, the risk-aware MDP is reduced to a standard MDP with an augmented state space, for which, a dynamic programming based solution is proposed to derive the optimal stationary policy. In particular, the optimal history-dependent policy of the risk-aware MDP is shown to depend on the history only through the augmented system states and can be readily constructed using the optimal stationary policy of the augmented MDP. The proposed solution is computationally tractable and minimizes the AoI in real-time IoT monitoring systems in a risk-aware manner.
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