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The freshness of status information is of great importance for time-criticalInternet of Things (IoT) applications. A metric measuring status freshness isthe age-of-information (AoI), which captures the time elapsed from the statusbeing generated at the source node (e.g., a sensor) to the latest statusupdate.However, in intelligent IoT applications such as video surveillance, thestatus information is revealed after some computation intensive andtime-consuming data processing operations, which would affect the statusfreshness. In this paper, we propose a novel metric, age-of-processing (AoP),to quantify such status freshness, which captures the time elapsed of thenewest received processed status data since it is generated. Compared with AoI,AoP further takes the data processing time into account. Since an IoT devicehas limited computation and energy resource, the device can choose to offloadthe data processing to the nearby edge server under constrained status samplingfrequency.We aim to minimize the average AoP in a long-term process by jointlyoptimizing the status sampling frequency and processing offloading policy. Weformulate this online problem as an infinite-horizon constrained Markovdecision process (CMDP) with average reward criterion. We then transform theCMDP problem into an unconstrained Markov decision process (MDP) by leveraginga Lagrangian method, and propose a Lagrangian transformation framework for theoriginal CMDP problem. Furthermore, we integrate the framework withperturbation based refinement for achieving the optimal policy of the CMDPproblem. Extensive numerical evaluations show that the proposed algorithmoutperforms the benchmarks, with an average AoP reduction up to 30%.

Age of Processing: Age-driven Status Sampling and Processing Offloading for Edge Computing-enabled Real-time IoT Applications