The freshness of real-time status processing of time-sensitive information is crucial for many applications, including flight control, image processing, and autonomous vehicles. In this paper, unprocessed information is sent from sensors to a base station over a shared, unreliable wireless network. The base station has a set of dedicated non-preemptive processors with constant processing times to process information from each sensor. The age of processed information is the time elapsed since the generation of the packet that the processor most recently processed. Our objective is to minimize the expected weighted sum of this age over an infinite time horizon. Here, the challenge is the coupling between a scheduling problem under unreliable communications and the processing times. We first break the coupling by tracking the age of information during processing and derive a lower performance bound of the objective. We then design a stationary randomized policy and a Max-Weight policy for two queueing disciplines: no queues and single-packet queues to achieve our objective. We prove that these policies achieve performance within a factor of two from the optimal. In addition, we prove queues are useful to the stationary randomized policies in highly unreliable or large network settings. Our analytical results are further validated by numerical experiments.

The freshness of real-time status processing of time-sensitive information is crucial for several applications, including patient monitoring and autonomous driving. This freshness is considered in this paper for the system where unprocessed information is sent from sensors to a base station over a shared wireless network. The base station has a dedicated non-preemptive processor with a constant processing time to process information from each sensor. The age of processed information is the time elapsed since the generation of the packet that was most recently processed by a processor. Our objective is to minimize the average age of processed information over an infinite time-horizon. We first show that a drop-free policy simplifies the system without sacrificing optimality. From this simplification, we propose three transmission-scheduling policies with 2-optimal guarantees for different requirements. A distributed Power-2 policy can be implemented without a central scheduler. With a central scheduler, both Back-Off and Max-Weight policies are near optimal with different advantages. The Back-Off policy guarantees a bound on the maximum age of processed information, while the Max-Weight policy achieves the lowest average age in simulation without the guarantee of bound. Simulation results confirm our theoretical findings.