Timely Processing and Offloading of Short Term Tasks in the Internet of Things

Numerous Internet of things (IoT) applications demand accurate and timely information to enable effective actuation. Nonetheless, in computation-intensive status update systems or data gathering systems, the capabilities of IoT devices may fall short in computing/acquiring data with high accuracy, thereby requiring multiple trials before success. Offloading data computation or acquisition tasks to robust units mitigates this inaccuracy. However, these units can be positioned far from the user, and latency becomes an issue for offloading due to some factors, such as propagation delays and resource sharing with other users. This paper investigates the optimization of information freshness for short term tasks, introducing a cost function representing the staleness of the recently obtained data that is accurate enough for actuation. The problem is cast as a Markov decision process and solved through finite-horizon dynamic programming. We find interesting trends of the resulting optimal policy, which can lead to relevant applications for many IoT scenarios.