Goal-Oriented Scheduling in Sensor Networks With Application Timing Awareness

Abstract

Taking inspiration from linguistics, the communications theoretical community has recently shown a significant recent interest in pragmatic, or goal-oriented, communication. In this paper, we tackle the problem of pragmatic communication with multiple clients with different, and potentially conflicting, objectives. We capture the goal-oriented aspect through the metric of Value of Information (VoI), which considers the estimation of the remote process as well as the timing constraints. However, the most common definition of VoI is simply the Mean Square Error (MSE) of the whole system state, regardless of the relevance for a specific client. Our work aims to overcome this limitation by including different summary statistics, i.e., value functions of the state, for separate clients, and a diversified query process on the client side, expressed through the fact that different applications may request different functions of the process state at different times. A query-aware Deep Reinforcement Learning (DRL) solution based on statically defined VoI can outperform naive approaches by 15-20%.

Query-Age-Optimal Scheduling under Sampling and Transmission Constraints

“…Their results show that the sample-at-change policy, which simultaneously samples and transmits whenever there is a difference between the source state and its estimate at the monitor side, optimizes both the real-time error and AoII. Reference [32] studied a goal-oriented scheduling problem in a system where multiple sensors observe a process and send their data to the edge node upon commands. To this end, the authors introduced a query-based value of information metric, defined by MSE at the queries.…”