Maximizing quality of information from multiple sensor devices: The exploration vs exploitation tradeoff

This paper investigates Quality of Information (QoI) aware adaptive sampling in a system where two sensor devices report information to an end user. The system carries out a sequence of tasks, where each task relates to a random event that must be observed. The accumulated information obtained from the sensor devices is reported once per task to a higher layer application at the end user. The utility of each report depends on the timeliness of the report and also on the quality of the observations. Quality can be improved by accumulating more observations for the same task, at the expense of delay. We assume new tasks arrive randomly, and the qualities of each new observation are also random. The goal is to maximize time average quality of information subject to cost constraints. We solve the problem by leveraging dynamic programming and Lyapunov optimization. Our algorithms involve solving a 2-dimensional optimal stopping problem, and result in a 2-dimensional threshold rule. When task arrivals are i.i.d., the optimal solution to the stopping problem can be closely approximated with a small number of simplified value iterations. When task arrivals are periodic, we derive a structured form approximately optimal stopping policy. We also introduce hybrid policies applied over the proposed adaptive sampling algorithms to further improve the performance. Numerical results demonstrate that our policies perform near optimal. Overall, this work provides new insights into network operation based on QoI attributes.