  Although resource-limited networked autonomous systems must be able to
efficiently and effectively accomplish tasks, better conservation of resources
often results in worse task performance. We specifically address the problem of
finding strategies for managing measurement communication costs between agents.
A well understood technique for trading off communication costs with estimation
accuracy is event triggering (ET), where measurements are only communicated
when useful, e.g., when Kalman filter innovations exceed some threshold. In the
absence of measurements, agents can use implicit information to achieve results
almost as well as when explicit data is always communicated. However, there are
no methods for setting this threshold with formal guarantees on task
performance. We fill this gap by developing a novel belief space discretization
technique to abstract a continuous space dynamics model for ET estimation to a
discrete Markov decision process, which scalably accommodates
threshold-sensitive ET estimator error covariances. We then apply an existing
probabilistic trade-off analysis tool to find the set of all optimal trade-offs
between resource consumption and task performance. From this set, an ET
threshold selection strategy is extracted. Simulated results show our approach
identifies non-trivial trade-offs between performance and energy savings, with
only modest computational effort.
