Resources such as bandwidth and energy are limited in many wireless communications use cases, especially when large numbers of sensors and fusion centers need to exchange information frequently. One opportunity to overcome resource constraints is the use of event-based transmissions and estimation to transmit only information that contributes significantly to the reconstruction of the system's state. The design of efficient triggering policies and estimators is crucial for successful event-based transmissions. While previously deterministic and stochastic event triggering policies have been treated separately, this paper unifies the two approaches and gives insights into the design of reliable trigger-matching estimators. Two different estimators are presented, and different pairs of triggers and estimators are evaluated through simulation studies.

Many automation tasks require to fuse information that is acquired by distributed sensors and passed through a wireless network across multiple nodes. The growing number of connected sensors and agents increases the burden on the communications network and the energy consumption. Further challenges in information fusion arise from correlated data shared between nodes. To mitigate the negative effects, an efficient multi-sensor fusion approach is presented in this paper. A system design that uses stochastic event-based instead of periodic transmissions is proposed based on two different algorithms, the augmented state approach and fast covariance intersection. Furthermore, two different network topologies are investigated and a methodology to handle correlations among both finite impulse response and recursive estimates is developed. Together, the results represent a wide range of network topologies and possible correlation structures and give insights into the estimation performance and network utilization.