  Distributed state estimation strongly depends on collaborative signal
processing, which often requires excessive communication and computation to be
executed on resource-constrained sensor nodes. To address this problem, we
propose an event-triggered diffusion Kalman filter, which collects measurements
and exchanges messages between nodes based on a local signal indicating the
estimation error. On this basis, we develop an energy-aware state estimation
algorithm that regulates the resource consumption in wireless networks and
ensures the effectiveness of every consumed resource. The proposed algorithm
does not require the nodes to share its local covariance matrices, and thereby
allows considerably reducing the number of transmission messages. To confirm
its efficiency, we apply the proposed algorithm to the distributed simultaneous
localization and time synchronization problem and evaluate it on a physical
testbed of a mobile quadrotor node and stationary custom ultra-wideband
wireless devices. The obtained experimental results indicate that the proposed
algorithm allows saving 86% of the communication overhead associated with the
original diffusion Kalman filter while causing deterioration of performance by
16% only. We make the Matlab code and the real testing data available online.
