  Distributed sensor networks have gained interest thanks to the developments
in processing power and communications. Event-triggering mechanisms can be
useful in reducing communication between the nodes of the network, while still
ensuring an adequate behaviour of the system. However, very little attention
has been given to continuous-time systems in this context. In this work, we
propose a strategy for distributed state estimation in sensor networks, based
on average dynamic consensus of the continuous measurements. While
communication between nodes is discrete and heavily reduced due to the
event-triggering mechanism, our method ensures that the nodes are still able to
produce a continuous estimate of the global average measurement and the state
of the plant, within some tuneable error bounds.
