  Data analysis in the Internet of Things (IoT) requires us to combine event
streams from a huge amount of sensors. This combination (join) of events is
usually based on the time stamps associated with the events. We address two
challenges in environments which acquire and join events in the IoT: First, due
to the growing number of sensors, we are facing the performance limits of
central joins with respect to throughput, latency, and network utilization.
Second, in the IoT, diverse sensor nodes are operated by different
organizations and use different time synchronization techniques. Thus, events
with the same timestamps are not necessarily recorded at the exact same time
and joined data tuples have an unknown time incoherence. This can cause
undetected failures, such as false correlations and wrong predictions. We
present SENSE, a system for scalable data acquisition from distributed sensors.
SENSE introduces time coherence measures as a fundamental data characteristic
in addition to common time synchronization techniques. The time coherence of a
data tuple is the time span in which all values contained in the tuple have
been read from sensors. We explore concepts and algorithms to quantify and
optimize time coherence and show that SENSE scales to thousands of sensors,
operates efficiently under latency and coherence constraints, and adapts to
changing network conditions.
