Finding Representative Sampling Subsets in Sensor Graphs using Time Series Similarities

With the increasing use of IoT-enabled sensors, it is important
to have effective methods for querying the sensors. For example,
in a dense network of battery-driven temperature sensors, it is
often possible to query (sample) just a subset of the sensors at
any given time, since the values of the non-sampled sensors can
be estimated from the sampled values. If we can divide the set
of sensors into disjoint so-called representative sampling subsets
that each represent the other sensors sufficiently well, we can
alternate the sampling between the sampling subsets and thus,
increase battery life significantly. In this paper, we formulate the
problem of finding representative sampling subsets as a graph
problem on a so-called sensor graph with the sensors as nodes.
Our proposed solution, SubGraphSample, consists of two phases.
In Phase-I, we create edges in the sensor graph based on the
similarities between the time series of sensor values, analyzing
six different techniques based on proven time series similarity
metrics. In Phase-II, we propose two new techniques and extend
four existing ones to find the maximal number of representative
sampling subsets. Finally, we propose AutoSubGraphSample which
auto-selects the best technique for Phase-I and Phase-II for a
given dataset. Our extensive experimental evaluation shows that
our approach can yield significant battery life improvements
within realistic error bounds.