  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.
