Finding Representative Sampling Subsets in Sensor Graphs using Time Series Similarities

TL;DR

This paper introduces a graph-based method to identify representative sensor subsets in IoT networks, enabling efficient sampling that conserves battery life while maintaining data accuracy.

Contribution

It formulates the subset selection as a graph problem, proposing new techniques and an auto-selection method to optimize sensor sampling strategies.

Findings

Significant battery life improvements achieved in experiments.

Effective subset selection within realistic error bounds.

Comparison of six time series similarity techniques.

Abstract

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.