A new deterministic data aggregation method for wireless sensor networks

The processing capabilities of wireless sensor nodes enable to aggregate redundant data to limit total data flow over the network. The main property of a good aggregation algorithm is to extract the most representative data by using minimum resources. From this point of view, sampling is a promising aggregation method, that acts as surrogate for the whole data, and once extracted can be used to answer multiple kinds of queries (such as AVG, MEDIAN, SUM, COUNT, etc.), at no extra cost to the sensor network. Additionally, sampling also preserves correlations between attributes of multi-dimensional data, which is quite valuable for further data mining. In this paper, we propose a novel, distributed, weighted sampling algorithm to sample sensor network data and compare to an existing random sampling algorithm, which is the only algorithm to work in this kind of setting. We perform popular queries to evaluate our algorithm on a real world data set, which covers climate data in the US for the past 100 years. During testing, we focus on issues such as sample quality, network longevity, energy and communication costs.