Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
