The advent of emerging information and communication technologies, such as RFID, small size sensors and sensor networks, has made accessible a huge amount of information that requires sophisticated and efficient search algorithms to support queries on that data. In this paper we focus on the problem of aggregating data collected from these devices to efficiently support queries, inferences or statistics on them. In general, data aggregation techniques are necessary to efficiently collect information in a compact and cost-effective way. Some current solutions try to meet the above criteria, by exploiting different data aggregation techniques, for instance BitVector or Q-Digest. In this manuscript, we exploit the mathematical wavelet structure to define a sophisticated data aggregation technique for information collected from different nodes. The aggregated data is then exploited for solving multi-dimensional range queries. Experimental results based on simulations of a real dataset show the effectiveness of our approach with respect to other aggregation strategies.

A data aggregation strategy based on wavelet for the internet of things

Guidi B.;Ricci L.
2018-01-01

Abstract

The advent of emerging information and communication technologies, such as RFID, small size sensors and sensor networks, has made accessible a huge amount of information that requires sophisticated and efficient search algorithms to support queries on that data. In this paper we focus on the problem of aggregating data collected from these devices to efficiently support queries, inferences or statistics on them. In general, data aggregation techniques are necessary to efficiently collect information in a compact and cost-effective way. Some current solutions try to meet the above criteria, by exploiting different data aggregation techniques, for instance BitVector or Q-Digest. In this manuscript, we exploit the mathematical wavelet structure to define a sophisticated data aggregation technique for information collected from different nodes. The aggregated data is then exploited for solving multi-dimensional range queries. Experimental results based on simulations of a real dataset show the effectiveness of our approach with respect to other aggregation strategies.
978-1-5386-2626-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1029474
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