An autonomic in network query processing for urban sensor networks

1. 2020 – 2021
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An Autonomic in-Network Query Processing for Urban Sensor Networks
Abstract
The sensing ofurban environments usuallytakes into accountthe deploymentofa large number ofdevices to measure
their environmental attributes,such as temperature,pressure,humidity,luminosityand pollution.In such applications,
nearby sensors usually produce similar readings due to their spatial and temporal correlation. In the era of big data,
managementofcollected data requires autonomous and scalable Wireless Sensor Network (WSN) structures.In this
paper, we propose an in-network data storage model,called AQPM, that provides efficient processing ofboth spatial
and value-based queries.AQPM is autonomous and scalable.Thatis, it does not rely on any central entity for neither
managing data storage on sensor devices nor for processing queries.Scalabilityis achieved by grouping sensors with
similar readings into clusters, while efficient query processing relies on the concept of repositories. Repositories are
sensors thatstore readings ofa setof clusters,and are the only ones thathave to be contacted for answering queries.
AQPM has been implemented on NS2 simulator and experimental results show that it is more effective than existing
approaches.