Abstract
In a wireless sensor network, sensors collect data about natural phenomena and transmit them to a server in real-time. Many studies have been conducted focusing on the processing of continuous queries in an approximate form. However, this approach is difficult to apply to environmental applications which require the correct data to be stored. In this paper, we propose a weather monitoring system for handling and storing the sensor data stream in real-time in order to support continuous spatial and/or temporal queries. In our system, we exploit two time-based insertion methods to store the sensor data stream and reduce the number of managed tuples, without losing any of the raw data which are useful for queries, by using the sensors' temporal attributes. In addition, we offer a method for reducing the cost of the join operations used in processing spatiotemporal queries by filtering out a list of irrelevant sensors from query range before making a join operation. In the results of the performance evaluation, the number of tuples obtained from the data stream is reduced by about 30% in comparison to a naïve approach, thereby decreasing the query execution time.
Highlights
Wireless Sensor Networks (WSNs) consist of a large number of sensors located in the physical world that collect and communicate data continuously [1,2,3]
We introduce two insertion methods called Time-Segment Insertion (TSI) and Time-Point Insertion (TPI)
The TSI and TPI methods discard duplicate values and store the data just once if they do not change over time
Summary
Wireless Sensor Networks (WSNs) consist of a large number of sensors located in the physical world that collect and communicate data continuously [1,2,3]. Modern hardware technologies make it possible to gather data by using cheap and small sensor devices (e.g., smart dust and RFIDs). These sensors collect data about natural phenomena such as the temperature, light, sound, and pressure and transmit them to a server in real-time. They are widely utilized in geophysical monitoring, movement tracking, and medical monitoring [4,5,6]. There are still a lot of issues arising from the application level that have not been fully addressed by those previous works yet, such as how to efficiently and sensibly store and manipulate a huge amount of current streaming data as well as historic data
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