PROJECT TITLE :
Novel $varepsilon$ -Approximation to Data Streams in Sensor Networks
We are gradually moving into a realm where sensors, processors, memory and wireless transceivers would be seamlessly integrated along within the physical world and form a wireless sensor network. Such networks pose new challenges in data processing and transmission because of the characteristic of restricted communication bandwidth and alternative resource constraints of sensor networks. To cut back the cost of storage, transmission and processing of your time series knowledge generated by sensor nodes, the need for additional compact representations of your time series data is compelling. Although a massive range of knowledge compression algorithms are proposed to cut back knowledge volume, their offline characteristic or super-linear time complexity prevents them from being applied directly on time series information generated by sensor nodes. Motivated by these observations, we propose an optimal on-line algorithm GDPLA for constructing a disconnected piecewise linear approximation of a time series which guarantees that the vertical distance between each real information point and the corresponding fit line is but or equal to . GDPLA not only generates the minimum variety of segments to approximate a time series with precision guarantee, but conjointly solely requires linear time bounded by a relentless coefficient , where unit denotes the time complexity of comparing the slopes of two lines. T- e low cost characteristic of our method makes it a proper alternative for resource-constrained WSNs. Extensive experiments on two real data sets have been conducted to demonstrate the superior compression performance of our methodology.
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