Feature Extraction in Densely Sensed Environments
Ref: CISTER-TR-140513 Publication Date: 27, May, 2014
Feature Extraction in Densely Sensed EnvironmentsRef: CISTER-TR-140513 Publication Date: 27, May, 2014
With the reduction in size and cost of sensor nodes, dense sensor networks are becoming more popular in a wide range of applications. Many such applications with dense deployments are geared towards finding various patterns or features such as peaks, boundaries and shapes in the spread of sensed physical quantities over an area. However, collecting all the data from individual sensor nodes can be impractical both in terms of timing requirements and the overall resource consumption. Hence, it is imperative to devise distributed information processing techniques that can help in identifying such features with a high accuracy and within certain time constraints.
In this paper, we exploit the prioritized channel-access mechanism of dominance-based Medium Access Control (MAC) protocols to efficiently obtain extrema of the sensed quantities. We show how by the use of simple transforms that sensor nodes employ on local data it is also possible to efficiently extract certain features such as local extrema and boundaries of events. Using these transformations, we show through extensive evaluations that our proposed technique is fast and efficient at retrieving only sensor data point with the most constructive information, independent of the number of sensor nodes in the network.
IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS 2014), IEEE.
Marina Del Rey, U.S.A..