TY - GEN
T1 - Feature extraction in densely sensed environments
AU - Vahabi, Maryam
AU - Gupta, Vikram
AU - Albano, Michele
AU - Tovar, Eduardo
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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 exterma 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.
AB - 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 exterma 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.
UR - http://www.scopus.com/inward/record.url?scp=84904435685&partnerID=8YFLogxK
U2 - 10.1109/DCOSS.2014.29
DO - 10.1109/DCOSS.2014.29
M3 - Article in proceeding
AN - SCOPUS:84904435685
SN - 9781479946198
T3 - Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014
SP - 143
EP - 151
BT - Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014
PB - IEEE Computer Society Press
T2 - 9th IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2014
Y2 - 26 May 2014 through 28 May 2014
ER -