A Relational Approach to Sensor Network Data Mining

2010 ◽  
pp. 163-181 ◽  
Author(s):  
Floriana Esposito ◽  
Teresa M. A. Basile ◽  
Nicola Di Mauro ◽  
Stefano Ferilli
Keyword(s):  
Data Mining ◽  
Sensor Network ◽  
Network Data ◽  
Relational Approach

Sensor Field Resource Management for Sensor Network Data Mining

2010 ◽  
pp. 280-304
Author(s):  
David J. Yates ◽  
Jennifer Xu
Keyword(s):  
Data Mining ◽  
Response Time ◽  
Data Quality ◽  
Real Time ◽  
Sensor Network ◽  
Resource Consumption ◽  
Data Accuracy ◽  
Network Data ◽  
Sensor Field ◽  
User Queries

This research is motivated by data mining for wireless sensor network applications. The authors consider applications where data is acquired in real-time, and thus data mining is performed on live streams of data rather than on stored databases. One challenge in supporting such applications is that sensor node power is a precious resource that needs to be managed as such. To conserve energy in the sensor field, the authors propose and evaluate several approaches to acquiring, and then caching data in a sensor field data server. The authors show that for true real-time applications, for which response time dictates data quality, policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This “win-win” is because when data acquisition response time is sufficiently important, the decrease in resource consumption and increase in data quality achieved by using approximate values outweighs the negative impact on data accuracy due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between resource consumption and data accuracy emerges. The authors then identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a sensor field query. Finally, the authors discuss the challenges facing sensor network data mining applications in terms of data collection, warehousing, and mining techniques.

Using data mules for sensor network data recovery

2016 ◽  
Vol 40 ◽  
pp. 26-36 ◽  
Author(s):  
Jon Crowcroft ◽  
Liron Levin ◽  
Michael Segal
Keyword(s):  
Sensor Network ◽  
Data Recovery ◽  
Network Data ◽  
Using Data

Learning from sensor network data

2009 ◽  
Author(s):  
Matthias Keller ◽  
Jan Beutel ◽  
Andreas Meier ◽  
Roman Lim ◽  
Lothar Thiele
Keyword(s):  
Sensor Network ◽  
Network Data
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