Paper Info
Title
Dynamic control of data measurement intervals in a networked sensing system using neurocomputing
Abstract
A new algorithm for dynamic controlling of data measurement intervals in a networked sensing system (NSS) is presented in this paper. The method is developed on a wireless sensor network (WSN) for food quality supervision during the transportation process using containers. The artificial neural network (ANN) is used for data approximation due to its learning capability and high flexibility. At each instance, the measurement interval is changed dynamically depending on the stability of the environmental parameters in the container. The wireless sensor network is able to detect the possible unstable situations automatically with low energy consumption. Firstly, the performance of the dynamic control mechanism is tested in a simulation environment. Later, the developed algorithm is implemented to adjust the measurement intervals in a real transportation system. The new developed technique could be applied to decrease the power consumption in various applications of the networked sensing systems.
Year
DOI
Venue
2010
10.1109/INSS.2010.5573671
INSS
Keywords
Field
DocType
containers,data handling,energy consumption,food safety,neural nets,transportation,wireless sensor networks,artificial neural network,data approximation,data measurement interval,dynamic control,food quality supervision,networked sensing system,neurocomputing,power consumption,transportation process,wireless sensor network,dynamic measurement interval,intelligent transportation,food quality
Sensing system,Computer science,Real-time computing,Data approximation,Intelligent transportation system,Artificial neural network,Group method of data handling,Wireless sensor network,Energy consumption,Power consumption
Conference
ISBN
Citations 
PageRank 
978-1-4244-7910-8
2
0.44
References 
Authors
5
4
Name
Order
Citations
PageRank
Xinwei Wang1202.65
Jabbari, A.220.44
Laur, R.320.44
Walter Lang43411.56