Keywords: mobile agent, decision tree, distributed classification, wireless sensor networks, energy consumption
Received January 30, 2007; revised April 12, 2007; accepted June 12, 2007.
Communicated by Pau-Choo Chung.
| Previous | [ 1] | [ 2] | [ 3] | [ 4] | [ 5] | [ 6] | [ 7] | [ 8] | [ 9] | [ 10] | [ 11] | [ 12] | [ 13] | [ 14] | [ 15] | [ 16] | [ 17] | [ 18] |
¡@
Shuang-Quan Wang, Xin Chen*, Ning-Jiang Chen* and Jie Yang
Institute of Image Processing and Pattern Recognition
Shanghai Jiaotong University
Shanghai, 200240 P.R.C.
*Philips Research East Asia
Shanghai, 200070 P.R.C.
Wireless sensor networks (WSNs) connect physical sensors that are distributed in
the environment. In many applications, the statistical pattern recognition methods, such
as decision tree (DT) algorithms, are used to recognize the patterns of the sensor readings.
To enable the decision tree classification (DTC) in WSNs, a new distributed decision
tree classification (DDTC) algorithm based on mobile agent is proposed in this paper.
We organize the conjunctive sets of linear classifiers in DT into groups of operations
on attributes. Each group of operations is allocated to a single sensor node. If a mobile
agent visits these sensor nodes serially, the recognition result can be acquired step by
step. Thus the sensor nodes do not need to transmit all the sensor data to a centralized
node where all the data processing is carried out in traditional DTC. In DDTC, if not all
the attributes are needed for operation, classification of one instance can finish halfway.
Two public data sets are used to evaluate the performance of DTC and DDTC on energy
consumption. The simulation results indicate that, compared with the centralized DTC,
the DDTC algorithm decreases the number of transmissions, balances the power consumption
and computation among sensor nodes, and prolongs the lifetime of the network.
Received January 30, 2007; revised April 12, 2007; accepted June 12, 2007.
Communicated by Pau-Choo Chung.