TY - GEN
T1 - Adaptive data stream mining for wireless sensor networks
AU - Cuzzocrea, Alfredo
AU - Gaber, Mohamed Medhat
AU - Shiddiqi, Ary Mazharuddin
PY - 2014
Y1 - 2014
N2 - Data stream mining in wireless sensor networks has many important applications. Realizing these applications is faced by resource constraints of the sensor nodes that form the network. Adaptation to availability of resources is crucial to the success of these applications. In this paper, we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. Experimental results evidenced the applicability of our technique to operate in such an environment of scarce resources. Copyright l'2014 ACM.
AB - Data stream mining in wireless sensor networks has many important applications. Realizing these applications is faced by resource constraints of the sensor nodes that form the network. Adaptation to availability of resources is crucial to the success of these applications. In this paper, we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. Experimental results evidenced the applicability of our technique to operate in such an environment of scarce resources. Copyright l'2014 ACM.
KW - Data mining
KW - Data streams
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=84906818973&partnerID=8YFLogxK
U2 - 10.1145/2628194.2628213
DO - 10.1145/2628194.2628213
M3 - Conference contribution
AN - SCOPUS:84906818973
SN - 9781450326278
T3 - ACM International Conference Proceeding Series
SP - 284
EP - 287
BT - Proceedings of the 18th International Database Engineering and Applications Symposium, IDEAS 2014
PB - Association for Computing Machinery
T2 - 18th International Database Engineering and Applications Symposium, IDEAS 2014
Y2 - 7 July 2014 through 9 July 2014
ER -