TY  - CONF
EP  - 322
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075639997&doi=10.1109%2fSCORED.2019.8896244&partnerID=40&md5=a99203de9236aac6a78c74117f1efbeb
A1  - Alam, M.K.
A1  - Aziz, A.A.
A1  - Latif, S.A.
A1  - Awang, A.
SN  - 9781728126135
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2019///
KW  - Cluster analysis; K-means clustering; Reduction; Time series
KW  -  Data clustering; Environment monitoring; K-means; Redundancy reductions; WSNs
KW  -  Sensor nodes
ID  - scholars11233
SP  - 317
TI  - Data Clustering Technique for In-Network Data Reduction in Wireless Sensor Network
N2  - In wireless sensor networks (WSNs), plenty of sensor nodes are typically deployed in the field to provide a long-term monitoring facility. These sensor nodes are usually collect a huge amount of data over time. Transmitting the huge data from the sensor nodes to a sink introduces a big challenge to the network due to energy constraint of the sensor nodes. Therefore, many research efforts have been carried out so far to design efficient data clustering techniques for WSNs. The main purpose of these techniques is to reduce the amount of data over the network while retaining their fundamental properties. This paper aims to develop a Histogram-based Data Clustering (HDC) technique at the cluster-head (CH) for in-network data reduction. The HDC groups the homogeneous data into clusters and then performs in-network data reduction by selecting the central values (instead of all data points) of each cluster. Simulations on real-world sensor data show that the proposed HDC can effectively reduce a significant amount of redundant data and outperform existing techniques. © 2019 IEEE.
N1  - cited By 5; Conference of 17th IEEE Student Conference on Research and Development, SCOReD 2019 ; Conference Date: 15 October 2019 Through 17 October 2019; Conference Code:154444
AV  - none
ER  -