TY  - CONF
SP  - 158 
A1  - Ng, Wanqing
A1  - Ooi, Boonyaik Yaik
A1  - Kh'ng, Xin Yi
A1  - Liew, Soung Yue
A1  - Symeonaki, Eleni G.
ID  - scholars20434
AV  - none
SN  - 9781665491648
KW  - Behavioral research; Cost reduction; Data compression; Energy utilization; Internet of things; Taxicabs; Communication cost; Context-Aware; Energy-consumption; High velocity; IoT; IoT-gateway; Lifespans; Redundant data; Taxi algorithm; Velocities data; Data reduction
N2  - While most of the existing works are focusing on the data reduction of IoT sensor to lower the energy consumption, extend the battery's lifespan, reduce redundant data sensed and transmitted, and eventually lower the communication cost, the data reduction performed in the IoT-gateway seems to be omitted. Besides that, most of the existing works do not consider the behaviour of the user when accessing the data. Therefore, our work aims to focus on data reduction at the IoT-gateway, which is using the user's data access behaviour to reduce the cost of communication between the IoT-gateway and the IoT cloud. In this paper, we proposed a context-aware data reduction framework for high velocity data, such as vibration data. The pattern of user accessing IoT data will be analysed first, and then the IoT-gateway will delay the data uploading process as long as possible before the user accessing the uploaded data. The purpose of delaying the data uploading process is the more data being compressed together, the smaller file size can be obtained. As a result, with the delay of data uploading process at the IoT-gateway, the communication cost can be saved up to 56. © 2022 IEEE.
TI  - A Context-Aware Data Reduction Framework for High Velocity Data
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141795345&doi=10.1109%2FAiDAS56890.2022.9918777&partnerID=40&md5=8044cc570eccf609646594692f32d070
Y1  - 2022///
EP  -  163
N1  - Cited by: 2
PB  - Institute of Electrical and Electronics Engineers Inc.
ER  -