TY - JOUR
T1 - STAC
T2 - a spatio-temporal approximate method in data collection applications
AU - Wei, Xiaohui
AU - Yan, Sijie
AU - Wang, Xingwang
AU - Guizani, Mohsen
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2021
PY - 2021/6
Y1 - 2021/6
N2 - Wireless sensor networks (WSNs) and IoT are often deployed for long-term monitoring. However, the network lifetime of these applications is limited by non-rechargeable battery-powered. To vastly reduce energy consumption, this paper proposes a spatio-temporal approximate data collection (STAC) method to prolong the network lifetime. Under the tolerable accuracy, STAC utilizes spatial correlation among neighbors to select partial network for data collection with balanced energy distribution, and takes advantage of temporal redundancy to dynamically adjust the sampling interval by Q-learning based method. With the spatio-temporal approximate and correlation-variation verification mechanism, STAC prolongs the network lifetime with error-bounded data precision. Simulation results demonstrate STAC significantly improves network lifetime in various circumstances.
AB - Wireless sensor networks (WSNs) and IoT are often deployed for long-term monitoring. However, the network lifetime of these applications is limited by non-rechargeable battery-powered. To vastly reduce energy consumption, this paper proposes a spatio-temporal approximate data collection (STAC) method to prolong the network lifetime. Under the tolerable accuracy, STAC utilizes spatial correlation among neighbors to select partial network for data collection with balanced energy distribution, and takes advantage of temporal redundancy to dynamically adjust the sampling interval by Q-learning based method. With the spatio-temporal approximate and correlation-variation verification mechanism, STAC prolongs the network lifetime with error-bounded data precision. Simulation results demonstrate STAC significantly improves network lifetime in various circumstances.
KW - Data prediction
KW - Environmental monitoring
KW - Internet of Things
KW - Q-Learning
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85102856607&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102856607&partnerID=8YFLogxK
U2 - 10.1016/j.pmcj.2021.101371
DO - 10.1016/j.pmcj.2021.101371
M3 - Article
AN - SCOPUS:85102856607
SN - 1574-1192
VL - 73
JO - Pervasive and Mobile Computing
JF - Pervasive and Mobile Computing
M1 - 101371
ER -