Paper Info
Title
Energy-Efficient Activation/Inactivation Strategy for Long-term IoT Network Operation.
Abstract
A critical concern for IoT (Internet of Things) networks is how to operate a large number of sensors in an energy-efficient manner. Energy consumption caused by operating these sensors cannot be overlooked, especially for long-term IoT network operation. Inactivating some of the unneeded sensors during the operation is one of the feasible solutions. Recent studies have mainly focused on selecting the unneeded sensors in a duty-cycled manner based on the sensoru0027s geographic location or the sensoru0027s communication status. These studies involve obtaining some of the useru0027s private information, moreover, anomaly detection performance, a significant criterion regarding IoT network operation performance has not been considered. In this paper, we develop a novel activation/inactivation strategy for long-term IoT network operation. In this strategy, a machine learning model is adopted to adaptively select the unneeded sensors to be inactivated during network operation. Moreover, to maintain high sensor data processing performance during long-term operation, by only using the basic sensor data (e.g., temperature and humidity), our proposal selects the unneeded sensor candidates which are periodically updated. Numerical experiments conducted in two IoT network environments over seven-month operation verify the effectiveness of our proposal.
Year
DOI
Venue
2019
10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00161
SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Zhishu Shen100.34
Kenji Yokota201.69
Atsushi Tagami36925.29
Higashino, T.41915.19