Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance
In cyber-physical systems, sensor transactions should be effectively scheduled to maintain the temporal validity of real-time data objects. Previous studies on sensor transaction scheduling mainly focus on uniprocessor systems. In this paper, we study the problem of data quality-based scheduling of...
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doaj-fb05182a22cb495488b21ea5fc51a8762021-07-02T13:08:34ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2020-01-01202010.1155/2020/88343838834383Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality MaintenanceTian Bai0Zhijie Li1Bo Fan2School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414000, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414000, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414000, ChinaIn cyber-physical systems, sensor transactions should be effectively scheduled to maintain the temporal validity of real-time data objects. Previous studies on sensor transaction scheduling mainly focus on uniprocessor systems. In this paper, we study the problem of data quality-based scheduling of sensor transactions on multiprocessor platforms. The data quality is defined to describe the validity degree of real-time data objects. Two methods, named the Partitioned Scheduling for Quality Maximization (P-QM) and the improved P-QM scheduling (IP-QM), are proposed. P-QM maximizes the data quality by judiciously determining the preallocated computation time of each sensor transaction and assigns the transactions to different processors. IP-QM improves the data quality obtained from P-QM by adaptively executing transaction instances on each processor based on the current status of the system. It is demonstrated through experiments that IP-QM can provide higher data quality than P-QM under different system workloads.http://dx.doi.org/10.1155/2020/8834383 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tian Bai Zhijie Li Bo Fan |
spellingShingle |
Tian Bai Zhijie Li Bo Fan Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance Mobile Information Systems |
author_facet |
Tian Bai Zhijie Li Bo Fan |
author_sort |
Tian Bai |
title |
Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance |
title_short |
Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance |
title_full |
Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance |
title_fullStr |
Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance |
title_full_unstemmed |
Multiprocessor Scheduling of Sensor Transactions for Real-Time Data Quality Maintenance |
title_sort |
multiprocessor scheduling of sensor transactions for real-time data quality maintenance |
publisher |
Hindawi Limited |
series |
Mobile Information Systems |
issn |
1574-017X 1875-905X |
publishDate |
2020-01-01 |
description |
In cyber-physical systems, sensor transactions should be effectively scheduled to maintain the temporal validity of real-time data objects. Previous studies on sensor transaction scheduling mainly focus on uniprocessor systems. In this paper, we study the problem of data quality-based scheduling of sensor transactions on multiprocessor platforms. The data quality is defined to describe the validity degree of real-time data objects. Two methods, named the Partitioned Scheduling for Quality Maximization (P-QM) and the improved P-QM scheduling (IP-QM), are proposed. P-QM maximizes the data quality by judiciously determining the preallocated computation time of each sensor transaction and assigns the transactions to different processors. IP-QM improves the data quality obtained from P-QM by adaptively executing transaction instances on each processor based on the current status of the system. It is demonstrated through experiments that IP-QM can provide higher data quality than P-QM under different system workloads. |
url |
http://dx.doi.org/10.1155/2020/8834383 |
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