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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Main Authors: Tian Bai, Zhijie Li, Bo Fan
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Mobile Information Systems
Online Access:http://dx.doi.org/10.1155/2020/8834383
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spelling 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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