Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things
Abstract The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy l...
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doaj-8ec70b69275c4ff18f5f03d1a796c9f12020-11-25T01:25:00ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992018-01-012018112210.1186/s13638-017-1015-zLearning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of ThingsMinrui Wu0Yanhui Wu1Xiao Liu2Ming Ma3Anfeng Liu4Ming Zhao5School of Information Science and Engineering, Central South UniversityHunan University of CommerceSchool of Information Science and Engineering, Central South UniversityDepartment of Computer Science, Stony Brook UniversitySchool of Information Science and Engineering, Central South UniversitySchool of Software, Central South UniversityAbstract The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5.13–11.64% and increase the energy efficiency by 14.29–17.53% under the same lifetime with a more balanced energy utilization.http://link.springer.com/article/10.1186/s13638-017-1015-zIndustrial Internet of ThingsLearning-based synchronous (LS) approachCommunication delayDuty cycleNetwork lifetime |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Minrui Wu Yanhui Wu Xiao Liu Ming Ma Anfeng Liu Ming Zhao |
spellingShingle |
Minrui Wu Yanhui Wu Xiao Liu Ming Ma Anfeng Liu Ming Zhao Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things EURASIP Journal on Wireless Communications and Networking Industrial Internet of Things Learning-based synchronous (LS) approach Communication delay Duty cycle Network lifetime |
author_facet |
Minrui Wu Yanhui Wu Xiao Liu Ming Ma Anfeng Liu Ming Zhao |
author_sort |
Minrui Wu |
title |
Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things |
title_short |
Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things |
title_full |
Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things |
title_fullStr |
Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things |
title_full_unstemmed |
Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things |
title_sort |
learning-based synchronous approach from forwarding nodes to reduce the delay for industrial internet of things |
publisher |
SpringerOpen |
series |
EURASIP Journal on Wireless Communications and Networking |
issn |
1687-1499 |
publishDate |
2018-01-01 |
description |
Abstract The Industrial Internet of Things (IIoTs) is creating a new world which incorporates machine learning, sensor data, and machine-to-machine (M2M) communications. In IIoTs, the length of the transmission delay is one of the pivotal performance because dilatory communication will cause heavy losses to industrial applications. In this paper, a learning-based synchronous (LS) approach from forwarding nodes is proposed to reduce the delay for IIoTs. In an asynchronous Media Access Control protocol, when senders need to send data, they always require to wait for their corresponding receiver to wake up. Thus, the delay here is greater than in the synchronous network. However, the synchronization cost of the whole network is enormous, and it is difficult to maintain. Therefore, LS mechanism uses a partial synchronization approach to reduce synchronization costs while effectively reducing delay. In LS approach, instead of synchronizing the nodes in the entire network, only sender nodes and part of the nodes in their forwarding node set are synchronized by self-learning methods, and accurate synchronization is not required here. Thus, the delay can be effectively reduced under the low cost. Secondly, the nodes near sink maintain the original duty cycle, while the nodes in the regions away from the sink use their remaining energy and perform synchronization operations, so as not to damage the network lifetime. Finally, because the synchronization in this paper is based on different synchronization periods among different nodes, it can improve the network performance by reducing the conflict between simultaneous data transmission. The theoretical analysis results show that compared with the previous approach FFSC, LS approach can reduce the end-to-end delay by 5.13–11.64% and increase the energy efficiency by 14.29–17.53% under the same lifetime with a more balanced energy utilization. |
topic |
Industrial Internet of Things Learning-based synchronous (LS) approach Communication delay Duty cycle Network lifetime |
url |
http://link.springer.com/article/10.1186/s13638-017-1015-z |
work_keys_str_mv |
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