Context-aware collect data with energy efficient in Cyber–physical cloud systems

Liu, Yuxin; Guo, Shuang; Li, Zhetao; Choi, Young‐June; Sekiya, Hiroo

doi:10.1016/j.future.2017.05.029

Cited by 59 publications

(31 citation statements)

References 34 publications

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“…(20). Because the energy consumption of packet transmission and reception is calculated in the equation before, ψ …”

Section: Energy Consumption Analysismentioning

confidence: 99%

“…Based on IoT technologies, the Industrial Internet of Things (IIoTs) [13] corporates machine learning and big data technology, sensor data, and machine-to-machine (M2M) communications that have existed in industrial areas for years [14][15][16]. In IIoTs, huge amounts of wireless sensor nodes [17][18][19][20][21][22][23][24] can be deployed more conveniently than the previous wired network to monitor the whole process of industrial production from many angles, in real time or semi-real time. In such way, IIoTs is creating a new world for industrial manufacturing, where the workers or managers can manage their industrial manufacturing in more informed ways and can make more opportune and better informed decisions.…”

Section: Introductionmentioning

confidence: 99%

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Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Xiao

et al. 2018

J Wireless Com Network

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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.

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“…(20). Because the energy consumption of packet transmission and reception is calculated in the equation before, ψ …”

Section: Energy Consumption Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Xiao

et al. 2018

J Wireless Com Network

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“…Reference [33] introduced IOT and WSN in a mine and described the mine production, monitoring, and prewarning system based on big data technology, but it did not explain how to use sensor nodes and how to build the prewarning model. References [34][35][36][37][38][39][40][41][42][43][44] designed an architecture of monitoring system based on WSN and discussed a data aggregation strategy but did not consider using big data. Our paper focuses on how to design the monitoring and prewarning system suitable for coal mine industry, how to use data aggregation strategy to decrease energy consumption, and how to build a prewarning model to prevent the accident happening in advance.…”

Section: Related Studiesmentioning

confidence: 99%

“…The Coalmining tunnels in China usually are categorized with their functions, including development tunnels, preparation tunnels, and mining tunnels [23,30,[35][36][37][38][39][40][41][42][43][44]. The development tunnels are served for the whole mine, including the horizontal mining area, such as the main haulage roadways and the main return airways.…”

Section: Structure Of Monitoring Networkmentioning

confidence: 99%

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Chen

Wei

2018

Scientific Programming

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More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

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“…Topology control technology brings benefits to the network. (1) Reducing the energy consumption of nodes [10][11][12][13][14]: in wireless sensor networks, the density of communication devices is usually large [15]. When communication between devices to devices (D2D) is required, the transmission power can be adaptively adjusted according to the distance between devices (or nodes).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Transmission Range Based Topology Control Scheme for Fast and Reliable Data Collection

Teng

Dong

Ota

et al. 2018

Wireless Communications and Mobile Computing

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An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows:(1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near-sink nodes is reduced, the energy consumption of the near-sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.

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Context-aware collect data with energy efficient in Cyber–physical cloud systems

Cited by 59 publications

References 34 publications

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Learning-based synchronous approach from forwarding nodes to reduce the delay for Industrial Internet of Things

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Adaptive Transmission Range Based Topology Control Scheme for Fast and Reliable Data Collection

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