A green and reliable communication modeling for industrial internet of things

Most applications require high reliability with low delay in wireless sensor networks (WSNs). Unfortunately, it is a challenge issue to achieve reliable communication because of the lossy nature of wireless channels. In this paper, a novel unequal redundancy level (URL) data collection scheme is proposed to achieve high reliability for wireless sensor networks while long lifetime can be still guaranteed. Aiming at the imbalance of energy consumption in WSNs, a URL data collection scheme adopts network coding-based mechanism with high redundancy level in non-hotspot areas while adopting low redundancy level in hotspot areas. The theoretical analysis and experimental results show that, comparing with equal redundancy level (ERL) approach, the URL approach improves the lifetime by 15-50 % under the same reliability, and it can also improve the reliability by 14.1-28.3 % under the same lifetime. Comparing with an SW-ARQ approach, the URL approach improves the lifetime by 7-30 % under the same reliability, and it can also improve the reliability by 8-23 % under the same lifetime.

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“…Thus, there is a tradeoff between reliability and energy efficiency [34,35]. We confirm this situation by Figs.…”

Section: Observationsupporting

confidence: 75%

An unequal redundancy level-based mechanism for reliable data collection in wireless sensor networks

Zhang

Liu

2016

J Wireless Com Network

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“…When the distance from a node and the sink is L, the delay of end-to-end transmission can be calculated in Eq. (13).…”

Section: Delay 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.…”

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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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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“…The Internet of Things (IoT) [1][2][3][4] uses smart equipment such as sensor-based devices [5][6][7][8][9][10][11], smartphones [12] and vehicle sensor devices [13] to gathering information and give a different method for carrying out complex data-sensing-based tasks to satisfy the significant demands of critical infrastructures [14][15][16][17][18], such as environmental and weather monitoring systems [19][20][21][22][23]. Wireless Sensor Networks (WSNs) has large numbers of sensor nodes, it has huge potential to solve military and civilian domains [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Reliability Improved Cooperative Communication over Wireless Sensor Networks

Chen

Liu

et al. 2017

Symmetry

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Abstract:With the development of smart devices and connection technologies, Wireless Sensor Networks (WSNs) are becoming increasingly intelligent. New or special functions can be obtained by receiving new versions of program codes to upgrade their software systems, forming the so-called smart Internet of Things (IoT). Due to the lossy property of wireless channels, data collection in WSNs still suffers from a long delay, high energy consumption, and many retransmissions. Thanks to wireless software-defined networks (WSDNs), software in sensors can now be updated to help them transmit data cooperatively, thereby achieving more reliable communication. In this paper, a Reliability Improved Cooperative Communication (RICC) data collection scheme is proposed to improve the reliability of random-network-coding-based cooperative communications in multi-hop relay WSNs without reducing the network lifetime. In WSNs, sensors in different positions can have different numbers of packets to handle, resulting in the unbalanced energy consumption of the network. In particular, nodes in non-hotspot areas have up to 90% of their original energy remaining when the network dies. To efficiently use the residual energy, in RICC, high data transmission power is adopted in non-hotspot areas to achieve a higher reliability at the cost of large energy consumption, and relatively low transmission power is adopted in hotspot areas to maintain the long network lifetime. Therefore, high reliability and a long network lifetime can be obtained simultaneously. The simulation results show that compared with other scheme, RICC can reduce the end-to-end Message Fail delivering Ratio (MFR) by 59.4%-62.8% under the same lifetime with a more balanced energy utilization.

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A green and reliable communication modeling for industrial internet of things

Cited by 65 publications

References 22 publications

An unequal redundancy level-based mechanism for reliable data collection in wireless sensor networks

An unequal redundancy level-based mechanism for reliable data collection in wireless sensor networks

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

Reliability Improved Cooperative Communication over Wireless Sensor Networks

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