CCI-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss

Shu, Jian; Tao, Juan; Liu, Linlan; Chen, Yebin; Zang, Chao

doi:10.1016/s1005-8885(13)60001-1

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Luo et al [13] used Cubic model to fit the relation between average LQI and PRR. Shu et al [14] proposed K-CCI, in which Kalman filtering is used to preprocess LQI and link quality is estimated by constructing a mapping model between smoothed LQI and PRR. Senel et al [15] proposed KLE, in which Kalman filtering is used to preprocess RSSI.…”

Section: Related Workmentioning

confidence: 99%

“…where f (•) represents the mapping model between SNR and PRR, which can be implemented in the form of lookup table. K-CCI uses Kalman filtering to preprocess LQI and then estimates link quality quantitatively using a mapping model between LQI and PRR, which is constructed using Cubic model [14]. The estimated PRR of i-th window is:…”

Section: Link Quality Estimators For Comparisonmentioning

confidence: 99%

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Lightweight, Fluctuation Insensitive Multi-Parameter Fusion Link Quality Estimation for Wireless Sensor Networks

et al. 2020

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Accurate and agile link quality estimation is essential for wireless sensor networks. Using the mapping models between physical layer parameters and packet reception ratio, link quality can be estimated with advantages of high agility and low overhead. However, existing estimators based on physical layer parameters fail to utilize link quality information carried by different physical layer parameters efficiently and effectively and fail to effectively solve the problem that physical layer parameters fluctuate greatly, which makes them difficult to describe link conditions really. In this study, a lightweight, fluctuation insensitive multi-parameter fusion link quality estimator is proposed. Two physical layer parameters, Signal-to-Noise Ratio and Link Quality Indicator are preprocessed by exponential weighted Kalman filtering to get more stable estimation values. Then, these two parameters are fused using lightweight weighted Euclidean distance to fully utilize link quality information carried by them. On this basis, link quality is estimated quantitatively with the mapping model of the fused parameter and packet reception ratio, which is constructed by logistic regression. Experimental results show that the proposed estimator could reflect link quality more realistically. Compared with similar estimators, estimate error of the proposed one is reduced by 18.32% to 60.11% under moderate and bad links with large fluctuations, by 1.42% to 83.43% under sudden changed links, and by 16.64% to 65.61% under a long-time link. More importantly, computation overhead of the proposed estimator is equivalent to that of single-parameter estimators, but much less than other multi-parameter fusion estimators. Compared with the later, computation overhead is reduced by 72.36% to 95.61%.

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Section: Related Workmentioning

confidence: 99%

Section: Link Quality Estimators For Comparisonmentioning

confidence: 99%

Lightweight, Fluctuation Insensitive Multi-Parameter Fusion Link Quality Estimation for Wireless Sensor Networks

et al. 2020

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“…Jayasri et al also pointed out that the correlation coefficient of LQI and PRR is higher than that of RSSI [ 6 ]. By studying the relationship between LQI and PLR in outdoor environments, Shu et al [ 16 ] pointed out that there is a definite relationship between LQI and PLR. Luo et al [ 17 ] fitted a mapping model between LQI and PRR using the Cubic model.…”

Section: Related Workmentioning

confidence: 99%

“…To observe the environmental impact on the relationship between LQI and PRR more clearly, the relationship between μ lqi and PRR is shown in Figure 21 . There already exist some studies which utilize the relationship between LQI and PRR to estimate link quality [ 6 , 15 , 16 , 17 , 18 , 26 , 27 , 28 , 30 ]. It can be seen that there is no obvious difference among the relationships between μ lqi and PRR in different environments.…”

Section: Environmental Impacts On Hardware-based Lqesmentioning

confidence: 99%

Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks

Liu

Xia

Zheng

et al. 2020

Sensors

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Hardware-based link quality estimators (LQEs) in wireless sensor networks generally use physical layer parameters to estimate packet reception ratio, which has advantages of high agility and low overhead. However, many existing studies didn’t consider the impacts of environmental changes on the applicability of these estimators. This paper compares the performance of typical hardware-based LQEs in different environments. Meanwhile, aiming at the problematic Signal-to-Noise Ratio (SNR) calculation used in existing studies, a more reasonable calculation method is proposed. The results show that it is not accurate to estimate the packet reception rate using the communication distance, and it may be useless when the environment changes. Meanwhile, the fluctuation range of the Received Signal Strength Indicator (RSSI) and SNR will be affected and that of Link Quality Indicator (LQI) is almost unchanged. The performance of RSSI based LQEs may degrade when the environment changes. Fortunately, this degradation is mainly caused by the change of background noise, which could be compensated conveniently. The best environmental adaptability is gained by LQI and SNR based LQEs, as they are almost unaffected when the environment changes. Moreover, LQI based LQEs are more accurate than SNR based ones in the transitional region. Nevertheless, compared with SNR, the fluctuation range of LQI is much larger, which needs a larger smoothing window to converge. In addition, the calculation of LQI is typically vendor-specific. Therefore, the tradeoff between accuracy, agility, and convenience should be considered in practice.

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“…The success of message transmission as well as the energy consumed by transmission is greatly affected by the number of nodes in a mine roadway, the number of messages to be transmitted, and message size. Finally, the parameters of the physical layer, link layer, and network layer can be combined [33] to design cross-layer opportunistic routing algorithms. emergency, such as a coal mine flood, the presence of excessive gas, and production accidents.…”

Section: Opportunistic Routings Of Intermittent or Regional Connectiomentioning

confidence: 99%

A Novel Cognitive Opportunistic Communication Framework for Coal Mines

Ding

2019

Mathematical Problems in Engineering

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The dynamic advancement and harsh environment of coal mines often result in intermittent or regional wireless connection between sending nodes and receiving nodes and then lead to the decrease of transmission success ratio and even failure. To solve this problem, the environmental cognition and best-effort transmission are both demanded. Here we proposed a novel communication framework for coal mines based on a cognitive opportunistic concept to address the wireless network communication problems in coal mines, which consists of the node mobility model in coal mines, cooperative cognition of the time-varying communication environment, and the opportunistic routing of intermittent or regional connection scenarios. To realize this framework, real time neighbor discovering mechanisms and mobility perceiving strategies, called environment cognition, must be deeply investigated to predict the trends of node movement. The obtained results of environment cognition are then used to analyze current channel characteristics in order to determine and set optimum communication system parameters and reduce the probability of intermittent or regional connection. To address those unavoidable situations of the intermittent or regional connection, the opportunistic routing mechanism is brought forward to provide relatively stable data transmission. Finally, as an example of cognitive opportunistic mine communication of this framework, personnel evacuation under an emergency is discussed.

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CCI-based link quality estimation mechanism for wireless sensor networks under non-perceived packet loss

Cited by 5 publications

References 23 publications

Lightweight, Fluctuation Insensitive Multi-Parameter Fusion Link Quality Estimation for Wireless Sensor Networks

Lightweight, Fluctuation Insensitive Multi-Parameter Fusion Link Quality Estimation for Wireless Sensor Networks

Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks

A Novel Cognitive Opportunistic Communication Framework for Coal Mines

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