Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine

Shu, Jian; Song, Lei; Liu, Linlan; Li, Zhan; Hu, Gang

doi:10.1049/cje.2017.01.013

Cited by 80 publications

(79 citation statements)

References 8 publications

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“…Fu et al [ 29 ] proposed RADIUS, a thresholding method based on Bayes theory, which uses mean value and variance of RSSI to identify the degradation of links, namely, from good links to bad links. Shu et al [ 30 ] proposed a link quality classification model, which fuses two physical layer parameters LQI and RSSI and trains the mean values of them by support vector machine. Sun et al [ 22 ] proposed WNN-LQE, which employs a wavelet neural network to predict SNR and its variance, and then estimates link quality quantitatively using the theoretical model between SNR and PRR .…”

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%

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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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Section: Related Workmentioning

confidence: 99%

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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“…However, similar to many other models, this model cannot be applied in real-time systems that predict secondary incidents because the model needs real parameters to train and build the prediction results. Following the trend of implementing methods for link quality estimation based on DT and support vector machine (SVM) to propose an improved route of data delivery over WSNs, Shu et al [42] utilized two parameters for estimation-namely, receive signal strength and link quality indicator. DT was combined with SVM in this model because SVM can only handle problems with binary classification.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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“…Different types of link quality metrics have different characteristics. For example, hardware-based metrics evaluations can reflect changes in link quality in real time [9]. On the other hand, software-based metrics can evaluate the link quality more accurately than hardware-based metrics [10].…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

Huang

Por

Ang

et al. 2019

Advances in Fuzzy Systems

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Link quality estimation is essential for improving the performance of a routing protocol in a wireless sensor network. Many methods have been proposed to increase the performance of the link quality estimation; however, most of them are not able to evaluate link quality accurately. In this study, a method that uses fuzzy logic to combine both hardware-based and software-based metrics is proposed to improve the accuracy rate for evaluating a link quality. This proposed method consists of three types of modules, the Fuzzifier module, the Inference module, and the Defuzzifier module. The Fuzzifier module is used to determine the degree to which input link quality metrics belong to each fuzzy set through proposed membership functions. The Inference module obtains the rule outputs based on the proposed fuzzy rules and the given inputs acquired from the Fuzzifier module. The Defuzzifier module is used to aggregate the rule outputs inferred from the Inference module. The result from the Defuzzifier module is then used to evaluate the link quality. A simulation conducted to compare the accuracy rates of the proposed method and those found in related works showed that the proposed method had higher accuracy rates for evaluating a link quality.

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Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine

Cited by 80 publications

References 8 publications

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

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

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Improving the Accuracy Rate of Link Quality Estimation Using Fuzzy Logic in Mobile Wireless Sensor Network

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