WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs

Sun, Wei; Lu, Wei; Li, Qiyue; Chen, Liangfeng; Mu, Daoming; Yuan, Xiaojing

doi:10.1109/access.2017.2723360

Cited by 69 publications

(82 citation statements)

References 23 publications

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“…They discover a close relationship between solar radiation and actual air temperature, which can be effectively learned by neural networks. In [350], Sun et al employ a Wavelet neural network based solution to evaluate radio link quality in WSNs on smart grids. Their proposal is more precise than traditional approaches and can provide end-to-end reliability guarantees to smart grid applications.…”

Section: Wsn Data Analysismentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,335

838

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The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space.In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.

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Section: Wsn Data Analysismentioning

confidence: 99%

Deep Learning in Mobile and Wireless Networking: A Survey

Zhang

Patras

Haddadi

2019

IEEE Commun. Surv. Tutorials

1,335

838

View full text Add to dashboard Cite

show abstract

“…Therefore, PRR could be computed using the theoretical bit error rate model and SNR , which can be calculated by subtracting background noise from RSSI . For instance, Sun et al [ 21 ] and Chang et al [ 22 ] respectively use the theoretical model of DSSS-OQPSK for PRR estimation. When there are no co-channel interferences, the background noise usually remains stable for a few seconds or even minutes.…”

Section: Related Workmentioning

confidence: 99%

“…It can be seen that most studies are conducted in a single environment. Although some studies [ 5 , 7 , 9 , 10 , 12 , 20 , 21 , 23 , 25 , 26 , 28 ] considered two or three different environments, they did not explore the impacts of environmental changes on the applicability of hardware-based LQEs. In fact, WSN applications may face a variety of deployment environments.…”

Section: Related Workmentioning

confidence: 99%

“…According to Figure 3 , the theoretical model and logistic regression model (for short, LR model) are usually used as mapping models between SNR and PRR for SNR based LQEs. In [ 21 ], the theoretical model was used as the mapping model between averaged SNR (defined as μ snr ) and PRR , as shown in Equation (8).

where Q (·) represents the Q function, l is the number of bits in a packet, R is the data rate in kb/s, and B N is the noise bandwidth of the transceiver in kHz.…”

Section: Typical Hardware-based Lqes: a Surveymentioning

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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“…The authors in [19] proposed that nodes monitor RSSI, SINR, and packet reception rates; exchange information with neighbors; and use this information as input to a supervised learning algorithm that uses labeled training samples to estimate the quality of links. The authors in [47] proposed estimating the probability of successful packet transmission in a link by using wavelet and neural network techniques. Their approach would require decomposing measurements of the SINR into a time-varying component and a non-stationary random part.…”

mentioning

confidence: 99%

Improving Route Selections in ZigBee Wireless Sensor Networks

Meka

Fonseca

2019

Sensors

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The ZigBee wireless communication specifications forecast the use of multihop routes between nodes and define that nodes select their routes based on their costs. The specifications define how to compute a route cost from the probability of successfully transmitting on each of the routes’ links; and it is recommended that such probabilities be obtained by counting received link status messages or averaging link quality indicators from received packets. In this paper, we study the performance of these two recommended procedures, show that they can lead to degraded route selections, and propose a procedure that can improve route selections without modifications to the ZigBee protocol or frame formats. Our procedure estimates the probability of successful transmission on each link, based on information from the medium access layer during unicast packet transmissions, and includes a modification into how ZigBee nodes treat routing messages internally in order to reduce variations in the link cost estimates. Focusing on a home environment with one or two hops, our simulation results show that, in several scenarios, our procedure performs better than either of the two procedures recommended in the ZigBee specifications.

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WNN-LQE: Wavelet-Neural-Network-Based Link Quality Estimation for Smart Grid WSNs

Cited by 69 publications

References 23 publications

Deep Learning in Mobile and Wireless Networking: A Survey

Deep Learning in Mobile and Wireless Networking: A Survey

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

Improving Route Selections in ZigBee Wireless Sensor Networks

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