LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

Zhou, Xinyan; Ji, Xiaoyu; Chen, Yichao; Li, Xiaopeng; Xu, Wenyuan

doi:10.3390/s18030747

Cited by 7 publications

(6 citation statements)

References 57 publications

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“…Then combining the obtained a q , b q , ω with the test data, we could calculate the prediction results for the test data by (13).Ŷ =Ĥ ω (13) where the input data contained inĤ is the test data, andŶ is the prediction results for test data.…”

Section: B Rvfl Network Prediction Modelmentioning

confidence: 99%

“…We first performed the simple mathematical operation through equation 6- (8) to decompose SNR. Then we use the RVFL network to establish the prediction model by equation (10)- (13). The RVFL randomly initializes all weights and biases between the input layer and hidden layer nodes, and the equation (12) is just to obtain the output weights.…”

Section: Application Examplementioning

confidence: 99%

“…The RVFL randomly initializes all weights and biases between the input layer and hidden layer nodes, and the equation (12) is just to obtain the output weights. Then we use equation (13) to calculate the outputs. For the RVFL network, we chose the number of input layer nodes N I = 2 • m = 16, the number of output layer nodes N O = 2, and the optimal number of hidden layer nodes N H = 33.…”

Section: Application Examplementioning

confidence: 99%

“…Shu et al [12] propose an LQP mechanism which based on dynamic Bayesian networks to achieve better accuracy and robustness. Zhou et al [13] design a compressive-sensing-based LQP to aid the link update in routing protocols. Qin et al [14] propose an augmented Kalman-filter-based LQP, the results of experiments show that LQP performance is significantly improved in comparison with conventional Kalman-filter-based LQP.…”

Section: Introductionmentioning

confidence: 99%

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RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

et al. 2020

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In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the timevarying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively. INDEX TERMS Wireless sensor networks, link quality prediction, RVFL network, probability-guaranteed interval boundary.

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Section: B Rvfl Network Prediction Modelmentioning

confidence: 99%

Section: Application Examplementioning

confidence: 99%

Section: Application Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

et al. 2020

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“…IWSNs are sometimes deployed in extremely harsh environments such as underground mines [ 82 ] and intertidal habitats [ 83 ]. Deploying IWSNs in this scenario introduce several monitoring and communication challenges [ 84 ].…”

Section: Observations and Recommendationsmentioning

confidence: 99%

A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study

Ramotsoela

Abu‐Mahfouz

Hancke

2018

Sensors

113

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The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.

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Modeling LoRa Communications in Estuaries for IoT Environmental Monitoring Systems

et al. 2022

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Low-power wide-area networks are extending beyond the conventional terrestrial domain. Coastal zones, rivers, wetlands, among others, are nowadays common deployment settings for Internet-of-Things nodes where communication technologies such as LoRa are becoming popular. In this article, we investigate large-scale fading dynamics of LoRa line-of-sight links deployed over an estuary with characteristic intertidal zones, considering both shore-to-shore and shore-to-vessel communications. We propose a novel methodology for path loss prediction which captures i) spatial, ii) temporal and iii) physical features of the RF signal interaction with the environmental dynamics, integrating those features into the two-ray propagation model. To this purpose, we resort to precise hydrodynamic modeling of the estuary, including the specific terrain profile (bathymetry) at the reflection point. These aspects are key to accounting for a reflecting surface of varying altitude and permittivity as a function of the tide. Experimental measurements using LoRa devices operating in the 868~MHz band show major trends on the received signal power in agreement with the methodology's predictions.

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LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

Cited by 7 publications

References 57 publications

RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study

Modeling LoRa Communications in Estuaries for IoT Environmental Monitoring Systems

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