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

Abstract

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.

A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

“…Then, the link quality estimation model is constructed by logistic regression to reflect the link quality. Xue [14] decomposes the raw SNR sequence into the time-varying sequence and stochastic sequence. A random-vector-functional-link-based algorithm is used to predict the two sequences separately.…”

“…To further verify the estimation ability of the link quality estimation model SCForest-LQE, we conduct more comparison experiments, and the results are shown in Figures.11-13. The gcForest-based model (gcForest), the random forest-based model [19] (RFC), the wavelet neural network-based model [13] (WNN-LQE), the naive Bayesbased model [11] (NB), the stacked autoencoder-based model [20] (LQE-SAE) and the lightweight, fluctuation insensitive multi-parameter fusion-based model [14] (LFI-LQE) are chosen to compare with the proposed estimator.…”

MobETX: uma métrica de roteamento ciente da mobilidade para Internet das Coisas

“…Entretanto, esses LQEs leem apenas pacotes recebidos, tornando difícil, por exemplo, a classificac ¸ão de enlaces com alta taxa de perda de pacotes e, como consequência, podem superestimar os enlaces. Detalhes dos LQEs apresentados na Figura 3 podem ser encontrados em [Baccour et al 2012, Tan et al 2012, Sun et al 2017, Xue et al 2020]. Neste trabalho, introduziremos o conceito de LQEs híbridos de forma a estender a classificac ¸ão apresentada em [Baccour et al 2012].…”

Spatiotemporal D2D Small Cell Allocation and On-Demand Deployment for Microgrids

“…Gaining an annual growing rate as 14% with more than 27 power capacity worldwide [1], microgrids have been playing an important role on the advanced smart grid development, capable of managing local energy resources and distributed power systems resiliently for critical communities [2]. As a typical cyber-physical system (CPS), prospective exceptional performance of microgrids is constructed upon the optimal deployment of communication technologies [3]- [5]. Heterogeneous Small Cell Networks…”