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 time-varying 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.
Highlights
The increasing demand for the reliability of the electricity supply promotes the modernization of the power grid system, which is called the smart grid
APPLICATION EXAMPLE To illuminate the proposed RVFL-link quality prediction (LQP) algorithm for realworld application in an industrial environment, we predict the signal-to-noise ratio (SNR) for a wireless link at an indoor substation
In this paper, by analyzing the log-normal path-loss model and using the decomposition method, the SNR time sequence in the wireless link is decomposed into two different parts due to the time-varying and stochastic characteristics
Summary
The increasing demand for the reliability of the electricity supply promotes the modernization of the power grid system, which is called the smart grid. The LQP methods proposed in [12]–[15] provide an accurate numerical prediction They all use the deterministic numerical prediction results to describe the stochastic features of link quality. 1) PHYSICAL METRICS BASED LQP The hardware of WSNs node, for example, the TI CC2530, a widely used low power radio chip, provides the Received Signal Strength Indication (RSSI) and the Link-Quality Indication (LQI) after successful transmission. Liu et al [29] present a link quality evaluation approach based on 4-bits, which combines the information of the network layer, data link layer, and physical layer This method could significantly improve the accuracy and timeliness of link estimation. For the prediction of the nonlinear model, linearization is required, and the linearization introduces errors
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