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Asset management was a common RFID-based Internet-of-Things (IoT) application scene. RFID tags in the equipment warehouse were usually large, and the communication between the reader and the tag was prone to data collision problems, which affected the recognition efficiency of the device. In practical applications, due to the structural characteristics of the micro-strip UHF RFID tag antenna, the traditional inter-coupling impedance expression had large errors and insufficient accuracy in predicting the mutual coupling effect, such as system frequency shift. In this paper, the 3D initialization model of the tag was used to indirectly extract the electrical parameter values by the ANSYS HFSS software. At the same time, the dualtag was taken as an example to derive the transimpedance expression between the dense tags to extract the corresponding coupling parameters. Finally, various tag-intensive scenarios in the actual environment were tested and the derivation formula was verified, and the dual-tag UHF RFID near-field frequency shift affected by the environmental factors, such as relative position, attachment, and the stacking method, was discussed. The mutual coupling effect on the minimum transmit power of the reader antenna was also studied. The experimental results showed that the average error of the formula calculated by this method was significantly smaller than that of the traditional formula. When the tag spacing was less than 30 mm, the derived mutual impedance expression was applied to the frequency shift calculation error range (1.6-7.3 MHz). For dense tag systems, the error was less than 9.8% when the number of tags was greater than 7, and the prediction accuracy was higher than the superposition method. The research results provided a theoretical and practical basis for the rapid identification and location of power assets during the dense RFID tag environment. INDEX TERMS UHF RFID, Internet of Things, power asset management, frequency shift, mutual couple effect, mutual impedance.

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As a promising wireless communication technology, the IEEE802.11ah standard is designed to connect various sensors in the Internet of Things (IoT) in future. It is important to investigate adaptive transmission in the IEEE802.11ah standard. However, exact channel state information (CSI) is required. Channel prediction is an available approach. Therefore, an adaptive elastic echo state network (AEESN) for channel prediction in the IEEE802.11ah standard-based orthogonal frequency division multiplexing (OFDM) system is introduced in this paper. The AEESN includes two key components, a basic echo state network and an adaptive elastic network. The latter is imported to overcome collinearity problems due to vast neurons in the former and to avoid ill-conditioned solutions when estimating output weights in the former. Moreover, the latter can produce sparse output weights, which reduces memory storage requirements. To evaluate system performances, 1MHz and 2MHz bandwidth cases with specified parameters are tested. One-step prediction, multi-step prediction and robustness are evaluated for various signal to noise ratios (SNRs). The results indicate that the AEESN not only offers satisfactory prediction performance, but also effectively avoids ill-conditioned solutions and produces sparse output weights. Therefore, it can assure adaptive IoT communication. INDEX TERMS IEEE802.11ah standard, OFDM system, channel prediction, echo state network, adaptive elastic network.

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