WiFi signal has become accessible everywhere, providing high-speed data transmission experience. Besides the communication service, channel state information (CSI) of the WiFi signals is widely employed for numerous Internet of Things (IoT) applications. Recently, most of these applications are based on analysis of the microwave reflections caused by physical movement of the objective. In this paper, a novel contactless wireless sensing technique named WiEps is developed to measure the dielectric properties of the material, exploiting the transmission characteristics of the WiFi signals. In WiEps, the material under test is placed between the transmitter antenna and receiver antenna. A theoretical model is proposed to quantitatively describe the relationship between CSI data and dielectric properties of the material. During the experiment, the phase and amplitude of the transmitted WiFi signals are extracted from the measured CSI data. The parameters of the theoretical model are calculated using measured data from the known materials. Then, WiEps is utilized to estimate the dielectric properties of unknown materials. The proposed technique is first applied to the ethanol/water mixtures. Then, additional liquids are measured for further verification. The estimated permittivities and conductivities show good agreement with the actual values, with the average error of 4.0% and 8.9%, respectively, indicating the efficacy of WiEps. By measuring the dielectric property, this technique is promising to be applied to new IoT applications using ubiquitous WiFi signals, such as food engineering, material manufacturing process monitoring, and security check.

A precise non-invasive estimation method for blood glucose concentration is proposed using an Improved Neural Network model and Hybrid Optimization (INNHO) method. This method consists of a modified back propagation neural network model and a hybrid least squares-random sample consensus algorithm for accuracy optimization. The S-parameter and frequency data measured by ultra-wide band (UWB) microwave are employed as the input data for glucose concentration estimation. Before model training, a preprocess technique is put forward to select the data range and generate relative S-parameters from a reference baseline. In this study, a three-layer earlobe model is built to theoretically analyze the wave propagation and elucidate the relationship between S-parameters and the glucose concentration. Based on the model, the sensitivity and uncertainty of the system are investigated and the suitable frequency range is determined. During the measurement, the S-parameters of UWB signals in 0.2-4 GHz range are measured and the concentration of glucose water solutions from 20 mg/dL to 500 mg/dL is investigated. Results show that using the proposed preprocessing method and neural networks, the estimation accuracy can be improved significantly. Then the hybrid optimization algorithm is able to further enhance the estimation accuracy. The final estimations for solutions of unknown concentrations agree well with the true concentration values. The relative error ranges from 0.31% to 4.64% and the root mean square error of final results is 5.5237 mg/dL, demonstrating that the proposed method is effective and precise for glucose concentration estimation.

Dynamic adaptive video streaming over HTTP (DASH) is widely studied and has been adopted in modern video players to ensure user quality of experience (QoE). In DASH, adaptive bitrate control is a key part whose ultimate goal is to maximize video bitrate while minimizing rebuffering. Throughput prediction plays an important role in helping select the proper video bitrate dynamically. In this paper, we studied the influence of throughput prediction on adaptive video streaming. Because the real-world network is dynamic, different methods need to be tested with large-scale deployments and analyzed statistically. However, this is difficult in academic research. Therefore, we established a reproducible trace-based emulation environment, which enables us to compare different methods quantitatively under the artificially same condition, with limited experiments. The throughput prediction methods are implemented into DASH to evaluate the effect on QoE for video streaming. The results indicate that the prediction method using long short-term memory (LSTM) performs better than the other methods. However, throughput prediction alone is not enough to ensure high QoE. To further improve the QoE, we proposed the decision map method (DMM), where the buffer occupancy is also incorporated to make a selection. By using this decision map, the choice of bitrate can be smarter than that when only prediction information is used. The total QoE is further improved by 32.1% in the ferry trace, which shows the effectiveness of DMM in further improving the performance of throughput prediction in adaptive bitrate control.

Owing to the plentiful information released by the commodity devices, Wi-Fi signals have been widely studied for various wireless sensing applications. In many works, both received signal strength indicator (RSSI) and the channel state information (CSI) are utilized as the key factors for precise sensing. However, the calculation and relationship between RSSI and CSI is not explained in detail. Furthermore, there are few works focusing on the measurement variation of the Wi-Fi signal which impacts the sensing results. In this article, the relationship between RSSI and CSI is studied in detail and the measurement variation of amplitude and phase information is investigated by extensive experiments. In the experiments, the transmitter and receiver are directly connected by power divider and RF cables and the signal transmission is quantitatively controlled by RF attenuators. By changing the intensity of attenuation, the measurement of RSSI and CSI is carried out under different conditions. From the results, it is found that in order to get a reliable measurement of the signal amplitude and phase by commodity Wi-Fi, the attenuation of the channels should not exceed 60 dB. Meanwhile, the difference between two channels should be lower than 10 dB. An active control mechanism is suggested to ensure the measurement stability. The findings and criteria of this work is promising to facilitate more precise sensing technologies with Wi-Fi signal.

Hydration status is a critical indicator for human health, yet there are relatively few studies on monitoring the hydration status of the human body. Traditional methods for hydration monitoring have shortcomings such as being invasive, time-consuming to detect, and not being real time. Recently, microwave hydration monitoring has attracted research interest widely. This approach is noninvasive and based on the change of dielectric properties of human tissues with different hydration statuses. In this article, a fused learning and enhancing method is proposed to accurately monitor the hydration by using an ultrawideband (UWB) microwave. In the proposed method, an Elman neural network learning model and a joint enhancing method are designed. The joint enhancing algorithm is composed of $k$ -medoids and least squares. To verify the performance of the proposed method, a muscle phantom is developed using bovine serum albumin. By changing the concentration, the phantom can mimic the muscle with different hydration statuses. In the evaluation experiment, the S-parameter and frequency data were measured with UWB microwave. Then, the data were employed for assessing the level of hydration with the proposed method. Experimental results showed that the relative error for estimating the concentration of the muscle phantom is less than 3.3%. These results demonstrated the feasibility of the proposed hydration monitoring method and proved that it is promising for monitoring human hydration status.