The number of electric vehicles in various countries has shown exponential growth so that the related industries to face the tremendous pressure of power batteries disposal. Efficient secondary use and recycling of power batteries require effective collection of battery data and reasonable estimation of battery state-of-health (SOH). In this paper, we propose a framework to collect battery charging data from different stakeholders with an anomaly detection method based on Isolation Forest with two features. Besides a score-based mechanism is adopted to do data screening and capture the data with good quality. Unlike prior works, our proposed method can exploit crowdsourced data to reduce the significant effort of battery data sensing and provide a data source scoring mechanism based on blockchain to improve the data quality and meet the requirement of reasonable estimation. In order to verify the effectiveness of the proposed collection method, a charge data test set is constructed based on the NASA battery data set. The simulation results indicate that the method increases the F-measure criteria up to 25.65% compared to the well-known anomaly detection algorithms. In addition, the proposed collection method outperforms the traditional method up to 10.9% in reducing the relative error when being used for SOH estimation.

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.

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.

Throughput prediction is one of the promising techniques to improve the quality of service (QoS) and quality of experience (QoE) of mobile applications. To address the problem of predicting future throughput distribution accurately during the whole session, which can exhibit large throughput fluctuations in different scenarios (especially scenarios of moving user), we propose a history-based throughput prediction method that utilizes time series analysis and machine learning techniques for mobile network communication. This method is called the Hybrid Prediction with the Autoregressive Model and Hidden Markov Model (HOAH). Different from existing methods, HOAH uses Support Vector Machine (SVM) to classify the throughput transition into two classes, and predicts the transmission control protocol (TCP) throughput by switching between the Autoregressive Model (AR Model) and the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM). We conduct field experiments to evaluate the proposed method in seven different scenarios. The results show that HOAH can predict future throughput effectively and decreases the prediction error by a maximum of 55.95% compared with other methods.

To realize highly reliable video surveillance and provide ultrahigh-definition/immersive video streaming, it is planned to adopt the 5G cellular system using millimeter-wave (mmWave) as the wireless-network infrastructure. However, mmWave communication has a challenging issue: mmWave communication is extremely sensitive to obstacles, such as walls, pillars, and even human bodies, and this issue easily increases the packet loss rates and round trip time (RTT) (or disconnection from the base station) due to a no line of sight (NLOS) environment. Therefore, in this work, 5G throughput performances were evaluates in an indoor train station by considering the effect of an NLOS environment caused by blockage by human bodies. In addition, to improve the robustness of TCP transmission in a high-RTT and high-packet-loss environment (e.g., an NLOS environment), a state-of-the-art TCP, TCP-FSO, was used. In the evaluations, the MATLAB 5G library was used to simulate the 5G environment, and a Linux software-based network emulator, Traffic Control, was used to emulate the 5G network. From the evaluations, it the 5G mobile throughput characteristics were confirmed in three different crowded patterns (low, middle, and high density), and the TCP-FSO advantage against CUBIC-TCP was validated.

Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.