Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same Industrial, Scientific, and Medical band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT , opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes’ priorities and reduce the delivery delay from high-priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi access point. We extensively evaluate ECT under different network settings, and results show that our ECT’s packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Password has become today's dominant method of authentication in social network. While the brute-force attack methods, such as HashCat and John the Ripper, are unpractical, the research then switches to the password guess. The state-of-the-art approaches, such as Markov Model and probabilistic context-free grammars(PCFG), are all based on statistical probability. These approaches have a low matching rate. The methods on neural network have been proved more accurate and practical for password guessing than traditional methods. However, a raw neural network model is not qualified for cross-sites attack since each data set has its own features. This paper proposes a general deep learning model for password guessing, called GENPass. GENPass can learn features from several data sets and ensure the output wordlist high accuracy in different data sets by using adversarial generation. The password generator of GENPass is PCFG+LSTM(PL), where LSTM is a kind of Recurrent Neural Network. We combine neural network with PCFG because we found people were used to set their passwords with meaningful strings. Compared with LSTM, PL increased the matching rate by 16%-30% in the cross-sites tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The result shows that the matching rate of GENPass is 20% higher than that of simply mixing those datasets in the cross-sites test.

Recent advances in cross-technology communication have significantly improved the spectrum efficiency in the same ISM band among heterogeneous wireless devices (e.g., WiFi and ZigBee). However, further performance improvement in the whole network is hampered because the cross-technology network layer is missing. As the first cross-technology network layer design, our work, named ECT, opens a promising direction for significantly reducing the packet delivery delay via collaborative and concurrent cross-technology communication between WiFi and ZigBee devices. Specifically, ECT can dynamically change the nodes' priorities and reduce the delivery delay from high priority nodes under unreliable links. The key idea of ECT is to leverage the concurrent transmission of important data and raw data from ZigBee nodes to the WiFi AP. We extensively evaluate ECT under different network settings and results show that our ECT's packet delivery delay is more than 29 times lower than the current state-of-the-art solution.

Researchers have demonstrated the feasibility of detecting human motion behind the wall with radio frequency (RF) sensing techniques. With these techniques, an eavesdropper can monitor people's behavior from outside of the room without the need to access the room. This introduces a severe privacy-leakage issue. To address this issue, we propose Aegis, an interference-negligible RF sensing shield that i) incapacitates the RF sensing of eavesdroppers that work at any unknown locations outside of the protected area; ii) has minimum interference to the ongoing WiFi communication; and iii) preserves authorized RF sensing inside the private region. Our extensive evaluation shows that when Aegis is activated, it i) has a negligible impact on the legitimate sensing system; ii) effectively prevents the illegitimate sensing system from sensing human motions. Moreover, the ongoing data communication throughput is even increased.

The exponentially increasing number of Internet-of-Thing (IoT) devices introduces a spectrum crisis in the shared ISM band. However, it also introduces opportunities for conducting radio frequency (RF) sensing using pervasively available signals generated by heterogeneous IoT devices. In this paper, we explore how to leverage the ambient wireless traffic that i) generated by uncontrollable IoT devices and ii sensed by ambient noise floor measurements (a widely available metric in IoT devices) for human gesture recognition. Specifically, we introduce our system EAR, which can conduct fine-grained human gesture recognition using coarse-grained measurements (i.e., noise floor) of ambient RF signals generated from uncontrollable signal sources. We conducted extensive evaluations in both residential and academic buildings. Experimental results show that although EAR uses coarse-grained noise floor measurements to sense the uncontrollable signal sources, the signal sources can be distinguished with an accuracy up to 99.76%. Moreover, EAR can recognize fine-grained human gestures with high accuracy even under extremely low traffic rate (i.e., 4%) from uncontrollable ambient signal sources.

Energy internet is now an industry hot spot which enables the interconnection and sharing of energy just like the Internet. Inspired by the concept of energy internet, this paper will focus on a designed energy distribution network, using city bus lines running Electric Vehicles (EV) to achieve electric power storage and transmission. This network is made of renewable energy sources providing power, charge stations for power exchange and bus lines as delivery, electric buses serving as the carriers of flowing power in network. This paper will mainly discuss and solve the problem of placing charge stations on city bus map to compose the network framework. Our work includes two optimization algorithms using some ideas of graph theory, simulating with real-world transporting data of different city maps and analyzing the results to evaluate efficiency as well as advantages and disadvantages on algorithms and data sets.

The exponentially increasing number of IoT devices makes the unlicensed industrial, scientific, and medical (ISM) radio bands (e.g., 2.4 GHz) extremely crowded. Currently, there is no efficient solution to coordinate the large amount heterogeneous IoT devices that have different communication technologies (e.g., WiFi and ZigBee). To fill this gap, in this paper, we introduce embedded multiple flows (EMF) communication method, which (i) embeds different pieces of information in existing traffic and (ii)concurrently sends out these information from one IoT sender to multiple IoT receivers that have a different communication technology from the sender. By doing this, our EMF method (i) enables cross-technology communication among heterogeneous IoT devices, (ii) does not introduce any extra control traffic, and (iii) is transparent to the higher layer applications. Our approach is implemented on USRPs and commercial off-the-shelf (COTS) ZigBee devices. We also conducted extensive experiments to evaluate our approach in real-world settings. The evaluation results show that EMF's throughput is more than 14 times higher than the latest cross-technology communication technique (i.e. FreeBee[1]).

As sensors spread across almost every industry, the Internet of Things (IoT) is going to trigger an era of big data. However, the abundance of available sensing data causes new challenges when building IoT applications. One main challenge is how to select proper data from large amount of sensing data for learning useful information efficiently. Existing approaches require developers to manage data for each specific application, which is very time consuming since the developers may not have enough knowledge about the dynamic changing data quality of different sensors. In this paper, we propose a data management middleware to learn the correlations between time series sensor data without prior knowledge. The learned correlation is then applied to select the useful sensor and reconstruct the incorrect data. To generalize the correlation models for each application, we utilize the dynamic feedback from the application to update the data selection and reconstruction. We evaluate our data management middleware in smart grids. The evaluation results show that our middleware can achieve better application performance with the help of dynamic feedback, data reconstruction and data selection.

Speech-based cognitive load modeling recently proposed in English have enabled objective, quantitative and unobtrusive evaluation of cognitive load without extra equipment. However, no evidence indicates that these techniques could be applied to speech data in other languages without modification. In this study, a modified Stroop Test and a Reading Span Task were conducted to collect speech data in English and Chinese respectively, from which twenty non-linguistic features were extracted to investigate whether they were language dependent. Some discriminating speech features were observed language dependent, which serves as an evidence that there is a necessity to adapt speech-based cognitive load detection techniques to diverse language contexts for a higher performance.

The emerging smart health and smart home applications require pervasive and non-intrusive human activity recognition and monitoring. Traditional technologies (e.g., using cameras or accelerometers and gyroscopes) may introduce privacy issues or require people to wear sensors. To address these issues, recent approaches exploit fine-grained wireless signals for activity recognition. However, these approaches require devices that are costly or need to provide unique wireless features (e.g., Doppler shifts or phase information). With the increasingly available Internet of Things (IoT) devices, in this paper, we propose Harmony, a human activity recognition and monitoring middleware which can utilize the coarse-grained (but pervasively available) received signal strength (RSS) measurements from the radios of IoT devices. We implement a complete evaluation platform (from data collection to data analysis) of the middleware on top of low cost ZigBee compliant MICAz nodes and a laptop. We also conducted extensive experiments. Our results show that our design can achieve similar accuracy as fine-grained WiFi channel state information (CSI) measurement-based approaches. Specifically, our overall human activities recognition accuracy is up to 74% and 90% for RSS readings from a single pair and 3 pairs of IoT devices, respectively.

This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.

This paper outlines the state-of-the-art of recently published papers on the subject of power aware design in various industrial applications. First, the basic concept of power aware design is given and then current trends are summarized. A summary is given of a wide variety of techniques and applications of power awareness.

This paper demonstrates the outline of the recently published literatures on the field of industrial control and gives an overview about the current trends of control issues in various industrial applications. Controllers can be divided into different categories according to different control algorithms and technologies, which includes PID algorithm, Kalman filter, least squares regression, network-based automation, fuzzy inference systems, neural networks, radial basis function networks and sliding-mode control and so on. A brief introduction will be given for every paper related to these control technologies and applications.

This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the training process. More accurate results can be obtained by increasing variable dimensions. Initial centers are chosen from training patterns and other parameters are generated randomly in limited range. Taking the advantages of fast convergence and powerful search ability of second order algorithms, the proposed ISO algorithm can normally reach smaller training/testing error with much less number of RBF units. During the computation process, quasi Hessian matrix and gradient vector are accumulated as the sum of related sub matrices and vectors, respectively. Only one Jacobian row is stored and used for multiplication, instead of the entire Jacobian matrix storage and multiplication. Memory reduction benefits the computation speed and allows the training of problems with basically unlimited number of patterns. Several practical discrete and continuous classification problems are applied to test the properties of the proposed ISO training algorithm.