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Due to their simple hardware, sensor nodes in IoT are vulnerable to attack, leading to data routing blockages or malicious tampering, which significantly disrupts secure data collection. An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection (APTAD) is proposed to collect integrated IoT data by recruiting Mobile Edge Users (MEUs). (a) An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes. (b) Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes. (c) The last, the number of active detection packets and detection paths are designed, so as to accurately identify the trust of nodes in IoT at the minimum cost of the network. A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33% respectively, while the accuracy of trust identification is improved by 20%.

The task of named entity recognition has been widely used in medical text analysis, but there is still the problem of poor transfer ability in practical applications. This work proposes a novel named entity recognition method based on a proposed knowledge distillation framework and Efficient GlobalPointer for Chinese biomedical and clinical data. Specifically, our study leverages the Efficienct GlobalPointer to address the issue of entity nesting and introduces a context shield window to mitigate interference from redundant information. Furthermore, the model’s generalization ability is enhanced through a novel knowledge distillation framework. The proposed knowledge distillation framework solves the problem of independent feature learning process in feature distillation by using linkage mechanism. The recognition accuracy is improved by the proposed knowledge distillation method while keeping the model complexity low, so that our method can meet the inference speed requirement in real applications while ensuring a certain recognition accuracy. Our method achieves excellent experimental results on three publicly available Chinese datasets, where the comprehensive evaluation metric F1 exceeds the best results achieved by existing methods.

Abstract With the development of the drying rack system, users with limited time tend to use the fully functional drying rack system to realize various intelligent control functions. However, the existing control methods for drying rack system has some defects such as, low intelligence and system delay, which are unsuitable for most users. In this paper, an efficient intelligent control algorithm based on the back‐propagation (BP) neural network is proposed. In this system, STM32F103 is first utilized as the central controller and multiple sensors are used to collect environmental information. Then, the remote control, voice keywords, and buttons can be used to achieve intelligent control. Subsequently, a motor drive intelligence control algorithm based on the BP neural network (MCBP) is proposed to improve the accuracy of the intelligent control. Next, an Application (APP) that can display environmental data such as wind speed, temperature, and humidity is developed. The APP can realize various intelligent control functions such as lifting, panning, rotating, and harvesting. Finally, MCBP is compared with normal control and programmable logic controller control. The accuracy of the MCBP is higher than other two control methods. The final extensive experiments confirm the accuracy and efficiency of the proposed intelligent control algorithm.

More and more sensor nodes which are deployed in smart cities for data collection and analysis form the ubiquitous urban Internet of Things. Energy saving and rapid data collection play a key role in environmental monitoring, military search, medical care, and general city management in smart cities. Therefore, it is a very meaningful and challenging work to design an energy efficient and fast routing protocol to collect data. Although the widely used duty cycle mechanism can effectively save energy, it brings greater delay to routing. Using wake-up radio enable nodes can effectively reduce the delay, but it will bring a large network deployment cost. So how to reduce the deployment cost and quickly route data is a challenge issue for sensor-based system. In this paper, Pre-Awake for Wake-up Radio Enable Sensor based system (PA-WuRES) routing protocol is proposed to reduce delay and deployment cost. The main contribution of our work are as follows: (a) A novel PA-WuRES protocol is proposed which requires much less WuR(WuR,Wake-up Radio) enable nodes, so its deployment cost is lower. (b) Using WuR enable nodes will increase a certain amount of energy consumption. Therefore, this paper proposes a differentiated service data routing mechanism to minimize energy consumption. (c) Full theoretical analysis and experimental results show that the proposed PA-WuRES protocol reduces the deployed WuR enable nodes by 80%, but sensitive-data routing delay is reduced by 48.4%.

Currently, large number of devices have been connected to the Internet of Things (IoT). Ubiquitous IoT devices encounter emergencies and generate much data which may lead to congestion and urgency to be processed timely. To alleviate it, many approaches have been proposed and Adaptive Duty Cycle Control (ADCC) scheme is an effective one. The main contributions of this paper are as follows: (a) Unlike previous studies that mostly the efficiency of congestion control and real-time processing is experiment-oriented, specially, this paper theoretically gives a targeted optimization of duty cycle ratio, which can effectively guide the design of ADCC scheme for real time big data. (b) A general design principle is proposed to guide the scheme designing in order to improve the efficiency and performance of networks. (c) A comprehensive congestion avoidance and real-time processing scheme combining dynamic duty cycle adjustment and full utilization of residual energy is proposed. Thus, these ideas can meet the concept of Green IoT. Through our extensively theoretical and experimental analysis, the principle can effectively guide the design of ADCC scheme, and can reduce the delay, data drop ratio by 20.95% − 77.85% and 29.63% − 100% respectively, without affecting network lifetime, compared with previous scheme.

This article anticipates the puzzle of acquiring a three-dimensional inhomogeneous site percolation on an irregular Bethe Lattice (TDIBL). In this work, we explore a TDIBL with the intent to realize the right tradeoff among different percolating variates, namely, cluster size distribution (CSD), critical occupation probability (COP), percolating probability (PP), and mean cluster size (MCS). The variates results are acquired using the generating function (GF) and generalized recursive approaches (GRA). The findings revealed that, for inhomogeneous site percolation (ISP) on the proposed model, the high fraction of probabilities (occupation and distribution probabilities) boosts the intensity of the process, which will enlarge the mean degree of the system in the percolation process. Moreover, numerical simulation and sensitivity analysis will also enhance our understanding of the percolating process via 2D and 3D. Their corresponding shape profiles of earned findings are drawn to perceive their dynamics amongst the options of entailed parameters. Furthermore, exhausting the above scheme, we discuss the transmission behavior of the novel corona-virus 2019 (COVID-19) and present particular disease-control schemes courtesy of groups with numerous infection probabilities and find the effect according to the age distribution. We recognize that this endeavor is timely, that it will be about curiosity, and that it will include scientists working with diverse percolation approaches.

The targeted compounds included Polychlorinated Biphenyls (PCBs), Pesticides (PESTs), Polycyclic Aromatic Hydrocarbons (PAHs) and so on in the Great Lakes Integrated Atmospheric Deposition Network (IADN), which is a platform based on the IoT (Internet of Things) technology to collect environmental pollutants data. While previous studies usually employed traditional statistical approaches to analyze the IADN results, we performed a complete modeling workflow of the total concentrations of PCBs, PESTs, and PAHs (which is referred to as Σ PCBs, Σ PEST s and Σ PAHs orderly) in 1990-2016 samples by using a machine learning algorithm combined with data-driven research method, which lets the model fit the data, so as to change the model to achieve the effect. The main results of this article are as follows, 1) identifying the spatial and temporal trends of POPs (Persistent Organic Pollutants) in the air of the Great Lakes; 2) An appropriate data-driven intelligent model was constructed for the data at EH (Eagle Harbor) and STP(Sturgeon Point) sampling sites, via which we estimated their Σ PCBs, Σ PESTs, and Σ PAHs in the following 4-5 years, showing the concentrations will continue declining with slight fluctuations; 3) The important role which IoT played in smart environmental protection was pointed out.

Mobile computing technology will enable computers or other information intelligent terminal devices to enable data transmission and resource sharing in a wireless environment. In industrial or disaster situations, the destruction of sensor nodes is unpredictable, and some urgent data, such as information about damage points in industrial equipment, needs to be immediately transmitted to decision makers. Traditional data collection protocols, such as Growth Codes, cannot distinguish the importance of data. This behavior increases the risk of losing important data. In this article, we analyze the impact of the Growth Codes on the “fair” processing of all data. From the perspective of distinguishing the importance of data, a real-time data collection model (RTDCM) is proposed. This model enables important data to capture the coding opportunities of common data, increase the proportion of code words with important data in the overall code word of the network, and layer the network, so that the data transmission has a certain purpose, and ultimately improve the decoding efficiency of important data. According to different preemption methods, two RTDCM-based protocols are designed. Experiments show that the data recovery characteristics of RTDCM-based data acquisition protocols are mainly concentrated on important data, and have high recovery efficiency for ordinary data.

Digital watermarking is an effective covert communication technique in the military field, which can solve the problems in traditional encryption. In general, the Spread Spectrum (SS) algorithm can well satisfy the requirements of concealment and anti-interference in the covert communication process. However, it has a natural defect in resisting desynchronization attacks. In this paper, we propose a Robust Feature Points Scheme (RFPS) against desynchronization attacks by calculating the maximum response value of the second-order derivative of the original audio signal. The maximum response value will be used as a feature point and the audio segments centering at the detected feature points are extracted for both watermark embedding and extraction process. This study mainly focuses on cropping attacks and pitch invariant Time-Scale Modification (TSM) attacks. The experimental results show that under non-uniform cropping attacks, the proposed method can extract the watermark information without any bit error rate. Even in the case of the pitch-invariant TSM attacks, the proposed scheme has great improvement compared with the existing works.

The internal mechanism of the social “earthquakes”- the wars burst in human society, is investigated by nonlinear theory. We explore the scaling behavior in the human activity and the historical evolution of ancient China. Based on the war chronology and history chronology from B.C.2000 to A.D.1840, using statistical analysis together with the detrended fluctuation analysis, we find that the social evolution system shows self-organized critical behavior and there exists self-similar scaling behavior during the evolution system. Based on time-delay reconstructed phase space, a modified radial basis function neural network (modified RBF-NN) is used to predict the number of wars and the transformation of the historical stage. It shows that the prediction result matches quite well with the real value for the following two historical stages. Furthermore, by singular value decomposition and the sparse identification algorithm in time-delay coordinates, the coefficients of the candidate functions in identified system can be solved from a sparse regression problem. We extracted the governing equations of big events burst in social system. The identified system fits well with the real data, and it can capture the same topology with the original system of wars. In this paper, we investigated the wars burst in social system by statistic analysis, neural network as well as sparse identification. It sheds light on the study of wars or conflicts from perspective of data science and dynamical theory.

In a traditional multistate quickest path problem (MQPP), the system reliability is evaluated based on a strict assumption that the net flow into and out of a system is equal to zero. However, certain networks, which are known as deteriorated networks, suffer a loss due to the deterioration effect, resulting a delivery shortage. For example, the data or goods will deteriorate or decay because the transmission distance is too long, which affects whether the delivered data or goods arrive intact. To provide a practical solution to this problem, a novel MQPP model, known as the deteriorated MQPP (MQPPde) model, is proposed in this work. The goal is to evaluate the system reliability, which is defined as the probability that the end user receives at least d units of data or goods in transmission time T in the case of a MQPP with the deterioration effect. A simple path-based algorithm based on an integer programming model of the flow conservation law is presented to generate all of the lower boundary points (d, T)-MPdes. Next, the reliability of the MQPPde model can be calculated in terms of all of the (d, T)-MPdes.

Wireless (smart) sensor networks (WSNs) comprise a myriad of embedded wireless smart sensors. They play a cardinal role in the functioning of many applications, such as the Internet of Things, smart grids, smart production systems, and smart homes, which ultimately render them paramount instruments in the modern age. Recent advances in WSNs have resulted in the rapid development of sensors. However, WSNs will only able to achieve better execution efficiencies if their energy consumption - owing to limited battery life and difficulty of recharging - can be better controlled. Moreover, signal transmission quality determines WSN performance. Hence, two main concerns - energy consumption and signal transmission quality - should be addressed to improve the performance of WSNs. Thus, a new bi-objective simplified swarm optimization algorithm (bSSO) is proposed by employing the concepts of simple routing, SSO, and crowd distance. The performance and applicability of the proposed bSSO using eight different parameter settings are demonstrated through an experiment involving ten WSN benchmarks ranging from 100 to 1000 sensors. The proposed algorithm is then compared with NSGA-II, which is an algorithm widely used to solve multi-objective problems. The results show that the proposed bSSO can successfully achieve the aim of this work.

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.

The correct optimization of the solar cell electrical-model parameters is the key to produce better and more realistic P-V characteristics. This helps prosumers to select solar panels having better comparative efficiency, which in turn increases electricity production. Evolutionary Algorithms have shown comparatively good results in the estimation of these parameters. The Photon-current, Diode dark saturation current, Series resistance, Shunt resistance and Diode ideality factor, constitute the single diode's unknown electrical model parameters. The mathematical-model of the P-V cell is derived in terms of Series-resistance and Diode-ideality factor. These two parameters are then used in a 2-variable single objective function. Using this derived model, Genetic Algorithm and Numerical method, a new parameter estimation technique has been proposed. Making use of machine learning and combination of two algorithms highlights the usefulness of the intended hybrid technique. P-V characteristic and relative maximum power point error of different solar cells, have been compared. The relative analysis disclosed that the proposed method offers more pragmatic P-V characteristics, as compared to the existing methods.

Acyclic (binary-state) networks are commonly implemented in many diverse disciplines and applications, including information systems, processes management, project management. These networks owe their popularity to the fact that they do not use directed cycles. Network reliability is the most commonly used tool for evaluating and managing systems modeled on acyclic networks, and minimal paths (MPs) play a significant role in evaluating this reliability. This study therefore proposes a new algorithm based on a novel convolution concept for evaluating acyclic network reliability. The proposed algorithm is able to find all convolution-based MP sets within polynomial time, and then obtain the symbolic function of the acyclic network reliability in terms of those convolution-based MP sets based on the pivotal decomposition. Its total time complexity is $O(2^{n})$ , which is the best among all existing MP algorithms which are at least $O(2^{\vert P\vert })$ , where $O(\vert P\vert) = O(2^{n})$ , and $n$ and $\vert P\vert $ are the number of nodes and MPs, respectively. The correctness and time complexity of the proposed algorithm is proven and examined. An example is used to display the novel convolution-based algorithm is implemented to solve the acyclic network reliability problem.

Cell free biocatalysis is showing promise as a replacement or complement to conventional microbial biocatalysts due to the potential for achieving high yields, titers, and productivities. However, there exist several challenges that need to be addressed before its broader industrial adoption is achieved. New paradigms and innovative solutions are needed to overcome these challenges. In this study we demonstrate high levels of glycerol conversion to 1,3-propanediol using a self-assembling metabolic pathway leveraging the arraying strategy (protein scaffolds) used by thermophilic cellulolytic bacteria to assemble their biomass degrading enzymes. These synthetic metabolons were capable of producing 1,3-PDO at a yield more than 95% at lower glycerol concentration and close to 70% at higher concentrations at a higher productivity rate than the equivalent microbial strain. One of the benefits of our approach is the fact that no enzyme purification is required, and that the assembly of the complex is accomplished in vivo before immobilization, while product formation is conducted in vitro. We also report the recovery of enzymatic activity upon fusion enzymes binding to these protein scaffolds, which could have broader applications when assembling arrayed protein complexes.

Wireless sensor networks are usually deployed in harsh and emergency scenarios, such as floods, fires, or earthquakes, where human participation to monitor and collect environmental data may be too dangerous. It can be also used for healthcare in extreme and remote environments. In such an environment, sensor nodes are faced with the risk of failure and the loss of valuable healthcare data. Therefore, fast collection and reliable storage of data becomes the two important basic topics for reliable data collection. Traditional distributed data collection protocols based on the network, such as Growth Codes proposed by Karma et al., have improved the persistence of data and the efficiency of reliable data collection in disaster scenarios. However, there are still some problems that reduce the overall efficiency. In this paper, we analyze the factors that affect the collection efficiency from a new perspective, the ratio of redundant symbols. Random feedback digestion (RFDG) model is proposed to digest the redundant symbols, similiar to our stomach digesting food, to remove redundant symbols and reduce resource consumption by using the feedback information of the already decoded code words sent by the sink node. This model can increase the valid information ratio in the network and finally increase data decoding efficiency. Three protocols are proposed in this paper according to different feedback mechanisms based on RFDG. It is shown that protocols based on RFDG outperform the growth codes protocol in data collection efficiency and reduce the delayed effect.

Due to the huge number of objects/things connected to the Internet of Things (IoT) which are embedded with electronics, software, and sensors, the IoT creates many exciting applications such as smart grids, smart homes, and smart cities. In the IoT, the sensing and control of objects/things can be abstracted as a task, in which many sensing devices sense and collect data. However, the substantial case studies show that by simply connecting them without further collaboration among the objects/things will lead to the bad performance of the system. With the number of sensing devices connected to the IoT increases, the collaboration for completing the task is becoming more and more urgent. In this paper, a Collaborative Multi-Tasks Data Collection Scheme (CMDCS) is proposed to solve the problem by constructing a collaborative platform for task publisher and data reporter. The main contribution of CMDCS includes the following two aspects: (1) a Task Unit Bid-based task selection strategy is proposed to select the task which can bring higher profits to the system, in which the Task Unit Bid is the ratio of task bid to the amount of data which are needed to collect sensing tasks; and (2) a greedy contributed density-based data collector set selection method is proposed to reduce the cost of data collection so as to maximize system profit, in which the contribution density is used to measure the contribution of a single data collector to a specific sensing task. A large number of experiments have been carried out to verify the effectiveness of our proposed strategy. The experiments show that compared to the traditional data collection strategy Random Task selection with Coverage First Reporter selection, in which the Task Unit Bid and Contribution Density are not used, the profit of the system is improved by 92.08%.

The rapid development of big data and cloud computing technologies greatly accelerate the spreading and utilization of images and videos. The copyright protection for images and videos is becoming increasingly serious. In this paper, we proposed the robust non-blind watermarking schemes in YCbCr color space based on channel coding. The source watermark image is encoded and singular value decomposed. Subsequently, the singular value matrixes are embedded into the Y, Cb, and Cr components of the host image after four-level discrete wavelet transform (DWT). The embedding factor for each component is calculated based on the just-noticeable distortion and the singular vectors of HL subband of DWT. The peak signal-to-noise ratio of the watermarked image and the normalized correlation coefficient of the extracted watermark are investigated. It is shown that the proposed channel coding-based schemes can achieve near exact watermark recovery against all kinds of attacks. Considering both robustness and transparency, the convolutional code-based additive embedding scheme is optimal, which can also achieve good performance for video watermarking after extension.

The encrypted image retrieval in cloud computing is a key technology to realize the massive images of storage and management and images safety. In this paper, a novel feature extraction method for encrypted image retrieval is proposed. First, the improved Harris algorithm is used to extract the image features. Next, the Speeded-Up Robust Features algorithm and the Bag of Words model are applied to generate the feature vectors of each image. Then, Local Sensitive Hash algorithm is applied to construct the searchable index for the feature vectors. The chaotic encryption scheme is utilized to protect images and indexes security. Finally, secure similarity search is executed on the cloud server. The experimental results show that compared with the existing encryption retrieval schemes, the proposed retrieval scheme not only reduces the time consumption but also improves the image retrieval accuracy.

Advanced communications and networks greatly enhance the user experience and have a major impact on all aspects of people’s lifestyles. Widely deployed sensor nodes provide support for these services. However, although energy harvesting and transfer technology provides a solution to allow the long-term survival of wireless sensor nodes for wireless sensor networks, the single collection scheme causes a lot of energy waste. Thus, efficient energy utilization and fast data collection are still serious challenges for energy harvesting wireless sensor networks. To overcome these challenges, an adaptive collection scheme based on matrix completion (ACMC) is proposed to reduce delay and to improve the energy utilization of the network. In the ACMC scheme, compared with traditional data collection schemes, the data collection schemes vary with the available energy, collecting large amounts of data when the available energy is sufficient to obtain high-quality data-based applications. Otherwise, adaptive selecting the collected data based on previously collected data, the amount of data collected can be effectively reduced based on the application requirements, thereby improving the energy utilization of the network. The ACMC scheme also proposes a method for reducing the delay by increasing the duty cycle of the nodes that are far from the CC. At the same time, the transmission reliability of these nodes increases due to the increase in the transmission frequency. Thus, the ACMC scheme can also further reduce the delay of the network. The experimental results of the ACMC scheme in planar networks show better performance than the traditional data collection schemes and can improve the energy utilization of the network by 4.26%–6.68% while reducing the maximum delay by 9.4%.

With the development of software-defined network (SDN) technology, sensor nodes can update soft codes to enable themselves to have new functions so as to make wireless sensor network (WSN) full of new vitality. However, how to spread the new code to every node in the network quickly and energy-efficient is a challengeable issue. In this paper, a Two-hop Neighborhood Information joint Double Broadcast Radius (TNI-DBR) scheme is proposed to disseminate the codes in duty cycle-based WSNs in a fast and energy-efficient style. The main innovations of this paper are as follows. The TNI-DBR scheme makes full use of the unbalanced energy consumption of the sensor network in the process of data collection and doubles the broadcast radius in the area with redundant energy so that more nodes can receive the new code in one broadcast and the broadcast range is further, which can reduce the delay of code dissemination effectively. Different from the conventional code dissemination schemes that select broadcasting nodes according to the information of the one-hop neighbors, in the TNI-DBR scheme, an $O(n)$ two-hop neighbor information exchange algorithm is proposed to obtain the duty cycle information of the two-hop neighbors. The TNI-DBR scheme selects the best broadcasting nodes based on information in the range of two-hop neighbors to enlarge the number of active nodes and reduce the time required for code dissemination. Sufficient theoretical analysis and experimental results show that the TNI-DBR scheme can significantly optimize the performance of code dissemination. Compared to previous schemes, the delay of code dissemination can be reduced by 43.09%~78.69%, the number of broadcasts can be reduced by 44.51%~86.18%, and the energy utilization ratio is improved by about 24.5%.

With the continuous development of the Chinese economy and the gradual acceleration of urbanization, it has caused tremendous damage to the environment. The bad air environment seriously damages the physical and mental health of the people. The change in smog concentration will be affected by many realistic factors and exhibit nonlinear characteristics. The method proposed in this paper is to use the Internet of Things (IoT) technology to monitor the acquired data, process the data, and predict the next data using a neural network. The existing prediction models have limitations. They don't accurately capture the law between the concentration of haze and the factors affecting reality. It is difficult to accurately predict the nonlinear smog data. One algorithm proposed in this paper is a two-layer model prediction algorithm based on Long Short Term Memory Neural Network and Gated Recurrent Unit (LSTM&GRU). We set a double-layer Recurrent Neural Network to predict the PM2.5 value. This model is an improvement and enhancement of the existing prediction method Long Short Term Memory (LSTM). The experiment integrates data monitored by the IoT node and information released by the national environmental protection department. First, the data of 96 consecutive hours in four cities were selected as the experimental samples. The experimental results are close to the true value. Then, we selected daily smog data from 2014/1/1 to 2018/1/1 as a train and test dataset. It contains smog data for 74 city sites. The first 70% of the data was used for training and the rest for testing. The results of this experiment show that our model can play a better prediction.

The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well.

Wireless sensor network are widely used in various types of environmental monitoring and there are also many applications in the industrial field, we usually deploy sensor nodes in the corresponding areas to monitor and collect relevant data, waiting for a sink node to collect the stored data for further analysis and decision. But in some extremely harsh scenarios, the living environment of the sensor node is very bad, hence the data on the nodes will always lose. Generally, we use LT Codes to improve data persistence. However, some difficult problems with regard to the efficiency of coding and decoding of data can not be resolved by traditional solutions. In terms of the above problems, this paper improves the traditional coding method by reducing the number of encoded packets, so that the network congestion can be alleviated under the premise of successful data coding. What's more, we propose an edge layered collection model and a new coding strategy Edge Layering Fountain Codes (ELFC) based on the model, each sensor node inside the network sends its data to the edge nodes which are relatively safe to ensure the secure collection of data and allocate the resource consumption of the sensor nodes around the sink node. On this basis, the Ordered Edge Layering Fountain Codes (OELFC) is further proposed. The OELFC can collect data packets in a low degree-to-high degree manner, which greatly improves the efficiency of data decoding.

With the development of mobile Internet, it is changing the way we communicate with others. Internet media have gradually become the main mobile crowdsourcing applications for information dissemination and user communication, including online news and social networks. However, the potential business opportunities have stimulated the emergence of a large number of spammers, who release false speech, advertisements, pornographic contents, and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users. Therefore, in order to reduce the harm of false information, the research on the identification technology of spammers has been carried out extensively. However, the traditional technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news. In this paper, we propose an effective technology of identifying online news comment spammers based on the label propagation algorithm (LPA), making full use of the user comment behaviors and contents. First of all, we collect a large amount of news and comments from NetEase News and label some users in the data as spammers or normal users manually to construct a labeled dataset. Then, a set of behavioral and semantic features are extracted and quantified from the user comment behaviors and comment contents by statistical analysis. Next, we propose the identification technology based on the LPA. Finally, the set of feature values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine the most effective combination of features and improve the technology. The results show that the technology proposed in this paper involves a lower data cost but a better identification effect than some traditional technologies based on the supervised classifier.

The technology of software-defined devices has great prospect in updating the programs of the sensing devices to enable the devices to adapt to the new environments and applications. However, to effectively spread the code to each node in the software-defined-based wireless sensor networks (WSNs) is still a challenging task. In this paper, a proportion to duty cycle length-based broadcast (PDLB) scheme is proposed to disseminate code in duty cycle-based WSNs in a fast and energy-efficient style. In PDLB scheme, the sink first initiates a code broadcast. Then, the nodes that have received code start counting and calculate the priority of broadcasting based on an improved counter-based priority broadcasting approach proposed in this paper. Next, the nodes with the highest priority select k slots to broadcast according to the length of duty cycle so that the code can be spread from the sink to the network periphery. Finally, the nodes that have not obtained the code wait for a period of timeout to broadcast to actively send code request; after receiving code request, the node that obtained the code initiates a code broadcast to cause the code requester to obtain the code. The PDLB scheme has been validated by comprehensive experiments that show the following regularities. The smaller the count threshold value Ch, the larger the number of slots selected for broadcasting that can guarantee a certain coverage ratio, while the code dissemination speed slows down at the same time. The longer the duty cycle is, the more the broadcast time is required, and the larger the transmission delay is. For each slot added in a working cycle, the number of broadcasts is increased by 1.54%-33.33% and the transmission delay is augmented by 2.27%-70.04%. The higher the node density is, the shorter the code spread speed is. As the network density increases, the transmission delay is reduced by 0.83%-65.87%. When the number of broadcasting slots is half of the total number of slots in a work cycle, the energy consumption reaches the lowest value.

Vehicular ad hoc networks (VANETs) in which vehicles act as the mobile nodes provide a wide variety of services, such as audio and video surveillance. However, such networks suffer from an important problem of service delivery reliability as the network performance degrades significantly in the presence of misbehaving vehicles. Most of the existing works either assume that vehicles' misbehaviors are constant or ignore heterogeneous traffic for multiservice (a mix of video, voice, and data traffic). In this paper, we investigate the problem of trust-based multiservice delivery via integrated misbehavior detection and tolerance for fault-aware VANETs. To model the effects of time-varying misbehaviors, modern fog computing could help to analyze and to store related data in VANETs while evaluating the dynamic trust weights based on attribute parameters of each vehicle. Then, we incorporate these weights into our service delivery framework that integrates trustworthy vehicle selection for misbehavior detection and uses differential resource allocation to achieve misbehavior tolerance. We present a multi-path selection criterion based on the trust evaluation and design a trust-aware heterogeneous traffic allocation algorithm over multiple routing paths. Finally, our scheme is evaluated using extensive simulations where we show that (i) approximately 12% - 40% higher successful service delivery can be achieved by our routing algorithm at the expense of acceptable delay loss compared to other three routing protocols; (ii) our trust-aware traffic allocation algorithm can gain 10% - 20% higher effective network utility and better fairness than that of the baseline solution; and (iii) the integrated scheme significantly improves both effective utility ratio and the path-usage proportion by comparing it with only routing scheme and only traffic allocation scheme.

Blockchain technology has a wide range of applications in the fields of finance, credit reporting and intellectual property, etc. As the core of blockchain, consensus algorithm affects the security and performance of blockchain system directly. In the past 10 years, there have been about 30 consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Ripple Protocol Consensus Algorithm (RPCA) and AlgoRand. But their security, stability and operating efficiency still lag far behind our actual needs. This paper introduces the computing power competition of PoW into DPoS to design an improved consensus algorithm named Delegated Proof of Stake with Downgrade (DDPoS). Through the further modification, the impact of both computing resources and stakes on generating blocks is reduced to achieve higher efficiency, fairness, and decentralization in consensus process. Then a downgrade mechanism is proposed to quickly replace the malicious nodes to improve the security. The simulation experiments in blockchain system show that the proposed consensus algorithm is significantly more efficient than PoW and PoS, but slightly lower than DPoS. However, its degree of centralization remains far below that of DPoS. And through the downgrade mechanism, the proposed consensus algorithm can detect and downgrade the malicious nodes timely to ensure the security and good operation of system.

Urban computing creates novel solutions for smart city systems by integrating with ubiquitous sensing, cooperative communication, and data/code collection or dissemination technologies. In this paper, a collaborative code dissemination (CCD) approach with two-way vehicle to everything (V2X) paradigm is proposed by using vehicles act as code mules to disseminate code with fast and low cost style for urban Computing. The main innovation of the CCD-V2X scheme, which is different from the previous code dissemination method, is the use of three cooperative communication methods to speed up the dissemination of code. They are Roadside Smart Devices (RSDs) to vehicle (R2V), vehicle to vehicle (V2V) and R2V combined V2V (R2V-V2V) communications method. The corresponding three code dissemination schemes are proposed as follow: (1) The Code Dissemination with Roadside Smart Device Assisted (short as CDRSDA) scheme is proposed for vehicles to fast disseminate code in which the vehicles can receive code from RSDs that have get update code. (2) A Code Dissemination with V2V Transmission Assisted (short as CDVVA) scheme is proposed to improve the performance of code dissemination by enabling the transmission from code mules that have update code to the code mules that have no update code. (3) The R2V combined V2V (R2V-V2V) scheme is proposed which can further improve the performance by using both R2V and V2V communication method. The CCD scheme has been extensively experimentally verified by using real vehicle trajectory data. The experimental results show that: Compared with the Traditional Scheme, the CDRSDA scheme, CDVVA scheme, R2V-V2V scheme can be increased by up to 35.42%, 35.42%, 34.72%, respectively from the code diffusion coverage performance index, and from the speed performance indicators, the three schemes increased by at least 68.4%, 89.3% and 90.3%.

Interest is an important concept in psychology and pedagogy and is widely studied in many fields. Especially in recent years, the widespread use of many interest-based recommendation systems has greatly promoted research on interest modeling and mining on social networks. However, the existing studies have rarely tried to explore the relationships among interests and their application value, and most similar studies analyze user behavior data. In this paper, we propose and verify two hypotheses about the interests of social network users. We then use association rules to mine users' interests from LinkedIn users' profiles. Finally, based on the interest association rules and user interest distribution on Twitter, we design an approach to mine interests for Twitter users and conduct two experiments to systematically demonstrate the approach's effectiveness. According to our research, we found that there are a large number of association rules between human interests. These rules play a considerable role in our method of interest mining. Our research work not only provides new ideas for interest mining but also reveals the internal relationship between interest and its application value. The research work has certain theoretical and practical value.

Microbial conversion of biomass relies on a complex combination of enzyme systems promoting synergy to overcome biomass recalcitrance. Some thermophilic bacteria have been shown to exhibit particularly high levels of cellulolytic activity, making them of particular interest for biomass conversion. These bacteria use varying combinations of CAZymes that vary in complexity from a single catalytic domain to large multi-modular and multi-functional architectures to deconstruct biomass. Since the discovery of CelA from Caldicellulosiruptor bescii which was identified as one of the most active cellulase so far identified, the search for efficient multi-modular and multi-functional CAZymes has intensified. One of these candidates, GuxA (previously Acel_0615), was recently shown to exhibit synergy with other CAZymes in C. bescii, leading to a dramatic increase in growth on biomass when expressed in this host. GuxA is a multi-modular and multi-functional enzyme from Acidothermus cellulolyticus whose catalytic domains include a xylanase/endoglucanase GH12 and an exoglucanase GH6, representing a unique combination of these two glycoside hydrolase families in a single CAZyme. These attributes make GuxA of particular interest as a potential candidate for thermophilic industrial enzyme preparations. Here, we present a more complete characterization of GuxA to understand the mechanism of its activity and substrate specificity. In addition, we demonstrate that GuxA exhibits high levels of synergism with E1, a companion endoglucanase from A. cellulolyticus. We also present a crystal structure of one of the GuxA domains and dissect the structural features that might contribute to its thermotolerance.

Visual identification of cattle in the wild provides an essential way for real-time cattle monitoring applicable to precision livestock farming. Chinese Simmental exhibit a yellow or brown coat with individually characteristic white stripes or spots, which makes a biometric identifier for identification possible. This work employed the observable biometric characteristics to perform cattle identification with an image from any viewpoint. We propose multi-center agent loss to jointly supervise the learning of DCNNs by SoftMax with multiple centers and the agent triplet. We reformulated SoftMax with multiple centers to reduce intra-class variance by offering more centers for feature clustering. Then, we utilized the agent triplet, which consisted of the features and the agents, to enforce separation among different classes. As there are no datasets for the identification of cattle with multi-view images, we created CNSID100, consisting of 11,635 images from 100 Chinese Simmental identities. Our proposed loss was comprehensively compared with several well-known losses on CNSID100 and OpenCows2020 and analyzed in an engineering application in the farming environment. It was encouraging to find that our approach outperformed the state-of-the-art models on the datasets above. The engineering application demonstrated that our pipeline with detection and recognition is promising for continuous cattle identification in real livestock farming scenarios.

Abstract As increasing numbers of broadly neutralizing monoclonal antibodies (mAbs) against HIV-1 enter clinical trials, it is becoming evident that combinations of mAbs are necessary to block infection by the diverse array of globally circulating HIV-1 strains and to limit the emergence of resistant viruses. Multi-specific antibodies, in which two or more HIV-1 entry-targeting moieties are engineered into a single molecule, have expanded rapidly in recent years and offer an attractive solution that can improve neutralization breadth and erect a higher barrier against viral resistance. In some unique cases, multi-specific HIV-1 antibodies have demonstrated vastly improved antiviral potency due to increased avidity or enhanced spatiotemporal functional activity. This review will describe the recent advancements in the HIV-1 field in engineering monoclonal, bispecific and trispecific antibodies with enhanced breadth and potency against HIV-1. A case study will also be presented as an example of the developmental challenges these multi-specific antibodies may face on their path to the clinic. The tremendous potential of multi-specific antibodies against the HIV-1 epidemic is readily evident. Creativity in their discovery and engineering, and acumen during their development, will be the true determinant of their success in reducing HIV-1 infection and disease.

Abstract Color information plays an important role in the color image segmentation and real-time color sensor, which affects the result of video image segmentation and correct real-time temperature value. In this paper, a novel real-time color image segmentation method is proposed, which is based on color similarity in RGB color space. According to the color and luminance information in RGB color space, the dominant color is determined at first, and then color similarity can be calculated with the proposed calculation method of color component, which creates a color-class map. Next, the information of the corresponding color-class map is utilized to classify the pixels. Due to the characteristic that thermal inks feature color values that change in real time as the temperature changes, the segmentation results of thermal ink can be used as a real-time color sensor. Then, we also propose a method of color correction and light source compensation for the sake of potential inaccuracy of its measures. We discuss the proposed segmentation method application combining with color sensor (thermal ink) in real-time color image segmentation for Cyber physical system (CPS) by the application in fire detection and summarize a new method in identifying fire in a video based on these characteristics. The experiments showed that the proposed method in vision-based fire detection and identification in videos was effective; the results were accurate and can be used in real-time analysis.

The education plays a more and more important role in disseminating knowledge because of the explosive growth of knowledge. As one kind of carrier delivering knowledge, image also presents an explosive growth trend and plays an increasingly important role in education, medical, advertising, entertainment, and so on. Aiming at the long time of massive image feature extraction in the construction of smart campus, the traditional Harris corner has problems, such as low detection efficiency and many non-maximal pseudocorner points. This paper proposes a Harris image matching method that combines adaptive threshold and random sample consensus (RANSAC). First, the Harris feature points are selected based on the adaptive threshold and the Forstner algorithm in this method. On the one hand, candidate points are filtered based on the adaptive threshold. On the other hand, the Forstner algorithm is used to further select the corner points. Second, the normalized cross correlation matching and the RANSAC are applied to precisely match the detected Harris corners. The experimental results show that compared with the existing algorithms, the proposed method not only obtains a matching accuracy higher than 20% of Cui’s algorithm but also saves more than 30% detection time of corner detection and image matching. Furthermore, the proposed method obtains a matching accuracy higher than 50% of the Cui’s algorithm and saves more than 50% detection time of corner detection and image matching.

With the emergence of Internet of Things, the number of connected devices has been dramatically increasing, causing severe spectrum shortage problem. To fully explore the spectrum resources, big data, and cloud computing can be employed by cognitive radio networks, to make efficient use of various sensing results from different sensing sources. However, the massive growth of sensing data brings tremendous load pressure on the data center, resulting in long service response time and poor Quality of Experience. Edge computing and fog computing deal with these issues by placing computation resources at the network edge. However, compared with the data center, the capabilities at edge servers are limited. Therefore, a services routing-based caching scheme (SRCS) is proposed, which can greatly lighten the load on the data center and maintain the advantages of global intelligent computing of traditional cloud computing. Specifically, SRCS first introduces the concept of transmitting service flow. At the edge layer, data are converted to service flow by network hardware and software, thus achieving the network architecture centered on service computing. Then, SRCS proposes a service routing based on service similarity, transmits similar services through the same path, and service data are fused on the path to minimize transmission load. Moreover, SRCS caches services in content routers (CRs). When the service is requested again, CRs are used as service providers to return data, thus achieving the nearest access to the content. Both theoretical analysis and experiment results demonstrate that comparing existing schemes, SRCS improves service response time by 13.67%–51.15%, reduces transmitting data amount by 23.62%–30.3%, and makes energy consumption more balanced.

Least mean squares (LMS) adaptive algorithms are attractive for distributed environment parameter estimation problems in a smart city due to the benefits of cooperation, adaptation, and rapid convergence. To obtain a reliable estimate of the network-wide parameter vector, local results can be further fused by intermediate agents in a distributed incremental way. In this paper, we propose an intelligent variable step size incremental LMS (VSS-ILMS) algorithm to solve the dilemma between fast convergence rate and low mean-square deviation (MSD) in conventional incremental LMS (ILMS) algorithms. The main idea behind our proposal is that the local step-size is adaptively updated by minimizing the MSD in every iteration, where Tikhonov regularization and time-averaging estimation methods are adopted. A theoretical analysis of proposed algorithm is presented in terms of mean square performance and mean step size in a closed form. Simulation results show that VSS-ILMS algorithm outperforms the constant step size ILMS algorithm and several classical variable step-size LMS algorithms. The derived theoretical results shows good agreement with those based on simulated data. For a practical consideration, the proposed algorithm is also verified by the model of target localization in sensor networks.

The smart campus can monitor students in real time by analyzing students' images, but a large number of images bring an unbearable burden to the smart campus. The convenience of cloud computing has attracted smart campus to outsource their huge amount of data to cloud servers. Although the outsourcing of data can reduce the computational and storage burden on smart campus, the privacy preserving becomes the biggest concern. This issue has attracted many researchers to study the protection of outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the local binary pattern (LBP) feature over huge encrypted images. The image owner uploads the encrypted version of images to the cloud. The cloud server takes the responsibility of extracting the LBP features from encrypted images for various applications. In the encryption process, an image is divided into non-overlapping blocks at first, and the blocks are shuffled to protect the image content. Next, all the non-center pixels in each block are shuffled. Finally, the pixels are encrypted by splitting the original image data randomly. When such an encrypted image is received, the cloud servers can calculate the LBP features by secure multiparty computation. The extracted features can be applied to many applications, such as texture classification, image retrieval, face recognition, and so on.

The cyber-physical-social (CPS) computing and networking is a human centric and holistic computing framework which needs to convert the low-level data of physical, cyber, and social worlds into higher level information which can provide insights and help humans make complex decisions. Here, we focus on human fishing behavior recognition for vessel monitoring systems (VMS), an application of CPS. And the recognition of fishing behavior is the key task for studying human fishing activities, monitoring illegal fishing, and protecting fishery resources. However, VMS data basically consist of sequentially recorded position information and do not directly indicate whether a fisherman is fishing or not; thus, converting these low-level CPS data into intuitive information to humans is the primary task. In this paper, an identification model based on multi-step clustering algorithm (MSC-FBI) is proposed to automatically learn and discover fishing behaviors at sea. First, a temporal-spatial distance model is established; then, an improved multi-step clustering algorithm is used to identify human fishing behaviors, and finally, the patterns of different behaviors are extracted from the trajectory, and the unsupervised behavior learning model is established. Using this method, many experiments on different fishing trajectory data were implemented and compared with a traditional identification method based on the Gaussian mixture model (GMM-FBI). The experimental results illustrate the proposed model's superior performance.

Vehicular ad-hoc networks, as one of the major components of Internet of Things in smart cities, have enormous idle transportation capability due to the huge number of vehicles. This paper argues that it is feasible to employ a large number of mobile vehicles in smart city to construct a large-scale low-cost delivery infrastructure using opportunistic routing to improve the city's social welfare. To this end, a low-cost good delivery system (LCGDS) with opportunistic relay is proposed to meet the requirements of good delivery. In LCGDS, an enormous number of mobile vehicles constitute a stable, highly reliable, low-cost infrastructure with large transportation capabilities, called the mobile Internet of mission-critical things (M-IoMCT). The main contributions of the LCGDS are: 1) demonstrate the technological feasibility of M-IoMCT for delivering goods through mobile vehicles in a “ride-sharing” fashion with low costs and 2) in order to increase the reliability of goods transmission, the LCGDS proposes mobile vehicle selection algorithm which effectively selects a vehicle with higher probability of arriving at the destination. Both theoretical and experimental simulation results show that the performance of the LCGDS outperforms those of other solutions. Compared with other solutions, our results demonstrate that the costs in the LCGDS are only about 24% of the costs in the origin goods transmission scheme.

Large numbers of software-defined sensor devices have formed a basic infrastructure for data collection. One of the challenges is how to update the program codes of devices in a low-cost and reliable style. This paper figures that the vehicular wireless networks (VWNs), combined with a base station (BS) platform, can play a significant role in disseminating program codes in a smart city. A code dissemination algorithm that selects vehicles as disseminators is proposed to accomplish the coverage ratio of codes and benefits of vehicular disseminators in a reliable manner and to reduce the costs of the BS. The algorithm targets to make the disseminators to achieve more coverage regions of a code. The benefit distribution method, which targets the improvement of benefits in suburban regions and encourages vehicle owners, is redefined and differs from previous benefit distribution schemes. Through disseminating, the vehicles can obtain more benefits, and the costs of the BS can be reduced. We ultimately simulate the performances of the selection algorithms with GPS datasets to prove the progressiveness of the “Code Disseminations through Opportunistic Communication in Vehicular Wireless Networks”(CD-VWNs) scheme in terms of coverage and benefits. The benefit-ratio performance criterion can be improved by 21.078% over other models. The coverage-ratio performance criterion can result in an improvement of 31.94% over other selection schemes. The evaluation metric Zr performance criterion can result in an improvement of approximately 25.256%. With the CD-VWNs scheme, loss of codes can be decreased, and the dissemination system remains reliable and survivable.

Camera node perception capability is one of the crucial issues for visual sensor networks, which belongs to the field of Internet of Things. Multi-object tracking is an important feature in analyzing object trajectories across multiple cameras, thus allowing synthesis of data and security analysis of images in various scenarios. Despite intensive research in the last decades, it remains challenging for tracking systems to perform in real-world situations. We therefore focus on key issues of multi-object state estimation for unconstrained multi-camera systems, e.g., data fusion of multiple sensors and data association. Unlike previous work that rely on camera network topology inference, we construct a graph from 2-D observations of all camera pairs without any assumption of network configuration. We apply the shortest path algorithm to the graph to find fused 3-D observation groups. Our approach is thus able to reject false positive reconstructions automatically, and also minimize computational complexity to guarantee feasible data fusion. Particle filters are applied as the 3-D tracker to form tracklets that integrate local features. These tracklets are modeled by a graph and linked into full tracks incorporating global spatial-temporal features. Experiments on the realworld PETS2009 S2/L1 sequence show the accuracy of our approach. Analyses of the different components of our approach provide meaningful insights for object tracking using multiple cameras. Evidence is provided for selecting the best view for a visual sensor network.

Telemedicine is expected to play a significant role in previsioning better service and meeting various needs of patients, healthcare providers, and policy makers by exploiting advanced information and communication technologies. Because health data are highly sensitive, the security and privacy of telemedicine should be protected carefully to protect telemedicine from being potentially interrupted and attacked. In telemedicine networks, the sink node is the most vulnerable to attacks because it is where sensitive data will be collected. Thus, it is of great importance to protect the privacy of the sink node in telemedicine networks. To this end, this paper proposes an efficient privacy-preserving protocol for sink node location in telemedicine networks. Compared with the existing work, the scheme can improve the safe time of telemedicine networks by injecting request packets and reduce the delivery time by transmitting along the shortest route. In particular, the safe time can be improved by 28.57% to 52.70%, and the delivery time can be reduced by 22.86% to 27.61%.

In machine learning and data mining, feature selection aims to seek a compact and discriminant feature subset from the original feature space. It is usually used as a preprocessing step to improve the prediction performance, understandability, scalability, and generalization capability of classifiers. A typical gene microarray data set has the characteristics of high dimensionality, limited samples, and most irrelevant features, and these characteristics make it difficult to discover a compact set of features that really contribute to the response of the model. In this paper, a score-based criteria fusion feature selection method (SCF) is proposed for cancer prediction, and this method aims at improving the prediction performance of the classification model. The SCF method is evaluated on five open gene microarray data sets and three low-dimensional data sets, and it shows superior performance over many well-known feature selection methods when employing two classifiers SVM and KNN to measure the quality of selected features. Experiments verify that SCF is able to find more discriminative features than the competing methods and can be used as a preprocessing algorithm to combine with other methods effectively.

The smart device combined with artificial intelligence can act as robot system to perform data collection task. To minimize the data collection cost and to guarantee the quality of service (QoS) of tasks are two vital issue in such mobile robot system. Data collection platform and data reporter often needs to negotiate with each other before start of data collection which will generate a certain cost. Once the platform and the data reporter agree to the cooperation, data reporter will collect and report data for a period. However, in previous researches, it was often considered that data reporters can report data at any time without considering the cost of interaction and negotiation, which is not suitable for the practice. In this paper, we propose an efficiency cost data collection scheme (ECDCS) in which the data reporter is selected according to the contribution that all the data it collects have on the whole system rather than a single data samples. Because there exists correlation in data, matrix completion technology can be adopted to recover the missing data samples with partial data while guarantee the QoS of the task. So, a data reporter selection scheme ECDCS based on the matrix completion technology is proposed in which the selection is in terms of the cooperation effect of the reporters rather than a single data sample. The main goal is to select the reporter set with low cost and high QoS which has the best cooperative effect. By doing so, in the proposed data collection scheme, the missing of partial data can be tolerated which can reduce data collection cost while guarantee the QoS. The extensive experiments results indicate that the proposed scheme can effectively reduce the data cost while maintain the QoS of application.

The handwritten signature is a widely accepted biometric trait for the individual authentication. This paper proposes a secure and dynamic signature verification method which applies to the mobile phone. A key point for signature verification is to extract features of good distinguishability. There are four main steps in our feature extraction process, including preprocessing, attribute generation, attribute truncation and quantization, and feature generation. In addition to the global features that are extracted from the whole signature, we divide a signature into several segments and extract features from these separate regions. The final feature vector is the combination of global and regional features. A user template is constructed by averaging the feature vectors whose elements are scaled by the feature-specific factors. Then, the similarity score of the test signature to the user template can be measured by Euclidean distance. In order to protect the user privacy in a cloud computing scenario, the template and features are further protected by secure kNN which has no influence on signature matching. The performance of the proposed method is demonstrated on the SG-NOTE database acquired by Samsung Galaxy Note and the MCYT-100 database captured by a WACOM pen tablet.

Device to device (D2D) communication is a key candidate for 5G. Its purpose is to enable direct communication between user devices that are close to each other, thereby reducing the load on the base station. Wireless sensor networks (WSNs) have received a lot of attention as the basis for D2D. The opportunistic routing (OR) scheme is proposed to deal with data transmission problems in loss WSNs. In this paper, a duty cycle adaptive adjustment-based bopportunistic routing (DCAAOR) scheme is proposed to speed up reliable data transmission. According to the wake-up rule of nodes, we propose three different DCAAOR schemes, respectively. In modified opportunistic routing (MOR) scheme, the nodes are random awake/sleep. In active slot uniform distribution (ASUD) scheme, the active slot of relay nodes are evenly distributed by adaptive adjustment and the active slot group (ASG) scheme divides the active slots of the relay nodes into groups. The active slots of each group are the same, and the active slots of different groups are evenly distributed. After a lot of theoretical analysis, the ASUD and ASG scheme proposed in this paper is superior to the simple modified MOR scheme in terms of energy consumption and delay. When the duty length τ = 20, the energy consumption of the nodes in the MOR scheme is reduced by 38.75% compared with the OR scheme, and the other two schemes are reduced by 41.83%.

Monoclonal antibodies (mAbs) have wide clinical utility, but global access is limited by high costs and impracticalities associated with repeated passive administration. Here, we describe an optimized electroporation-based DNA gene transfer platform technology that can be utilized for production of functional mAbs in vivo, with the potential to reduce costs and administration burdens. We demonstrate that multiple mAbs can be simultaneously expressed at protective concentrations for a protracted period of time using DNA doses and electroporation conditions that are feasible clinically. The expressed mAbs could also protect mice against lethal influenza or Ebola virus challenges. Our findings suggest that this DNA gene transfer platform technology could be a game-changing advance that expands access to effective mAb therapeutics globally.