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A newly developed frosting simulation method, pseudo-VOF (p-VOF) method, has applied to simulate the dynamic frost formation on the NACA0012 airfoil under strong convection. The p-VOF method is a pseudo volume of fraction simulation method of the multiphase flow with phase change. By solving a simplified mass conservation equation explicitly instead of the original volume fraction equations in CFD software, the efficiency and robustness of calculation are greatly improved. This progress makes it possible to predict a long-time frost formation. The p-VOF method was successfully applied to the simulation of dynamic frosting on the two-dimensional NACA0012 airfoil under strong convection conditions with constant frost physical properties. The simulation result shows that the average thickness of the frost layer increases, and the frost bulges and flow separation appear earlier, when the airfoil surface temperature decreases or the air humidity increases. The frost bulges and flow separation appear earlier, when the air velocity is faster, the growth rate of the frost layer at the early stage is greater, but the final frost layer is thinner.

Sensing coverage has attracted considerable attention in wireless sensor networks. Existing work focuses mainly on the 0/1 disk model which provides only coarse approximation to real scenarios. In this article, we study the connected target coverage problem which concerns both coverage and connectivity. We use directional probabilistic sensors, and combine probabilistic and directional sensing model features to characterize the quality of coverage more accurately in an energy efficient manner. Based on the analysis of the collaborative detection probability with multiple sensors, we formulate the minimum energy connected target ϵ -probability coverage problem, aiming at minimizing the total energy cost while satisfying the requirements of both coverage and connectivity. By a reduction from a unit disk cover, we prove that the problem is nondeterministic polynomial (NP)-hard, and present an approximation algorithm with provable time complexity and approximation ratio. To evaluate our design, we analyze the performance of our algorithm theoretically and also conduct extensive evaluations to demonstrate its effectiveness.

Recommendation technology is an important part of the Internet of Things (IoT) services, which can provide better service for users and help users get information anytime, anywhere. However, the traditional recommendation algorithms cannot meet user's fast and accurate recommended requirements in the IoT environment. In the face of a large-volume data, the method of finding neighborhood by comparing whole user information will result in a low recommendation efficiency. In addition, the traditional recommendation system ignores the inherent connection between user's preference and time. In reality, the interest of the user varies over time. Recommendation system should provide users accurate and fast with the change of time. To address this, we propose a novel recommendation model based on time correlation coefficient and an improved K-means with cuckoo search (CSK-means), called TCCF. The clustering method can cluster similar users together for further quick and accurate recommendation. Moreover, an effective and personalized recommendation model based on preference pattern (PTCCF) is designed to improve the quality of TCCF. It can provide a higher quality recommendation by analyzing the user's behaviors. The extensive experiments are conducted on two real datasets of MovieLens and Douban, and the precision of our model have improved about 5.2 percent compared with the MCoC model. Systematic experimental results have demonstrated our models TCCF and PTCCF are effective for IoT scenarios.

Modeling and simulation of thermal-fluid systems are very important in industrial numerical simulation and play key roles in their design and control. In this paper, the modeling and simplification method of one-dimensional thermal-fluid network with variable-property are presented, including matrix representation of the network, simplification algorithm for series/parallel connection based on matrix operation and generation of flow equations based on system topology. This simplification is suitable for the simulation of thermal-fluid systems with arbitrary topological structure. The method to treat reflux during iteration is proposed. The outstanding features of the simplification algorithm are the significant reduction in the thermal-fluid network and therefore the number of the related governing equations, as well as the computation burden. The example in this paper shows that the number of the governing equations for flow is reduced by about 45% and the calculation time of flow calculation is reduced by an average of 32% after the simplification.

Big sensing data is commonly encountered from various surveillance or sensing systems. Sampling and transferring errors are commonly encountered during each stage of sensing data processing. How to recover from these errors with accuracy and efficiency is quite challenging because of high sensing data volume and unrepeatable wireless communication environment. While Cloud provides a promising platform for processing big sensing data, however scalable and accurate error recovery solutions are still need. In this paper, we propose a novel approach to achieve fast error recovery in a scalable manner on cloud. This approach is based on the prediction of a recovery replacement data by making multiple data sources based approximation. The approximation process will use coverage information carried by data units to limit the algorithm in a small cluster of sensing data instead of a whole data spectrum. Specifically, in each sensing data cluster, a Euclidean distance based approximation is proposed to calculate a time series prediction. With the calculated time series, a detected error can be recovered with a predicted data value. Through the experiment with real world meteorological data sets on cloud, we demonstrate that the proposed error recovery approach can achieve high accuracy in data approximation to replace the original data error. At the same time, with MapReduce based implementation for scalability, the experimental results also show significant efficiency on time saving.

Barrier Coverage is an important sensor deployment issue in many industrial, consumer and military applications.The barrier coverage in bistatic radar sensor networks has attracted many researchers recently. The Bistatic Radars (BR) consist of radar signal transmitters and radar signal receivers. The effective detection area of bistatic radar is a Cassini oval area that determined by the distance between transmitter and receiver and the predefined detecting SNR threshold. Many existing works on bistatic radar barrier coverage mainly focus on homogeneous radar sensor networks. However, cooperation among different types or different physical parameters of sensors is necessary in many practical application scenarios. In this paper, we study the optimal deployment problem in heterogeneous bistatic radar networks.The object is how to maximize the detection ability of bistatic radar barrier with given numbers of radar sensors and barrier’s length. Firstly, we investigate the optimal placement strategy of single transmitter and multiple receivers, and propose the patterns of aggregate deployment. Then we study the optimal deployment of heterogeneous transmitters and receivers and introduce the optimal placement sequences of heterogeneous transmitters and receivers. Finally, we design an efficient greedy algorithm, which realize optimal barrier deployment of M heterogeneous transmitters and N receivers on a L length boundary, and maximizing the detection ability of the barrier. We theoretically proved that the placement sequence of the algorithm construction is optimal deployment solution in heterogeneous bistatic radar sensors barrier. And we validate the algorithm effectiveness through comprehensive simulation experiments.

Detection and recognition of road traffic signs constitute an important element in Advanced Driver Assistance Systems (ADAS), which can provide real-time road sign perception information to vehicles. In this paper, we proposed a new traffic sign detection method based on adaptive color threshold segmentation and the hypothesis testing of shape symmetry by leveraging traffic signs and image data. First, we calculated an adaptive segmentation threshold using the cumulative distribution function of the image histogram. Based on this, we designed an approximate maximum and minimum normalization method, which is used to suppress the interference of high brightness area and background in image thresholding processes. Secondly, we transformed the highlight shape feature of thresholding image into a connected domain feature vector. And we formulated a shape symmetry detection algorithm based on statistical hypothesis testing to efficiently extract the ROI of traffic signs based on traffic data analysis. We performed some comprehensive experiments on the GTSDB (German Traffic Sign Detection Benchmark) dataset. The accuracy of traffic sign detection exceeded 94%. This method has higher detection accuracy and time efficiency than other methods, and better robustness under complex traffic environment.

Tensor data (i.e., the data having multiple dimensions) are quickly growing in scale in many practical applications, which poses new challenges for data modeling and analysis approaches, such as high-order relations of large complexity, gross noise, and varying data scale. Existing low-rank data analysis methods, which are effective at analyzing matrix data, may fail in the regime of tensor data due to these challenges. A robust and scalable low-rank tensor modeling method is heavily desired. In this paper, we develop an online robust low-rank tensor modeling (ORLTM) method to address these challenges. The ORLTM method leverages the high-order correlations among all tensor modes to model an intrinsic low-rank structure of streaming tensor data online and can effectively analyze data residing in a mixture of multiple subspaces by virtue of dictionary learning. ORLTM consumes a very limited memory space that remains constant regardless of the increase of tensor data size, which facilitates processing tensor data at a large scale. More concretely, it models each mode unfolding of streaming tensor data using the bilinear formulation of tensor nuclear norms. With this reformulation, ORLTM employs a stochastic optimization algorithm to learn the tensor low-rank structure alternatively for online updating. To capture the final tensors, ORLTM uses an average pooling operation on folded tensors in all modes. We also provide the analysis regarding computational complexity, memory cost, and convergence. Moreover, we extend ORLTM to the image alignment scenario by incorporating the geometrical transformations and linearizing the constraints. Extensive empirical studies on synthetic database and three practical vision tasks, including video background subtraction, image alignment, and visual tracking, have demonstrated the superiority of the proposed method.

Spay cooling is a complicated flow and heat transfer process affected by multi-factors among which the environmental pressure is extremely important. However the influence of pressure is not investigated sufficiently, especially the reduced pressure. In the present study, spray cooling under low initial environmental partial pressures and vapor partial pressures with R21 are investigated with a closed spray and condensation system. To study the influence of initial environmental partial pressure, different amounts of nitrogen are inflated into the vacuum flash chamber, while the vapor partial pressure is kept constant. To study the influence of vapor partial pressure, a cascade refrigerator is used to condense the vapor with different condensation temperatures so that the vapor partial pressure can be maintained or adjusted, while the initial environmental partial pressure is kept constant. The experimental results show that the spray cooling power increases monotonically with the increasing spray flow rate in the experimental range, while the cooling efficiency decreases with the increasing spray flow rate. The spray cooling power and cooling efficiency vary with the initial environmental partial pressure or the vapor partial pressure non-monotonously, which indicates there is an optimal pressure for the heat transfer performance. Besides, the mechanism of the non-monotonous variation trend is discussed based on the key aspects including flash evaporation, convection and boiling. Especially, the boiling heat transfer curve is applied to explain the trend.

Open-cell metal foam mini-channel evaporator (OMFME) is proposed to enhance heat transfer in electronics cooling. In present study, flow boiling in an OMFME is investigated using R134a. The OMFME has nine channels, which are 507 μm in width and 1097 μm in depth. Comparing with a mini-channel evaporator (ME) with the same footprint area, the heat transfer coefficient (HTC) is enhanced up to 1.5 times and the dry out is delayed. Flow patterns are presented and compared with an existed flow patterns map. HTCs generally increases with the quality, the heat flux and it is slightly influenced by the mass flow rate. Nucleate boiling and evaporation in the porous fins are the two heat transfer mechanisms in different quality range. A correlation is presented to calculate frictional pressure drop.

The transient heat transfer performance of spray cooling with R21 is studied experimentally. Three variation patterns of surface temperature depending on the initial surface temperature are observed. There is only one rapid temperature drop at lower initial surface temperature and two drops with a shoulder transition at higher initial surface temperature. Further increasing the initial surface temperature will deteriorate heat transfer. Based on the pool boiling heat transfer curve and one-dimensional transient heat conduction model, the temperature variation patterns and processes are discussed which relate to nucleate boiling, transition boiling and film boiling, respectively. The experimental results reveal that the deterioration temperature decreases with increase of the heating flux and nozzle-to-heater distance, while increases with the spray flow rate. Based on the experimental results a region map of temperature variation patterns in transient spray cooling is given.

In this paper, the performance of a concentrating photovoltaic/thermal solar system is numerically analyzed with a mathematical and physical model. The variations of the electrical efficiency and the thermal efficiency with the operation parameters are calculated. It is found that the electrical efficiency increases at first and then decreases with increasing concentration ratio of the sunlight, while the thermal efficiency acts in an opposite manner. When the velocity of the cooling water increases, the electrical efficiency increases. Considering the solar system, the surface of the sun, the atmosphere and the environment, we can get a coupled energy system, which is analyzed with the entropy generation minimization and the entransy theory. This is the first time that the entransy theory is used to analyze photovoltaic/thermal solar system. When the concentration ratio is fixed, it is found that both the minimum entropy generation rate and the maximum entransy loss rate lead to the maximum electrical output power, while both the minimum entropy generation numbers and the maximum entransy loss coefficient lead to the maximum electrical efficiency. When the concentrated sunlight is not fixed, it is shown that neither smaller entropy generation rate nor larger entransy loss rate corresponds to larger electrical output power. Smaller entropy generation numbers do not result in larger electrical efficiency, either. However, larger entransy loss coefficient still corresponds to larger electrical efficiency.

Experiments are carried out on the frost and defrost characteristics of fine tube bundles under coolant temperature between −20 and −58 °C. The diameter of the tube is 2 mm. The frost and defrost processes are observed by taking photos and measuring pressure drops. The results indicate that the frost layer thickness on the tube surface increases with decreasing coolant temperature, but decreases with increasing air humidity. A model is given to explain the variation of frost thickness. The experiments verified that spraying ethanol is effective on inhibiting the formation of the frost.

Amateur videos always contain focusing issues. A focusing mistake may produce out-of-focus blur, which seriously degrades the expressive force of the video. In this paper, we propose a patch-based method to remove the out-of-focus blur of a video and build an all-in-focus video. We assume that the out-of-focus blurry region in one frame will be clear in a portion of other frames; thus, the clear corresponding regions can be used to reconstruct the blurry one. We divide each video frame into a grid of patches and track each patch in the surrounding frames. We independently reconstruct each video frame by building a Markov random field model to identify the optimal target patches that are sharp, similar to the original patches, and are coherent with their neighboring patches within the overlapped regions. To recover an all-in-focus video, an iterative framework is utilized, in which the reconstructed video of each iteration is substituted in the next iteration. Finally, we employ the idea of a bilateral filter to temporally smooth the reconstructed video. The experimental results and the comparison with the previous works demonstrate the effectiveness of our method.

The influence of gravity on flow boiling poses a challenge to the development of two-phase thermal systems for space applications. The present study investigates the heat transfer coefficient of flow boiling in a mini-channel evaporator in both horizontal and vertically downward flow directions to observe the effect of gravity. The evaporator has 23 channels that are 624 μm in width and 923 μm in depth. The experiments show that nucleate boiling dominates the heat transfer. The heat transfer and flow patterns for the two directions differ when the mass flow rate is less than 264.3 kg/m2 s and the heat flux is less than 3.0 W/cm2. The experimental results are compared with five general correlations. The outlet Froude number is more suitable for correlating the results than existing gravity-independent criteria. Gravity may influence the heat transfer through instabilities and changes in the flow patterns.

Wireless Power Transfer (WPT) is a promising technology to replenish energy of sensors in Rechargeable Wireless Sensor Networks (RWSN). In this paper, we investigate the mobile directional charging optimization problem in RWSN. Our problem is how to plan the moving path and charging direction of the Directional Charging Vehicle (DCV) in the 2D plane to replenish energy for RWSN. The objective is to optimize energy charging efficiency of the DCV while maintaining the sensor network working continuously. To the best of our knowledge, this is the first work to study the mobile directional charging problem in RWSN. We prove that the problem is NP-hard. Firstly, the coverage utility of the DCV&rsquo, s directional charging is proposed. Then we design an approximation algorithm to determine the docking spots and their charging orientations while minimizing the number of the DCV&rsquo, s docking spots and maximizing the charging coverage utility. Finally, we propose a moving path planning algorithm for the DCV&rsquo, s mobile charging to optimize the DCV&rsquo, s energy charging efficiency while ensuring the networks working continuously. We theoretically analyze the DCV&rsquo, s charging service capability, and perform the comprehensive simulation experiments. The experiment results show the energy efficiency of the DCV is higher than the omnidirectional charging model in the sparse networks.

Heterogeneous Bistatic Radars (BR) have different sensing ranges and couplings of sensing regions, which provide more flexible coverage for the boundary at complex terrain such as across rivers and valleys. Due to the Cassini oval sensing region of a BR and the coupling of sensing regions among different BRs, the coverage problem of BR sensor networks is very challenging. Existing works in BR barrier coverage focus mainly on homogeneous BR sensor networks. This paper studies the heterogeneous BR placement problem on a line barrier to achieve optimal coverage. 1) We investigate coverage differences of the basic placement sequences of heterogeneous BRs on the line barrier, and prove the optimal basic placement spacing patterns of heterogeneous BRs. 2) We study the coverage coupling effect among adjacent BRs on the line barrier, and determine that different placement sequences of heterogeneous BR transmitters will affect the barrier&rsquo, s coverage performance and length. The optimal placement sequence of heterogeneous BR barrier cannot be solved through the greedy algorithm. 3) We propose an optimal BRs placement algorithm on a line barrier when the heterogeneous BR transmitters&rsquo, placement sequence is predetermined on the barrier, and prove it to be optimal. Through simulation experiments, we determine that the different placement sequences of heterogeneous BR transmitters have little influence on the barrier&rsquo, s maximum length. Then, we propose an approximate algorithm to optimize the BR placement spacing sequence on the heterogeneous line barrier. 4) As a heterogeneous barrier case study, a minimum cost coverage algorithm of heterogeneous BR barrier is presented. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

© 2018 Association for Computing Machinery. In a contemporary data center, Linux applications often generate a large quantity of real-time system call traces, which are not suitable for traditional host-based intrusion detection systems deployed on every single host. Training data mining models with system calls on a single host that has static computing and storage capacity is time-consuming, and intermediate datasets are not capable of being efficiently handled. It is cumbersome for the maintenance and updating of host-based intrusion detection systems (HIDS) installed on every physical or virtual host, and comprehensive system call analysis can hardly be performed to detect complex and distributed attacks among multiple hosts. Considering these limitations of current system-call-based HIDS, in this article, we provide a review of the development of system-call-based HIDS and future research trends. Algorithms and techniques relevant to system-call-based HIDS are investigated, including feature extraction methods and various data mining algorithms. The HIDS dataset issues are discussed, including currently available datasets with system calls and approaches for researchers to generate new datasets. The application of system-call-based HIDS on current embedded systems is studied, and related works are investigated. Finally, future research trends are forecast regarding three aspects, namely, the reduction of the false-positive rate, the improvement of detection efficiency, and the enhancement of collaborative security.

Video summarization is an effective way to facilitate video searching and browsing. Most of existing systems employ encoder-decoder based recurrent neural networks, which fail to explicitly diversify the system-generated summary frames while requiring intensive computations. In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames. Particularly, the GDA module has two advantages: (1) it models the relations within paired frames as well as the relations among all pairs, thus capturing the global attention across all frames of one video; (2) it reflects the importance of each frame to the whole video, leading to diverse attention on these frames. Thus, SUM-GDA is beneficial for generating diverse frames to form satisfactory video summary. Extensive experiments on three data sets, i.e., SumMe, TVSum, and VTW, have demonstrated that SUM-GDA and its extension outperform other competing state-of-the-art methods with remarkable improvements. In addition, the proposed models can be run in parallel with significantly less computational costs, which helps the deployment in highly demanding applications.

Synthetic hydroxyapatite (HAP) containing both Zn 2+ and SeO 3 2− was synthesized via a co-precipitation method and the effects of the two ion concentrations on the morphology, surface charge and composition of the HAP were studied. The Zn 2+ and SeO 3 2− co-substituted hydroxyapatite (ZnSeHAP) samples were characterized by various techniques. The synthetic ZnSeHAP products were well-crystallized with typical apatite structures. The zinc and selenium were incorporated into the HAP lattice by respectively replacing some of the Ca 2+ with Zn 2+ and some of the PO 4 3− with SeO 3 2− . As the Zn content increased, the a -axis of the HAP lattice increased and the c -axis decreased. An increase in Zn in the HAP lattice also led to a decrease in the surface charge when ZnSeHAP was suspended in simulated body fluid (pH 7.4). This study shows that the surface properties of hydroxyapatite can be controlled by adding suitable dopants, which is useful for biomedical applications.

Bulk data dissemination is a basic building block for enabling a variety of applications in wireless sensor networks such as software update, network bug fixing, surveillance video distribution, etc. The recent structure based approach looks promising for efficient dissemination since it facilitates transmission and sleep scheduling. However, a number of limitations exist in existing structured protocols. In this paper, we propose a correlated core based solution for efficient bulk data dissemination in wireless sensor networks (called CoCo+). CoCo+ has three salient features. First, CoCo+ is based on an efficient node selection algorithm for constructing the core structure by exploiting link correlation (called Correlated Core). Second, CoCo+ employs a novel consecutive transmission mechanism, which allows out-of-order transmissions and reduces propagation delay. Third, CoCo+ uses a novel lightweight negotiation mechanism that greatly reduces negotiation overhead as compared to the existing structured protocols. We implement CoCo+ with TinyOS/TelosB and conduct both simulation and testbed experiments. Results show that our proposed solution outperforms the state-of-the-art by 52.3 and 49.6% in terms of the number of transmissions and the completion time, respectively.

Droplet impact onto thin film is a ubiquitous phenomenon in many industrial applications. In the present study, the VOF method is adopted to simulate the crown evolution during the impact process with different surface tension coefficient, viscosity and gravity. Especially, the crown behavior is investigated in the energy aspect. The kinetic energy, gravitational potential energy, surface energy and viscous dissipation are taken into consideration. The results reveal that higher surface tension coefficient leads to larger crown diameter. The evolution of gravitational potential energy can be divided into three phases: decrease phase, increase phase and collapse phase. Similarly, the evolution of surface energy can be divided into four phases: increase phase, drop phase, flat phase and collapse phase. The drop phase is caused by the film rupture in the center zone. In the case with zero gravity the flat phase is replaced by a second increase phase. In particular, it is found that larger viscosity will postpone the film rupture and the film even doesn't break at high viscosity.

The impacts of rapid industrialization on agricultural soil cadmium (Cd) accumulation and their potential risks have drawn major attention from the scientific community and decision-makers, due to the high toxicity of Cd to animals and humans. A total of 812 topsoil samples (0&ndash, 20 cm) was collected from the southern parts of Jiangsu Province, China, in 2000 and 2015, respectively. Geostatistical ordinary kriging and conditional sequential Gaussian simulation were used to quantify the changes in spatial distributions and the potential risk of Cd pollution between the two sampling dates. Results showed that across the study area, the mean Cd concentrations increased from 0.110 mg/kg in 2000 to 0.196 mg/kg in 2015, representing an annual average increase of 5.73 &mu, g/kg. Given a confidence level of 95%, areas with significantly-increased Cd covered approximately 12% of the study area. Areas with a potential risk of Cd pollution in 2000 only covered 0.009% of the study area, while this figure increased to 0.75% in 2015. In addition, the locally concentrating trend of soil Cd pollution risk was evident after 15 years. Although multiple factors contributed to this elevated Cd pollution risk, the primary reason can be attributed to the enhanced atmospheric deposition and industrial waste discharge resulting from rapid industrialization, and the quick increase of traffic and transportation associated with rapid urbanization.

The widespread use of smartphones has brought great convenience to our daily lives, while at the same time we have been increasingly exposed to security threats. Keystroke security is an essential element in user privacy protection. In this paper, we present GazeRevealer, a novel side-channel based keystroke inference framework to infer sensitive inputs on smartphone from video recordings of victim's eye patterns captured from smartphone front camera. We observe that eye movements typically follow the keystrokes typing on the number-only soft keyboard during password input. By exploiting eye patterns, we are able to infer the passwords being entered. We propose a novel algorithm to extract sensitive eye pattern images from video streams, and classify different eye patterns with Support Vector Classification. We also propose a novel enhanced method to boost the inference accuracy. Compared with prior keystroke detection approaches, GazeRevealer does not require any external auxiliary devices, and it relies only on smartphone front camera. We evaluate the performance of GazeRevealer with three different types of smartphones, and the result shows that GazeRevealer achieves 77.43% detection accuracy for a single key number and 83.33% inference rate for the 6-digit password in the ideal case.

Blur is an important factor affecting the image quality. This paper presents an efficient no-reference (NR) image blur assessment method based on a response function of singular values. For an image, the grayscale image is computed to the acquire spatial information. In the meantime, the gradient map is computed to acquire the shape information, and the saliency map can be obtained by using scale-invariant feature transform (SIFT). Then, the grayscale image, the gradient map, and the saliency map are divided into blocks of the same size. The blocks of the gradient map are converted into discrete cosine transform (DCT) coefficients, from which the response function of singular values (RFSV) are generated. The sum of the RFSV are then utilized to characterize the image blur. The variance of the grayscale image and the DCT domain entropy of the gradient map are used to reduce the impact of the image content. The SIFT-dependent weights are calculated in the saliency map, which are assigned to the image blocks. Finally, the blur score is the normalized sum of the RFSV. Extensive experiments are conducted on four synthetic databases and two real blur databases. The experimental results indicate that the blur scores produced by our method are highly correlated with the subjective evaluations. Furthermore, the proposed method is superior to six state-of-the-art methods.

The rapid increase of multidimensional data (a.k.a. tensor) like videos brings new challenges for low-rank data modeling approaches such as dynamic data size, complex high-order relations, and multiplicity of low-rank structures. Resolving these challenges require a new tensor analysis method that can perform tensor data analysis online, which however is still absent. In this paper, we propose an Online Robust Low-rank Tensor Modeling (ORLTM) approach to address these challenges. ORLTM dynamically explores the high-order correlations across all tensor modes for low-rank structure modeling. To analyze mixture data from multiple subspaces, ORLTM introduces a new dictionary learning component. ORLTM processes data streamingly and thus requires quite low memory cost that is independent of data size. This makes ORLTM quite suitable for processing large-scale tensor data. Empirical studies have validated the effectiveness of the proposed method on both synthetic data and one practical task, i.e., video background subtraction. In addition, we provide theoretical analysis regarding computational complexity and memory cost, demonstrating the efficiency of ORLTM rigorously.

Wireless charging is an important issue in wireless sensor networks, since it can provide an emerging and effective solution in the absence of other power supplies. The state-of-the-art methods employ a mobile car and a predefined moving path to charge the sensor nodes in the network. Previous studies only consider a factor of the network (i.e., residual energy of sensor node) as a constraint to design the wireless charging strategy. However, other factors, such as the travelled distance of the mobile car, can also affect the effectiveness of wireless charging strategy. In this work, we study wireless charging strategy based on the analysis of a combination of two factors, including the residual energy of sensor nodes and the travelled distance of the charging car. Firstly, we theoretically analyze the limited size of the sensor network to match the capability of a charging car. Then, the networked factors are selected as the weights of traveling salesman problem (TSP) to design the moving path of the charging car. Thirdly, the charging time of each sensor node is computed based on the linear programming problem for the charging car. Finally, a charging period for the network is studied. The experimental results show that the proposed approach can significantly maximize the lifetime of the wireless sensor network.

Coverage is a fundamental issue in the research field of wireless sensor networks (WSNs). Connected target coverage discusses the sensor placement to guarantee the needs of both coverage and connectivity. Existing works largely leverage on the Boolean disk model, which is only a coarse approximation to the practical sensing model. In this paper, we focus on the connected target coverage issue based on the probabilistic sensing model, which can characterize the quality of coverage more accurately. In the probabilistic sensing model, sensors are only be able to detect a target with certain probability. We study the collaborative detection probability of target under multiple sensors. Armed with the analysis of collaborative detection probability, we further formulate the minimum ϵ-connected target coverage problem, aiming to minimize the number of sensors satisfying the requirements of both coverage and connectivity. We map it into a flow graph and present an approximation algorithm called the minimum vertices maximum flow algorithm (MVMFA) with provable time complex and approximation ratios. To evaluate our design, we analyze the performance of MVMFA theoretically and also conduct extensive simulation studies to demonstrate the effectiveness of our proposed algorithm.

Perimeter barriers can provide intrusion detection for a closed area. It is efficient for practical applications, such as coastal shoreline monitoring and international boundary surveillance. Perimeter barrier coverage construction in some regions of interest with irregular boundaries can be represented by its minimum circumcircle and every point on the perimeter can be covered. This paper studies circle barrier coverage in Bistatic Radar Sensor Network (BRSN) which encircles a region of interest. To improve the coverage quality, it is required to construct a circle barrier with a predefined width. Firstly, we consider a BR deployment problem to constructing a single BR circular barrier with minimum threshold of detectability. We study the optimized BR placement patterns on the single circular ring. Then the unit costs of the BR sensor are taken into account to derive the minimum cost placement sequence. Secondly, we further consider a circular BR barrier with a predefined width, which is wider than the breadth of Cassini oval sensing area with minimum threshold of detectability. We propose two segment strategies to efficiently divide a circular barrier to several adjacent sub-ring with some appropriate width: Circular equipartition strategy and an adaptive segmentation strategy. Finally, we propose approximate optimization placement algorithms for minimum cost placement of BR sensor for circular barrier coverage with required width and detection threshold. We validate the effectiveness of the proposed algorithms through theory analysis and extensive simulation experiments.

Sensing coverage is a fundamental problem in wireless sensor networks (WSNs), which has attracted considerable attention. Conventional research on this topic focuses on the 0/1 coverage model, which is only a coarse approximation to the practical sensing model. In this paper, we study the target coverage problem, where the objective is to find the least number of sensor nodes in randomly-deployed WSNs based on the probabilistic sensing model. We analyze the joint detection probability of target with multiple sensors. Based on the theoretical analysis of the detection probability, we formulate the minimum ϵ-detection coverage problem. We prove that the minimum ϵ-detection coverage problem is NP-hard and present an approximation algorithm called the Probabilistic Sensor Coverage Algorithm (PSCA) with provable approximation ratios. To evaluate our design, we analyze the performance of PSCA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.