Currently, utilizing Mobile Vehicles (MVs) equipped with both wireless charging and data transmission capabilities to recharge nodes and collect their data in a “parallel” manner has become an effective approach to enhancing the efficiency of Wireless Rechargeable Sensor Networks (WRSNs). However, how to address the varying types and frequencies of service requests from nodes while minimizing the data loss ratio remains a critical issue that needs to be solved. To this end, this paper proposes an energy Replenishment and data Collection strategy based on Minimizing the data Loss ratio (RCML). First, the theoretical upper bound of service duration per round for MV was calculated, and reasonable service request thresholds were set for nodes accordingly. Then, an objective function was constructed with the primary and secondary goals of reducing data loss and minimizing travel duration of MV, respectively. Based on this, a Service Queue Generation algorithm (SQG) which utilizes the simulated annealing was proposed. To address the issue of a large number of requests, a Node Selection Strategy (NSS) was also proposed to prioritize the nodes at higher risk of data loss. Finally, an Idle-Time Service (ITS) strategy was adopted to further improve the overall service efficiency of MV. Simulation results show that RCML demonstrates significant advantages in terms of data loss ratio as well as energy consumption of MV compared to typical methods such as MPF and PMCDC.

With the development of wireless communication technology and the Industrial Internet, Software Defined Network (SDN) technology has been introduced to wireless sensor networks due to its agility and flexibility. This meets the potential scalability and flexibility requirements of the Internet of Things. Thus, a new Industrial Internet architecture, called SDN-WSN, was formed. As the scale of SDN-WSN increases, efficient routing protocols with low latency and high security are required, while the standard routing protocol of SDN is still vulnerable to dynamic changes in traffic control rules, especially when the network is under attack. To address the above issues, a network node credibility evaluation model based on D-S evidence theory was constructed to evaluate the trust value of wireless sensor network nodes. A trustworthy secure routing algorithm based on Q-learning (QTSRA) was proposed. This method extracts knowledge from historical traffic demands by interacting with the underlying network environment to evaluate the trustworthiness of network nodes. Simultaneously, it implements dynamic optimising routing strategies based on deep reinforcement learning algorithms. We conducted simulation experiments for several network performance metrics, and the results showed that the proposed QTSRA routing algorithm exhibited good performance. In most of the cases, the QTSRA had an improved relative performance gain as compared to the traditional AODV and OLSR routing algorithms.

“Request Triggered Recharging” has been a flexible type of scheduling schemes to allow the mobile charging vehicle (MCV) to supply energy for sensor nodes on demand. However, in most existing works, MCV always passively waits for the arrival of the unpredictable requests that may cause it missing the best departure time to serve nodes. To solve this problem, we propose a balanced distribution strategy for the number of recharging requests based on dynamic dual thresholds (BDRR). First, the adjustable double recharging request thresholds (DRRTs) are set for each node to ensure that all the requesting nodes can be successfully charged. Then, the method for setting the energy replenishment value (MSERV) is proposed to enable the distribution of the moments at which nodes send out their recharging requests being concentrated within each period. Furthermore, an efficient traversal path for the MCV is constructed by safe or dangerous scheduling strategy, and the charging capacity reduction scheme (CCRS) is also executed to help survive more nodes in need. Finally, a passer-by recharging scheme (PRS) is introduced to further improve the energy efficiency (EE) of the MCV. Simulation results show that BDRR outperforms the compared algorithms in terms of surviving rate of sensors as well as the EE of MCV with different network scales.

Efficiently and precisely identifying small items on traffic highways using unmanned aerial vehicle (UAV) platforms with limited resources is a crucial yet challenging job. This research suggests a speedy, accurate, and component-optimized road surveillance model (SAC-RSM) for UAVs. This model addresses slow detection speed, limited detection of small objects, and deployment difficulties. First, we designed a super-resolution-assisted learning branch in the network to balance the model’s detection speed and accuracy. This branch learns the feature representation from low to high resolution. This branch uses multiscale feature fusion in the encoding stage to enhance the feature representation of small objects, thereby enhancing their detection accuracy. Second, to avoid the problem of cross-layer convolution, which results in the loss of fine-grained information and low-learning efficiency, we propose using the convolution-to-space-convolution (CSPC) module in the backbone network to improve model detection’s robustness. Third, to achieve real-time detection, we realized the model using the Huawei Ascend compute architecture for neural networks (CANNs) framework to enable automatic quantization and parallel inference acceleration. Finally, we deployed the accelerated model to the embedded platform Atlas 200I developer kit (DK) A2. Compared to the baseline model, the proposed method shows significant increases in mean average precision (mAP) values for the VisDrone and DroneVehicle data sets, with increases of 17.4% and 9.4%, respectively. The proposed method achieves frames/s (FPS) of 38.3, which is 2.1 times faster than the baseline model, meeting the requirement for high-performance real-time detection in a UAV environment.

With the increasing scale of parallel computer interconnection network, the possibility of processor failure or link failure between processors in the network is also increasing. In the design of supercomputers, not only link overhead and communication delay should be taken into account, but also fault-tolerant performance of networks should be emphasized. Locally exchanged twisted cube ($LeTQ$) is a newly proposed interconnection network with lower link overhead and shorter diameter. With the increasing scale of supercomputers, fault-tolerant routing is indispensable. In this article, we propose a new load balancing fault-tolerant routing algorithm based on node contraction for$LeTQ$networks. The proposed algorithm uses the node shrinkage method to evaluate the priority of nodes. The sending node adaptively adjusts the probability of forwarding packets to the neighbor node according to the priority of the neighbor node and the state of the network. The path can be adapted to the load state of the network. The simulation results show that the fault-tolerant routing algorithm has good performance in throughput and delay.

In the author list, "Ferry Sansoto" should be Ferry Susanto.• To reflect more accurately the contribution of the article, the title should be changed to "A permutation and equinumerosity based polynomial-time algorithm for simple undirected graph isomorphism."• In the abstract, the "Pythagorean Triples Theorem" should be removed.• In the abstract, "squared sums of elements" should be "nth power sums."• In Section 2.2, "and the sum of the individual squared elements.By checking two sums," should be ", the sum of the individual squared elements and until the sum of the nth power of the nth element in the array.By checking these sums,"• In Section 2.2, "For both vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one, the corresponding two graphs are isomorphic."should be "For both the vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one and the corresponding edge and vertex's adjacent relationship has been preserved, the corresponding two graphs are isomorphic."

Summary With the development of genome sequencing technology, the cost of genome sequencing is continuously reducing, while the efficiency is increasing. Therefore, the amount of genomic data has been increasing exponentially, making the transmission and storage of genomic data an enormous challenge. Although many excellent genome compression algorithms have been proposed, an efficient compression algorithm for large collections of FASTA genomes, especially can be used in the distributed system of cloud computing, is still lacking. This article proposes two optimization schemes based on HRCM compression method. One is MtHRCM adopting multi‐thread parallel technology. The other is HadoopHRCM adopting distributed computing parallel technology. Experiments show that the schemes recognizably improve the compression speed of HRCM. Moreover, BSC algorithm instead of PPMD algorithm is used in the new schemes, the compression ratio is improved by 20% compared with HRCM. In addition, our proposed methods also perform well in robustness and scalability. The Java source codes of MtHRCM and HadoopHRCM can be freely downloaded from https://github.com/haicy/MtHRCM and https://github.com/haicy/HadoopHRCM .

Summary In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high‐precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter‐level high‐precision positioning combined with high‐precision map data to complete the detection of lane markings. In the process of generating high‐precision maps or in the uncovered areas of high‐precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust.

Abstract In this paper, the secrecy performance in a MIMO cognitive radio (CR) relay network with energy harvesting (EH) and transmit antenna selection/maximal ratio combining (TAS/MRC) is invstigated, where the DF relaying protocol and multiple colluding passive eavesdroppers are considered. To improve the security of wireless transmission, two antenna selection schemes are proposed, namely, the optimal transmit antenna selection (OTAS) scheme and suboptimal transmit antenna selection (STAS) scheme. For the purpose of comparison, the space‐time transmission (STT) scheme is introduced as a baseline. The exact and asymptotic closed‐form secrecy outage probability (SOP) expressions for OTAS, STAS and STT schemes are derived over Rayleigh fading channels. An extension of the TAS framework to an artificial noise (AN) aided MIMO network is further presented and an AN aided transmit antenna selection (AN‐TAS) scheme is proposed, in which the unselected antennas at R are used to emit AN for interfering with Es. Numerical results show that the OTAS and STAS schemes perform better than STT scheme in terms of SOP. Meanwhile, the SOP of AN‐TAS scheme is much smaller than that of OTAS, STAS and STT schemes in the high SNR region, indicating the benefit of applying AN in MIMO network.

Abstract The secrecy outage performance for a cooperative cognitive radio energy‐harvesting network is analyzed. The cognitive network is composed of an energy‐constrained cognitive source (CS), multiple energy‐constrained cognitive relays (CRs) and a cognitive destination (CD) as well as an eavesdropper (E) coexists with a primary network consisting of a primary transmitter (PT) and a primary receiver (PR). The CS and CRs are equipped with energy harvesters for collecting energy from the radio frequency signal from PT and their transmit powers are limited by the interference threshold at PR. To prevent confidential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed‐form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. For the purpose of comparison, the classical round‐robin relay selection (RRRS) is also analyzed in terms of secrecy outage probability. Furthermore, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability.

This paper investigates the secrecy performance of a spectrum sharing network, where${N}$legitimate source-destination pairs orderly access the shared spectrum for communication, while an eavesdropper (E) attempts to tap the legitimate information transmission. To improve the physical layer security, we propose two jamming strategies that can intelligently switch between jamming and non-jamming, namely, suboptimal jammer selection (SJS) scheme and optimal jammer selection (OJS) scheme. Specifically, when a user pair is assigned to access the shared spectrum, another source is chosen as a friendly jammer in order to create intentional interference at E. For the purpose of comparison, we present the non-jammer selection (NJS) scheme as a benchmark. Analytical closed-form secrecy outage probability expressions of NJS, SJS and OJS schemes are derived over Nakagami-${m}$fading channels. We further present an asymptotic secrecy outage probability analysis to evaluate the secrecy diversity gain performance of NJS, SJS and OJS schemes. Numerical results show that the secrecy outage probability performance of OJS scheme is better than SJS and NJS schemes in the low average channel power gain${\mathop \Omega \nolimits _{D} }$region. Furthermore, the secrecy outage probabilities of NJS as well as SJS and OJS schemes converge to each other with the increase of${\mathop \Omega \nolimits _{D} }$, due to the fact that the OJS and SJS scheme will switch to NJS scheme when${\mathop \Omega \nolimits _{D} }$tends to infinity.

In this paper, we investigate the physical layer security for a cognitive multiuser network which is composed of multiple cognitive sources, a cognitive destination and an eavesdropper under the joint impact of hardware impairments (HIs) and channel estimation errors (CEEs). We consider a practical scenario where mutual interference exists between the primary users and cognitive users. To achieve high physical layer security with low implementation complexity, we propose three pure user scheduling schemes, namely, selection combining (SC) scheme, threshold-based switched diversity (tSD) scheme and switch-and-examine combining with post-selection (SECps) scheme. To further improve physical layer security, we present an extension of our SC framework to a jammer aided multiuser network and propose a jammer aided SC (JSC) scheme. We derive the closed-form intercept probability (IP), outage probability (OP) and effective secrecy throughput (EST) expressions for SC, tSD, SECps and JSC schemes over Nakagami-$m$channels to analyze the system performance. Numerical results show that among the three pure multiuser scheduling schemes, the SC scheme achieves the best secrecy performance with the highest complexity, the SECps scheme obtains the worst secrecy performance with the lowest complexity. In addition, the secrecy performance of JSC scheme is better than that of SC scheme in the high SNR region.

Summary Qualitative spatio‐temporal reasoning is an important problem in artificial intelligence and has been widely and successfully applied in geographic information system and spatio‐temporal database. Currently, action features can be found in spatio‐temporal domain and the existing spatio‐temporal formalisms are not suitable for dealing with dynamic spatio‐temporal knowledge. Thus, how to represent and reason dynamic spatio‐temporal knowledge has become an important research issue. In this article, we present a dynamic spatio‐temporal logic for representing and reasoning dynamic spatio‐temporal knowledge. is a natural combination of spatio‐temporal logic ‐8 based on ‐8 and propositional dynamic logic. Timed actions of can be considered as temporal terms, moving the regions of topological space from one time point to another. can capture actions that change spatial relations between regions over time. For a formula from , we present a construction of a Büchi tree automaton. At the same time, we prove that deciding the satisfiability problem of is an EXPTIME‐complete problem.

Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

The heterogeneous defect prediction (HDP) technique can predict defects in a target company using heterogeneous metric data from external company, which has received substantial research attention. However, existing HDP methods assume that source data is labeled but labeling data is expensive. Semi-supervised defect prediction technique can perform defect prediction with few labeled data. In this paper, we investigate a new problem — semi-supervised HDP (SHDP). To solve this problem, we propose a new approach named cost-sensitive kernel semi-supervised correlation analysis (CKSCA) as a solution of SHDP problem. It introduces unified metric representation and canonical correlation analysis to make the data distributions of different company projects more similar. CKSCA also designs a cost-sensitive kernel semi-supervised discriminant analysis mechanism to utilize the limited labeled data and sufficient real-life unlabeled data from different companies. Besides we collect lots of open-source projects from GitHub website to construct a new large-scale unlabeled dataset called GITHUB dataset. It contains 26,407 modules and is greater than each public project dataset. It has been public online and can be extended continuously. Experiments on the GITHUB dataset and other public datasets indicate that unlabeled GITHUB data can help prediction model improve prediction performance, and CKSCA is effective and efficient for solving SHDP problem.

In this paper, we investigate the physical-layer security in an energy-harvesting (EH) multiuser network with the help of a friendly jammer (J), where multiple eavesdroppers are considered to tap the information transmission from users (Us) to base station (BS). In this system, a power beacon (PB) transmits radio frequency (RF) signals to Us for charging. In order to enhance the security of wireless transmission, we propose non-energy-aware multiuser scheduling (NEAMUS) scheme and energy-aware multiuser scheduling (EAMUS) scheme. For the purpose of comparison, we introduce conventional round robin multiuser scheduling (CRRMUS) scheme. The closed-form outage probability (OP) and intercept probability (IP) expressions of NEAMUS, EAMUS, and CRRMUS schemes are derived over Rayleigh fading channels. Additionally, we analyze the security-reliability tradeoff (SRT) of NEAMUS, EAMUS, and CRRMUS schemes in terms of OP and IP. Numerical results show that the proposed EAMUS scheme is superior to the CRRMUS scheme and NEAMUS scheme in terms of SRT, demonstrating the advantage of the proposed EAMUS scheme in improving the physical-layer security and reliability. Moreover, SRT performance of NEAMUS and EAMUS schemes can also be improved by increasing the number of users.

Due to the lack of trusted third parties as guarantees in peer-to-peer (P2P) networks, how to ensure trusted transactions between peers has become a research hotspot. However, the open and distributed characteristics of P2P networks have brought challenges to network security, and there are problems such as node fraud and unavailability of services in the network. To solve the problem of how to select trusted transaction peers in P2P groups, a new trust model, GT-Bidding, is proposed in this paper. This model follows the bidding process of human society. First, each service peer applies for a group of guarantee peers and carries out credit mortgages for this service. Second, based on the entropy and TOPSIS method (Technology for Order Preference by Similarity to an Ideal Solution) approaching the ideal solution, a set of ideal trading sequences is selected. Then, the transaction impact function is used to assign weights to the selected guarantee peers and service nodes, respectively; thus, the comprehensive trust of each service node can be calculated. Finally, the service peer is verified using feedback based on the specific confidence level, which encourages the reputation of the service and its guarantee peers to update. Experiments show that GT-Bidding improves the successful transaction rate and resists complex attacks.

In most RFID localization systems, acquiring a reader antenna's position at each sampling time is challenging, especially for those antenna-carrying robot or drone systems with unpredictable trajectories. In this article, we present RF-MVO that fuses RFID and computer vision for stationary RFID localization in 3D space by attaching a light-weight 2D monocular camera to two reader antennas in parallel. First, the existing monocular visual odometry only recovers a camera/antenna trajectory in the camera view from 2D images. By combining it with RF phase, we design a model to estimate a scale factor for real-world trajectory transformation, along with spatial directions of an RFID tag relative to a virtual antenna array due to the mobility of each antenna. Then we propose a novel RFID localization algorithm that does not require exhaustively searching all possible positions within the pre-specified region. Second, to speed up the searching process and improve localization accuracy, we propose a coarse-to-fine optimization algorithm. Third, we introduce the concept of horizontal dilution of precision (HDOP) to measure the confidence level of localization results. Our experiments demonstrate the effectiveness of proposed algorithms and show RF-MVO can achieve 6.23 cm localization error.

The topological distance is to measure the structural difference between two graphs in a metric space. Graphs are ubiquitous, and topological measurements over graphs arise in diverse areas, including, e.g. COVID-19 structural analysis, DNA/RNA alignment, discovering the Isomers, checking the code plagiarism. Unfortunately, popular distance scores used in these applications, that scale over large graphs, are not metrics, and the computation usually becomes NP-hard. While, fuzzy measurement is an uncertain representation to apply for a polynomial-time solution for undirected multigraph isomorphism. But the graph isomorphism problem is to determine two finite graphs that are isomorphic, which is not known with a polynomial-time solution. This paper solves the undirected multigraph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial-time solution to check if two undirected multigraphs are isomorphic or not. Based on the solution, we define a new fuzzy measurement based on graph isomorphism for topological distance/structural similarity between two graphs. Thus, this paper proposed a fuzzy measure of the topological distance between two undirected multigraphs. If two graphs are isomorphic, the topological distance is 0; if not, we will calculate the Euclidean distance among eight extracted features and provide the fuzzy distance. The fuzzy measurement executes more efficiently and accurately than the current methods.

Recent RFID systems start attaching a tag array consisting of two or more tags on an object to deal with polarization mismatch and RF phase periodicity for battery-free sensing and localization. The multi-tag solution can also provide target orientation estimation. However, when these tags are closely spaced apart, mutual coupling will be induced, producing the unexpected changes in reported RSSI and RF phase. In this paper, we present RF-Mirror that enables compensating the distortion in a two-tag array labeled RFID system. The system would output the accurate difference in tag-to-antenna distances between two tags, which is a fundamental parameter in previous works for use. Firstly, we model the backscatter signal of a responding tag in a two-tag scenario, and then formulate novel RSSI- and RF phase-distance models with coupling terms. Secondly, we design an algorithm to characterize the coupling effect on tag gain by fusing RSSI and RF phase. Thirdly, we design a decoupling algorithm based on an observation that tag mutual coupling is independent of the position of a tag array relative to a reader antenna. Our experiments show the effectiveness of our models and RF-Mirror achieves the decoupling error of 0.197 cm in calculating the tag-to-antenna distance difference.

Summary Network‐on‐chip (NoC) is a new design method of system‐on‐chip used in very large scale integrated circuit (VLSI) systems. It is an important issue for choosing the appropriate topology for NoC. Wirelength and layout area are significant parameters affecting NoC due to the restriction of chip area. In this paper, we propose a new interconnection network called the incomplete ternary n‐cube for parallel computing systems. Then, a linear algorithm is proposed to layout incomplete ternary n‐cube network onto torus NoC. Furthermore, the failure of interconnection network is also taken into account, and a fault‐tolerant layout of incomplete ternary n‐cube with faulty edges into torus NoC is verified. Theoretical analysis demonstrates that the proposed algorithm can reduce the network cost and wirelength, which be conducive to estimate the wire length and chip area.

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.

Currently, lots of real social relations in social networks force users to face the potential risk of privacy leakage. Consequently, data holders would like to disturbor anonymize their individual data before publishing them, for the purpose of privacy protection. Due to the characteristics of high sensitivity and large volume data of social network graph structure, it is difficult for privacy protection schemes to enable a reasonable allocation of noises while keeping desirable data availability and execution efficiency. On the basis of differential privacy model, combining with clustering and randomization algorithms, a privacy protection approach PBCN (Privacy Preserving Approach Based on Clustering and Noise) is proposed. This proposal is composed of five algorithms including random disturbance based on clustering, graph reconstruction after disturbing degree sequence and noise nodes generation, etc. Furthermore, a privacy measure algorithm based on adjacency degree is put forward in order to objectively evaluate the privacy-preserving strength of various schemes against graph structure and degree attacks. Simulation experiments are conducted to achieve performance comparisons between PBCN, Spctr Add/Del, Spctr Switch, DER and HPDP. The experimental results show that PBCN realizes more satisfactory data availability and execution efficiency. Finally, parameters utility analysis demonstrates PBCN can achieve a “trade-off” between data availability and privacy protection level.

In various applications of Wireless Sensor Network(WSN), the wildly used low-cost sensors are prone to measurement drift, which requests re-calibration to provide the accurate data. Otherwise, the data gathered by those sensors on massive mobile devices may be unavailable to use. In this research domain, sensor calibration is considered as a challenging problem.In this paper, the authors propose a cooperative calibration method, especially suitable for Mobile Wireless Sensor Network(MWSN). Different from those existing methods, this method introduces auto-calibration among mobile nodes. In the first phase, the calibration is carried out between fixed and selected mobile nodes. The second phase is modeled by the calibration relationship between the mobile sensors. From the experimental evaluation, the proposed scheme shows good efficiency to acquire high calibration accuracy.

Semi-supervised multi-view feature learning (SMFL) is a feasible solution for webpage classification. However, how to fully extract the complementarity and correlation information effectively under semi-supervised setting has not been well studied. In this paper, we propose a semi-supervised multi-view individual and sharable feature learning (SMISFL) approach, which jointly learns multiple view-individual transformations and one sharable transformation to explore the view-specific property for each view and the common property across views. We design a semi-supervised multi-view similarity preserving term, which fully utilizes the label information of labeled samples and similarity information of unlabeled samples from both intra-view and inter-view aspects. To promote learning of diversity, we impose a constraint on view-individual transformation to make the learned view-specific features to be statistically uncorrelated. Furthermore, we train a linear classifier, such that view-specific and shared features can be effectively combined for classification. Experiments on widely used webpage datasets demonstrate that SMISFL can significantly outperform state-of-the-art SMFL and webpage classification methods.

Description logic, as a logical foundation of knowledge representation and reasoning, plays an important role in the Semantic Web. In practical applications, many fields contain a large number of fuzzy spatio-temporal knowledge. With a large amount of fuzzy spatio-temporal knowledge and many corres ponding applications being incorporated into the Semantic Web, description logic becomes an effective method to solve the problem of fuzzy spatio-temporal knowledge representation and reasoning. Currently, many efforts have been done on fuzzy spatio-temporal extensions of description logics, and the literature on fuzzy spatio-temporal description logic has been booming. To address these issues and more importantly, in this paper, we provide a comprehensive survey of the research literature that applies description logics techniques in fuzzy spatio-temporal representation and reasoning. The paper serves as helping readers grasp the main results and highlighting the direction of fuzzy spatio-temporal representation and reasoning based on description logics.

Radio frequency identification technology (RFID) is widely used in identity authentication and payment, and it also becomes an indispensable part of daily life. Cloud-based RFID systems have broad application prospects, and can be provided as a service to individuals or organisations. For example, RFID cards can be used for cash-less payment, physical access control, temporary rights and identification in cloud environment. When an RFID card is used, there is a wireless transaction between the card and its reader, which could be attacked by several methods, including a relay attack. Relay attacks are difficult to completely prevent and a serious threat to RFID systems security. An attacker could use limited resources to build up this kind of attack and may need little knowledge of the underlying protocol. In recent years, researchers have proposed solutions using second channels to resist relay attack, such as using environmental measurements including noise, light and temperature. This paper describes research on the defence techniques for relay attacks in cloudbased RFID systems. The cloud-based architecture for RFID systems typically consists of RFID tags, card readers (fixed or mobile) and cloud-based server functionality.

Accurately estimating origin-destination (OD) network traffic is crucial for network management and capacity planning. However, the potential network anomaly and complex noise make this goal difficult to achieve. Existing network traffic estimation methods usually impute network traffic independent of anomaly detection, which ignores the potential relationship between the two tasks to help each other in achieving better performance. Moreover, these approaches can only be suitable for simple Gaussian or outlier noise assumptions, which cannot be applied to more complex noise distributions in practical applications. To address these issues, we propose a novel anomaly-tolerant network traffic estimation approach for simultaneously estimating network traffic and detecting network anomaly. Specifically, by utilizing the inherent low-rank property and temporal characteristic of traffic matrix, we formulate the network traffic estimation problem as a noise-immune temporal matrix completion (NiTMC) model, where the complex noise is fitted by mixture of Gaussian (MoG), and the network anomaly is smoothed by the L2,1-norm regularization. In addition, we also design a convergence-guaranteed optimization algorithm based on the expectation maximization (EM) and block coordinate update (BCU) methods to solve the proposed model. Furthermore, to deal with large-scale network problems, we develop a scalable and memory-efficient algorithm by employing stochastic proximal gradient descent (SPGD) method. Finally, the extensive experiments performed on real datasets demonstrate that our proposed NiTMC model outperforms the previously widely used network traffic estimation methods.

With the rapid development of social networks, privacy has also attracted attention. Based on this problem, a privacy protection scheme for social networks based on classified attribute encryption (PPSSN) is proposed for the data owner and attribute management server to manage user permissions; the approach reduces data owner overhead and also avoids use of a property management server to limit access user collusion attacks. To balance the privacy and security of data publication, this scheme classifies users and designs access control for different users and different privileges. In addition, this paper also introduces a good friend data cache mechanism to improve and optimize the original scheme to reduce the cost of decryption. The efficiency and system overhead of the proposed scheme are compared and analyzed based on experiments. The experiments show that the proposed scheme improves query efficiency, reduces system cost, and enhances privacy security.

As the Industry 4.0 or Internet of Things (IoT) era begins, security plays a key role in the Industry Internet of Things (IIoT) due to various threats, which include escape or Distributed Denial of Service (DDoS) attackers in the virtualization layer and vulnerability exploiters in the device layer. A successful cross-VM escape attack in the virtualization layer combined with cross-layer penetration in the device layer, which we define as an Advanced Persistent Escaper (APE), poses a great threat. Therefore, the development of detection and rejection methods for APEs across multiple layers in IIoT is an open issue. To the best of our knowledge, less effective methods are established, especially for vulnerability exploitation in the virtualization layer and backdoor leverage in the device layer. On the basis of this, we propose Escaper Cops (EscaperCOP), a detection method for cross-VM escapers in the virtualization layer and cross-layer penetrators in the device layer. In particular, a new detection method for guest-to-host escapers is proposed for the virtualization layer. Finally, a novel encryption method based on Identity-based Broadcast Encryption (IBBE) is proposed to protect the critical components in EscaperCOP, detection library, and control command library. To verify our method, experimental tests are performed for a large number of APEs in an IIoT framework. The test results have demonstrated the proposed method is effective with an acceptable level of detection ratio.

In this paper, a distributed filtering scheme is presented to deal with the fault detection problem of nonlinear stochastic systems with wireless sensor networks (WSNs). The nonlinear stochastic systems, which are of discrete-time form, are represented by interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy models. Each sensor of the WSN can receive measurements from itself and its neighboring sensors subject to a deterministic interconnection topology. Independent random variables obeying the Bernoulli distribution are formulated to characterize the randomly occurred packet losses between the WSN and the filter unit. To generate residual signals for evaluation functions of the fault detection mechanism, a novel type of IT2 T-S fuzzy distributed fault detection filter is proposed corresponding to each sensor node. Additionally, a fault reference model is adopted for improving the performance of the fault detection system. A new overall fault detection system is formulated in an IT2 T-S fuzzy model framework. Applying Lyapunov functional approach, we concentrate on the analysis of stability and performance of the resulting fault detection system. New techniques are utilized to handle the decoupling problem in design procedure. The desired parametric matrices of the fuzzy filters are designed subject to a developed criterion, which is a sufficient condition of the robust mean-square asymptotic stability for the overall fault detection system with a disturbance attenuation performance. Finally, a truck-trailer system with a four-node WSN is established for simulation validation. In simulations, the mincx function of the MatLab 2017a in Windows 10 OS is used to optimize the level of the disturbance attenuation performance, and to obtain the filter gains for the established system. By comparing the different time instants when the residual evaluation functions exceed their respective thresholds, simulation results successfully validate the effectiveness and applicability of the presented distributed fault detection scheme.

Stream join is one of the key operations for real-time stream data query and calculation. In light of changeable velocity of stream data, traditional static stream join methods are not so adaptive that stream data computing performance will be affected. Based on the large quantity and constantly changing velocity of stream data, by considering traditional stream join algorithm, this paper proposes an optimized algorithm for variable update cycle stream based on time window (VC-TWJoin, Variable Cycle Time Window Join). For unsteady stream calculated in stream join, the optimal update cycle will be calculated to reduce the response time of stream join and improve join efficiency and real-time capability. Both theoretical analysis and experiments demonstrate that the algorithm is better than traditional join algorithms in terms of real-time capability, join response time and throughput.

With the rapid development of Internet of Things (IOT), IOT is more and more important. Also, it faces serious security issues. This paper analyzes Mirai's architecture. The core components are C & C server and Loader server that take charge of command and control, IOT equipments are in charge of broadcast and attack. Paper analyzes Botnet propagation model, Mirais infection attack procedure, impact factor and then proposes the corresponding anti-virus strategy.

Most of the existing RFID-based localization systems cannot well locate RFID-tagged objects in a 3D space. Limited robot-based RFID solutions require reader antennas to be carried by a robot moving along an already-known trajectory at a constant speed. As the first attempt, this paper presents RF-MVO, which fuses battery-free RFID and monocular visual odometry to locate stationary RFID tags in a 3D space and recover an unknown trajectory of reader antennas binding with a 2D monocular camera. The proposed hybrid system exhibits three unique features. Firstly, since the trajectory of a 2D monocular camera can only be recovered up to an unknown scale factor, RF-MVO combines the relative-scale camera trajectory with depth-enabled RF phase to estimate an absolute scale factor and spatially incident angles of an RFID tag. Secondly, we propose a joint optimization algorithm consisting of coarse-to-fine angular refinement, 3D tag localization and parameter nonlinear optimization, to improve real-time performance. Thirdly, RF-MVO can determine the effect of relative tag-antenna geometry on the estimation precision, providing optimal tag positions and absolute scale factors. Our experiments show that RF-MVO can achieve 6.23cm tag localization accuracy in a 3D space and 0.0158 absolute scale factor estimation accuracy for camera trajectory recovery.

A see-through-wall system can be used in life detection, military fields, elderly people surveillance. and gaming. The existing systems are mainly based on military devices, customized signals or pre-deployed sensors inside the room, which are very expensive and inaccessible for general use. Recently, a low-cost RFID technology has gained a lot of attention in this field. Since phase estimates of a battery-free RFID tag collected by a commercial off-the-shelf (COTS) RFID reader are sensitive to external interference, the RFID tag could be regarded as a battery-free sensor that detects reflections off targeted objects. The existing RFID-based system, however, needs to first learn the environment of the empty room beforehand to separate reflections off the tracked target. Besides, it can only track low-speed metal objects with high-positioning accuracy. Since the human body with its complex surface has a weaker ability to reflect radio frequency (RF) signals than metal objects, a battery-free RFID tag can capture only a subset of the reflections off the human body. To address these challenges, a RFID-based human motion sensing technology, called RF-HMS, is presented to track device-free human motion through walls. At first, we construct transfer functions of multipath channel based on phase and RSSI measurements to eliminate device noise and reflections off static objects like walls and furniture without learning the environment of the empty room before. Then a tag planar array is grouped by many battery-free RFID tags to improve the sensing performance. RF-HMS combines reflections from each RFID tag into a reinforced result. On this basis, we extract phase shifts to detect the absence or presence of any moving persons and further derive the reflections off a single moving person to identify his/her forward or backward motion direction. The results show that RF-HMS can effectively detect the absence or presence of moving persons with 100% accuracy and keep a high accuracy of more than 90% to track human motion directions.

Exploiting radio frequency signals is promising for locating and tracking objects. Prior works focus on per-tag localization, in which each object is attached with one tag. In this paper, we propose a comprehensive localization and tracking scheme by attaching two RFID tags to one object. Instead of using per-tag localization pattern, adding one-more RFID tag to the object exhibits several benefits: 1) providing rich freedom in RFID reader's antenna spacing and placement; 2) supporting accurate calibration of the reader's antenna location and spacing, and 3) enabling fine-grained calculation on the orientation of the tags. All of these advantages ultimately improve the localization/tracking accuracy. Our extensive experimental results demonstrate that the average errors of localization and orientation of target tags are 6.415 cm and 1.330°, respectively. Our results also verify that the reader's antenna geometry does have impact on tag positioning performance.

WiFi technology has fostered numerous mobile computing applications, e.g. indoor localization, gesture and activity recognition, device-free localization, etc., due to its ubiquity. The awareness of LOS and NLOS is a prerequisite for WiFi-based methods, since the WiFi signals received under NLOS conditions may contain a lot of noise and multipath effects, exerting great influences on the accuracy of location or identification. Traditional schemes based on commodity WiFi devices can achieve real-time LOS/NLOS identification. However, these methods face the challenges of limited bandwidth and coarse multipath resolution. In this work, we explore the amplitude feature of PHY layer information, and accordingly propose AmpN, a real-time LOS identification scheme based on commodity WiFi infrastructure that is applicable in both static and mobile scenarios. AmpN employs BP neural network algorithm in static scenario and K-Mean method in dynamic scenario, respectively. Experimental results demonstrate that AmpN outperforms existing approaches, achieving overall LOS and NLOS detection rates of 94.2% and 97.6% in static case, and above 97% LOS and NLOS detection rates in mobile context. In addition, the detection delay is less than 0.4s when the link state switches from LOS to NLOS.

Due to its indispensability for device-free passive (DfP) sensing, DfP human detection has attracted numerous research efforts during the past years. Although previous works have achieved considerable detection performance, they mainly focus on moving human detection, making mobility a prerequisite for reliable detection. Besides, existing static human detection systems usually require dense deployment or controlled settings. In this paper, we propose a robust respiration-rate-estimation-based passive static human detection system, R-PSHD. Specifically, different from recent works which leverage the amplitude of channel state information (CSI) for DfP sensing, we resort to the more sensitive phase information for minute respiration detection. To deal with the randomness of raw phase, R-PSHD exploits phase difference between antennas for feature extraction. Moreover, due to varying sensitivity of different subcarriers, R-PSHD tries to identify the useful subcarriers and only uses them for accurate estimation. Experimental results with different people during a week demonstrate that R-PSHD achieves great performance with both TP and TN rate higher than 90%.

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human-computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit-receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

The convergence of Internet of Things, cloud computing, and wireless body-area networks (WBANs) has greatly promoted the industrialization of electronic-/mobile-healthcare (e-/m-healthcare). However, the further flourishing of e-/m-healthcare still faces many challenges including information security and privacy preservation. To address these problems, a healthcare system (HES) framework is designed that collects medical data from WBANs, transmits them through an extensive wireless sensor network infrastructure, and finally, publishes them into wireless personal-area networks via a gateway. Furthermore, HES involves the groups of send-receive model scheme to realize key distribution and secure data transmission, the homomorphic encryption based on matrix scheme to ensure privacy, and an expert system able to analyze the scrambled medical data and feedback the results automatically. Theoretical and experimental evaluations are conducted to demonstrate the security, privacy, and improved performance of HES compared with current systems or schemes. Finally, the prototype implementation of HES is explored to verify its feasibility.

Wi-Fi has been widely deployed with the rapid development of wireless communication technique. Wi-Fi hotspots are popular in campus, making convenient wireless network access possible. However, during class teaching, in order to avoid students browsing the web based on Wi-Fi hotspots and distracting, we hope Wi-Fi hotspots adaptively shield network access to students in the classroom, while grant access to users outside the classroom. In this work, we prototype TA3C system, a teaching-oriented adaptive Wi-Fi authorized access control scheme, using Channel State Information (CSI) to locate instead of coarse-grained and temporally unstable Received Signal Strength Indication (RSSI). CSI can distinguish multipath signals, stay stable in the same propagation environment, and show different characteristics in different propagation environments, based on which, we can distinguish outdoor and indoor environments, and identify user's location to decide whether to offer him wireless network access or not. Experiment results show that TA3C effectively achieve adaptive Wi-Fi authorized access control, which provides a guarantee for the quality of class teaching. Compared to traditional indoor localization techniques, TA3C does not need accurate location information but simply recognizes user's location indoors or outdoors, which means it does not need dedicated hardware, realizing the low-cost indoor localization technique.

Accurate and sufficient range measurements are essential for range-based localization in wireless sensor networks. However, noise and data missing are inevitable in distance ranging, which may degrade localization accuracy drastically. Existing localization approaches often degrade in terms of accuracy in the co-existence of incomplete and corrupted range measurements. To address this challenge, a noise-tolerant localization algorithm called NLIRM is presented. By utilizing the natural low rank property of Euclidean distance matrix, the reconstruction of partially sampled and noisy distance matrix is formulated as a norm-regularized matrix completion problem, where Gaussian noises and outliers are smoothed by Frobenius-norm and L1norm regularization, respectively. As far as we are aware of, this is the first scheme that can recover the missing range measurements and explicitly sift Gaussian noise and outlier simultaneously. Simulation results demonstrate that, compared with traditional algorithms, NLIRM achieves better localization performance under the same experiment setting. In addition, our algorithm provides an accurate prediction of outlier positions, which is the prerequisite for malfunction diagnosis in WSN.

Robust Device-free passive (Dfp) detection is an essential primitive for a broad range of applications such as intrusion detection and smart space. Most recent works focus on finer-grained Channel State Information (CSI), instead of the variable Received Signal Strength (RSS). However, existing solutions have some limitations, being feasible only in the line of sight (LOS) or for more than one targeted entities. Moreover, space diversity supported by the MIMO systems hasn't been fully investigated. Motivated by this observation, we propose a novel scheme for Robust Passive Motion Detection (R-PMD). In our scheme, the variance of CSI amplitude feature is extracted as a new metric and the earth mover's distance (EMD) is utilized to determine the detection results. Besides, CSIs across multiantennas are further exploited to improve the detection precision and robustness. We prototype R-PMD on commercial WiFi devices and evaluate it in a typical indoor scenario. Experiment results show R-PMD can achieve great performance in terms of sensitivity and robustness.