The broad learning system (BLS) is a lightweight neural network known for its efficient learning capabilities; however, it is limited by its reliance on a binary label strategy. Existing label enhancement models primarily focus on increasing the distances between labels from different classes, which inadvertently expands the distance within the same category. For classification tasks, maintaining similarity within the intraclass is essential for ensuring the model's effectiveness. To address this issue, we propose a groupwise label enhancement BLS model that ensures both intraclass similarity and interclass disparity of labels. Specifically, we develop a novel regression target that generalizes existing label enhancement targets in BLS, increasing the distances between labels of different classes while overcoming the constraints imposed by binary labels. Moreover, we design a groupwise constraint to jointly enhance the intraclass similarity and interclass disparity of labels. Additionally, we propose a novel alternating direction method of multipliers-based optimization algorithm to solve our proposed model, ensuring both computational efficiency and theoretical convergence. Experimental results on several public datasets demonstrate the outstanding effectiveness and efficiency of our proposed model compared to other state-of-the-art methods.

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.

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 rising security issues in the Internet of Things (IoT), IoT threat intelligence (short for ITI) raises more and more concern. However, the lack of ITI knowledge graphs hinders the sharing and utilization of ITI that is usually in the form of unstructured text data scattered around the Internet. In this article, we propose a knowledge extraction method vital to the construction of ITI knowledge graphs. We first build an ITI ontology based on existing security ontologies and knowledge bases, providing an organized schema to incorporate ITI text data. Secondly, we design a joint model for ITI entity and relation extraction, namely, ITIRel, based on TPLinkerplus. To assist ITIRel in ITI entity recognition, we introduce domain knowledge to help the model learn the semantics of IoT security terms, which also improves the accuracy of relation extraction. We further optimize the tagging scheme of TPLinkerplus to enhance joint extraction performance. Finally, due to the lack of annotated ITI text data, we combine manual annotation and data augmentation techniques to create a new ITI dataset. Experiment results show that ITIRel establishes the new state-of-the-art on the dataset, which implies that our knowledge extraction method is suitable for ITI.

This paper investigates the physical layer security for a distributed antenna system, where a base station (BS) equipped with multiple distributed antennas transmits signals to a legitimate user (U) through an opportunistic antenna. Meanwhile, multiple non-colluding eavesdroppers (Es) attempt to tap the information transmission between the BS and U. To enhance the physical layer security, a friendly jammer (J) is employed to create a potential interference to Es by emitting artificial noise. However, since U is not aware of the information sent by J, it can be affected by the interference. In this context, this paper proposes adaptive jamming (AJ) scheme and interference cancellation (IC) scheme to mitigate the interference’s impact on U and consequently improve secrecy performance. Specifically, the AJ scheme avoids interference from having a negative effect on the system security by setting an interference threshold, activating the jammer only when the interference to U is below the threshold value. By contrast, the IC scheme designs an advanced special signal to eliminate the interference from J to U. For comparison, we present conventional non-jammer (NJ) scheme as a benchmark. Exact and asymptotic secrecy outage probability expressions for NJ, AJ, and IC schemes are derived over Nakagami-mfading channels. Numerical results show that the proposed AJ and IC schemes perform better than NJ scheme in terms of secrecy outage probability. Additionally, the physical layer security of all schemes can be improved by increasing the number of distributed antennas.

Tensors are naturally suitable for representing high-dimensional data. Tensor train decomposition is an effective data processing method to cope with high-dimensional tensors. It is widely used in many fields, such as recommendation system, data completion and dimension reduction. However, experiments show that the traditional tensor decomposition method is only suitable for processing small-scale data. With the increase of the amount of data, the traditional algorithm will not be able to meet the efficiency of processing data. Therefore, this paper improves the traditional tensor train decomposition algorithm. Based on the most crucial step—SVD in the algorithm process, we first divide the matrix into column blocks, and then, considering the storage characteristics of cache, we call multiple threads to process different submatrix blocks. Each thread calls the one-sided Jacobi algorithm respectively to parallelize the SVD process of the matrix. In this paper, performance comparison experiments are carried out on simulated tensor data. The experimental results demonstrate that this method shows good scalability and can greatly improve the speed of tensor train decomposition.

Sliding window is a widely used model in the process of mining frequent patterns in data streams. In order to determine the exact size of the sliding window, it is necessary to know the time and scale of the change over data streams in advance. However, the prior knowledge is difficult to determine, and the fixed-size sliding window in the traditional algorithms obviously cannot adapt to this change, which have poor performance on the latest concepts of data streams. Based on the above shortcomings, this paper proposes a new frequent pattern mining algorithm based on concept drift using variable sliding window: VSWCDD (Variable Sliding Window-Concept Drift Detection), which is suitable for mining frequent patterns in changing data streams. The window size is dynamically determined by whether the concept drift occurs in the data stream. During the mining process, the mining result variables and the cause variables about the concept drift are monitored simultaneously. When the data stream is stable and there is no concept change, the window size keeps expansion. When the concept drift occurs, the window size changes according to the difference between detection nodes. Extensive experiments on both real and synthetic data show that the VSW-CDD algorithm proposed in this paper can timely detect the concept drift in the data stream and adapt to the new concepts by adjusting the window size. Finally, the latest frequent patterns in the data streams are mined and has a better effect for click data streams on e-commerce sites and medical data. Compared with other algorithms, the algorithm in this paper also has better performance in terms of recall and adaptation.

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.

Aiming at the shortcomings of the current YOLOv3 model, such as large size, slow response speed, and difficulty in deploying to real devices, this paper reconstructs the target detection model YOLOv3, and proposes a new lightweight target detection network YOLOv3-promote: Firstly, the G-Module combined with the Depth-Wise convolution is used to construct the backbone network of the entire model, and the attention mechanism is introduced and added to perform weighting operations on each channel to get more key features and remove redundant features, thereby strengthening the identification ability of feature network model’s to distinguish target objects among background; Secondly, in order to delete some less important channels to achieve the effect of compressing the model size and improving the calculation speed, the size of the scaling factor gamma in the batch normalization layer is used; Finally, based on NVIDIA’s TensorRT framework model conversion and half-precision acceleration were carried out, and the accelerated model was successfully deployed on the embedded platform Jetson Nano. The performed KITTI experimental results show that the inference speed of our proposed method is about 5 times that of the original model, the parameter volume is reduced to one tenth, the mAP is increased from 86.1% of the original model to 93.1%, and the FPS reaches 25.5fps, realizing the requirements of real-time detection with high precision.

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.

In order to solve the problem that the privacy preserving algorithm based on slicing technology is incapable of dealing with packet loss, this paper presents the redundancy algorithm for privacy preserving. The algorithm guarantees privacy by combining disturbance data and ensures redundancy via carrying hidden data. It also selects the routing tree that is generated by the CTP protocol as the routing path for data transmission. Through division at the source node, the method adds hidden information and disturbance data. This algorithm uses hidden data and adds perturbation data to improve the privacy preserving. Nonetheless, it can restore the original data when data are partly lost. According to the simulation via TOSSIM (TinyOS simulator), in the case of partial packet loss, the algorithm can completely restore the original data. Furthermore, the authors compared accuracy of proposed algorithm, probability of data reduction, data fitting degree, communication overhead, and PLR. As a result, it improves the reliability and privacy of data transmission while ensuring data redundancy.

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.

The Radio Frequency Identification (RFID) technology has been integrated into healthcare systems for the purpose of improving healthcare management. However, people have concerns about the security and privacy of this kind of RFID systems. In order to solve the security problems faced by RFID‐based healthcare systems, a novel security authentication protocol based on Physical Unclonable Function (PUF) and Advanced Encryption Standard (AES) encryption algorithm is designed. The protocol uses PUF technology to output unique and random responses to different excitation inputs, encrypts the authentication information sent by the tag, and uses the AES encryption algorithm to encrypt the authentication information between the cloud database and the reader. At the same time, in the authentication process, once the communicating entity completes the identity authentication of the other two entities, it immediately starts to update the key. The security analysis and formal analysis of BAN (proposed by Burrows et al.) logic prove the security and correctness of the protocol. Analysis results show that the computation cost and security performance of the proposed protocol are better than the compared protocols. Our findings will contribute to further enhancing the security for RFID healthcare systems.

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.

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.

Secure localization has become very important in wireless sensor networks. However, the conventional secure localization algorithms used in wireless sensor networks cannot deal with internal attacks and cannot identify malicious nodes. In this paper, a localization based on trust valuation, which can overcome a various attack types, such as spoofing attacks and Sybil attacks, is presented. The trust valuation is obtained via selection of the property set, which includes estimated distance, localization performance, position information of beacon nodes, and transmission time, and discussion of the threshold in the property set. In addition, the robustness of the proposed model is verified by analysis of attack intensity, localization error, and trust relationship for three typical scenes. The experimental results have shown that the proposed model is superior to the traditional secure localization models in terms of malicious nodes identification and performance improvement.