Federated learning enables global model training in a distributed manner without requiring clients to upload private data. However, the presence of malicious clients uploading incorrect model updates can severely degrade the performance of the global model. Existing methods often rely on limiting the number of malicious clients or require access to additional clean datasets. To address these limitations, we propose a secure aggregation algorithm named FedMCP to detect and remove malicious clients. FedMCP first constructs judgment vectors based on parameter importance. It then distinguishes benign from malicious clients based on the similarity between their judgment vectors. Moreover, the discrimination between benign and malicious clients is achieved by incorporating the historical distribution characteristics of the judgment vectors from known benign clients. Finally, the Isolation Forest algorithm is employed to remove malicious models that mimic the judgment vectors. Even in individual rounds where a large number of malicious clients participate in training, FedMCP can still accurately distinguish between benign and malicious clients. FedMCP maintains high identification accuracy for both benign and malicious clients, even under heavy adversarial participation. Extensive experiments across multiple datasets and models confirm that FedMCP effectively identifies and excludes malicious clients, achieving superior robustness and performance.

The integration of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) mounted on unmanned aerial vehicles (UAVs) offers a promising approach to enhancing wireless coverage and connectivity. However, practical deployment faces significant challenges due to STAR-RIS misalignment caused by UAV-induced vibrations and orientation offsets of the STAR-RIS substrate. To address these issues, this paper proposes a UAV-enabled aerial STAR-RIS multiple-input single-output (MISO) communication network that explicitly accounts for vibration-induced offsets and the orientation deployment of a vertically mounted STAR-RIS. By incorporating vibration offset compensation and orientation deployment strategies, a unified optimization framework is proposed to jointly optimize the UAV flight trajectory and STAR-RIS beamforming, thereby maximizing the system sum data rate. To handle the high-dimensional complexity and long-term exploration challenges inherent in dynamic optimization problems, a deep reinforcement learning (DRL) approach based on robust policy optimization (RPO) is adopted. This method introduces uniform perturbations into the action distribution to enhance policy entropy and exploration, improving training stability and overall performance. Extensive simulation results demonstrate that the proposed co-design framework significantly outperforms the conventional proximal policy optimization (PPO) algorithm, achieving faster convergence. Moreover, the orientation deployment of the UAV-mounted STAR-RIS yields a substantial improvement in the sum data rate. Even under high signal-to-noise ratio (SNR) conditions, the joint optimization of orientation control and vibration offsets closely approaches the ideal benchmark.

With the development of the medical service industry, traditional centralized medical device traceability management systems are becoming inadequate for current demands. To address the deficiencies in data integrity, security, and privacy protection within centralized medical device traceability management systems, this paper proposes a medical device traceability management scheme based on the Hyperledger Fabric blockchain platform. First, we design a comprehensive medical device supply chain management process that supports user traceability queries and transaction evidence storage. Second, to securely store consumer privacy transaction data, we develop a privacy data storage and sharing mechanism that integrates Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and SM4 hybrid encryption technology to ensure effective access control. This approach reduces the risks associated with plaintext data transmission while enhancing system security. Additionally, we design smart contracts to automate various functions related to information management, traceability queries, and access control, thereby improving the operational efficiency and flexibility of the system. Finally, experimental results indicate that the proposed scheme can effectively enhance the security of medical device traceability management.

In response to the high communication complexity and latency issues present in current Byzantine fault-tolerant algorithms applied in the field of medical device traceability, this paper proposes an improved RMG-PBFT consensus algorithm based on reputation value grouping and probabilistic matching. Firstly, a node reputation assessment model is developed to enable dynamic grouping of nodes during the consensus process, thereby enhancing consensus efficiency. A reputation recovery mechanism is introduced to ensure that the system can effectively recover in the face of sporadic errors or fluctuations in node performance, thus improving fault tolerance. The three-phase consensus process of the PBFT algorithm is optimized through the implementation of a dual-node communication mechanism based on probabilistic matching embedded in the Commit phase, which effectively reduces communication redundancy and resource consumption, while decreasing the likelihood of collusion among malicious nodes. Finally, some improvements to the view change protocol in the consensus are also presented. Experimental results indicate that the proposed RMG-PBFT consensus algorithm demonstrates better performance in terms of latency and throughput. This method shows favorable results in reducing communication overhead and delays among nodes, as well as mitigating the influence of malicious nodes within the traceability process.

Large-scale pre-trained visual foundation models, such as the Segment Anything Model 2 (SAM2), demonstrate strong performance in video segmentation. However, they require multiple iterations of sophisticated manual prompts to achieve satisfactory results. This paper introduces a method that integrates existing visual foundation models without the need for additional training, named IAP-SAM2, which enables iterative automatic prompting for open-world video segmentation. An innovative automatic prompting mechanism is designed to allow SAM2 to segment target object on videos. Additionally, we propose a multi-round iterative prompt generation strategy based on feature similarity, along with a voting mechanism to refine object segmentation and address occlusion issues in video segmentation. Experimental results show that IAP-SAM2 outperforms existing open-world segmentation approaches on the DAVIS and LVOS datasets, particularly in handling complex videos with multiple targets and object occlusions, while maintaining robust segmentation performance. In the era of emerging foundation models, this work unlocks the potential of these models for automated video segmentation and expands the pathway for leveraging combined foundation models to address real-world challenges.

Despite the significant advances made in large language models (LLMs) and vision-language models (VLMs), research on role-playing (RP) within VLMs remains in its nascent stages, with a conspicuous lack of systematic evaluations of their role-playing capabilities. This study aims to address this gap by exploring how specific prompts related to different roles influence VLM performance in image description tasks. We propose a comprehensive evaluation framework specifically designed to assess the role-playing abilities of VLMs, encompassing classification accuracy, semantic similarity, lexical diversity, and potential hazards of generated content. Our findings indicate that as the age of the roles increases, the performance of VLMs improves significantly; models portraying older roles produce descriptions that are semantically more accurate and contextually richer. Furthermore, the introduction of domain-specific roles markedly enhances model performance, particularly when expert knowledge aligns with task requirements. This study not only underscores the necessity for a systematic assessment of role-playing capabilities in VLMs but also provides valuable insights for the development of multimodal systems that exhibit contextual awareness and moral responsibility across various applications.

Deep Neural Networks (DNNs) are increasingly deployed in safety- and security-critical applications but remain vulnerable to adversarial and fault-inducing inputs, posing serious security risks. Traditional DNN testing often struggles to efficiently explore high-dimensional input spaces and to generate realistic test inputs, limiting its ability to expose security-critical weaknesses.We propose GMFuzz, a coverage-guided and fidelity-aware fuzz testing framework for enhancing the security evaluation of DNNs. By combining Monte Carlo Tree Search (MCTS) with the fidelity assessment of a GAN-based discriminator, GMFuzz performs adaptive mutation under a dual-objective reward, improving neuron coverage to expand the potential attack surface while preserving semantic fidelity. Experiments across multiple datasets and DNN architectures show that GMFuzz achieves higher coverage and fault-inducing rates than state-of-the-art fuzzers, demonstrating its practical value for robust and secure AI system assessment.

Session-based multimedia recommendation in edge computing remains an important issue for boosting the utilization of services since service composition has increasingly attracted attention. Existing session-based recommendations (SBRs) model the session sequence with multilevel feature extraction in graph neural networks (GNNs). However, multilevel feature extraction in disentangled graph neural networks causes over-smoothing and privacy leakage. To address the aforementioned problems, Secure and Efficient Session-based Multimedia Recommendation (SeSMR) model is proposed. In the proposed SeSMR model, based on BGV homomorphic encryption, a ciphertext training submodel is proposed to address the privacy leakage, ensuring the security in SBR. Furthermore, based on the reinforcement of feature activation, a residual attention mechanism is proposed to mitigate over-smoothing while maintaining the independence of multiple features. Finally, based on location coding, a soft attention mechanism is proposed to improve the recommendation accuracy, by introducing the position difference information between items into intra-session and inter-session scenarios. Experiments demonstrate that both Recall and MRR metrics exhibit nearly 2% to 5% improvement.

In the current era of rapid Internet and artificial intelligence development, the explosion of information requires effective filtering to match user interests. Accordingly, this paper proposes a personalized recommendation algorithm based on sentiment analysis and matrix factorization (PRSAMF). A sentiment analysis model is constructed utilizing a Long Short-Term Memory (LSTM) network for deep learning, with ongoing parameter adjustments for training and validation. Through this approach, the LSTM network effectively captures the emotional polarity in user reviews. This emotional polarity, combined with the user rating matrix, enhances the accuracy of representing user reviews. Subsequently, the user sentiment score matrix and the high-frequency matrix of user search items undergo factorization to uncover potential user preferences and item attributes. Recommendations are then made based on the users sentiment towards these attributes. Experimental results on the dataset demonstrate the models effectiveness, showing optimal accuracy and low loss rates in sentiment analysis. Additionally, the error rate remains within acceptable limits, indicating the feasibility and robustness of the proposed recommendation algorithm.

In recent years, the rapid development of Industrial Internet of Things (IIoT) has improved the efficiency of industrial production and also brought some security problems. Malicious devices join the network to tamper with data or obtain sensitive information, attack other honest devices, and affect network security. In response to the series of challenges brought by malicious IoT devices, this paper designs a reputation assessment scheme that uses communication time, communication quality and historical reputation value to calculate the reputation value of the device, which regulates the behaviour of the device and ensures the security of the device communication. When the device shows malicious behaviour, our scheme can reduce the reputation value of the device in time and reduce the loss. Most of the existing schemes in the industrial IoT environment store the reputation value and device information on a third-party server, which is too centralised and prone to information leakage or information tampering. In order to address this issue, this study introduces blockchain and IPFS to store device information and facilitate the process of reputation evaluation. By leveraging the characteristics of blockchain, such as its transparency, immutability, and openness, the security of information storage is ensured. Through experimental verification and performance evaluation, our scheme is able to quickly reduce the reputation score of a device when the device shows malicious behaviour, and also resists collusive attacks between devices and motivates the device to score honestly.

Abstract Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (namedMHDP$_{SVD\_{A}RIMA}$) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show thatMHDP$_{SVD\_{A}RIMA}$approach is effective and efficient in predicting missing healthcare data.

In the paper, we propose a truth inference algorithm based on bidirectional convolutional autoencoder to capture and utilize the internal structure information underling complex tasks, e.g. image segmentation. Firstly, the correlation information of adjacent pixels is extracted from the horizontal and vertical directions of the image by two encoders, and each pixel is encoded by the feature. Then, the embedding features of the two encoders are weighted and fused to obtain a new embedding feature, and the pixels are clustered according to this new embedding feature. Finally, determine whether the cluster is a background or an object based on what most people choose. Experiments are conducted on four real-world biomedical image datasets, and the experimental results prove the effectiveness and robustness of proposed algorithm.

In this study, we propose a cross-domain two-branch convolutional network framework (CD-Net) that maps image and point cloud features to a latent space, effectively addressing the semantic gap between the features of both domains. To enhance matching precision, we employ a describe-then-detect approach for local feature detection and description. This approach enables CD-Net to fully exploit the correlations between 2D images and 3D point clouds, enhancing the representation of features. We also design a loss function to guide the network to identify repeatable keypoints. Furthermore, we conduct a series of evaluation experiments on the SceneNN and 3DMatch datasets. These experiments showcase the robust performance of CD-Net in achieving precise and efficient 2D-3D matching.

In this paper, we explore the challenges associated with federated learning, a distributed machine learning paradigm that promotes collaborative model training while preserving the privacy of local client data. One significant hurdle is the non-IID nature of clients’ data, alongside limited communication resources between clients and the cloud server. These statistical heterogeneity and communication resource limitations pose practical obstacles to the implementation of federated learning. To address these challenges, we propose a communication-efficient framework called GCPFL for personalized federated learning. Our framework empowers individual clients to train personalized models while substantially reducing communication costs. Specifically, each client compresses the gradient before uploading it and handles the effects of gradient compression through an error correction process. By uploading only the compressed gradients, the communication costs are significantly diminished. On the cloud server side, the received gradients are recovered into models, and similarity aggregation is performed on these models to facilitate collaboration among clients. Once the aggregated models are received, clients conduct local updates to acquire personalized models. Extensive experimental results illustrate that the GCPFL algorithm not only achieves high model accuracy but also substantially reduces communication costs compared to existing methods.

Data-driven applications play an important role in modern-time maritime transportation systems, for instance in facilitating decision-making relating to communication and safety. One example application is position data sharing between vessels within the maritime Internet of Things (IoT)-enabled context. When designing such applications, we need to also consider how to ensure data accuracy as well as privacy in a large scale deployment. In this paper, we demonstrate the potential of using blockchain to facilitate privacy-preserving data sharing. Specifically, we develop a zero-knowledge proof-based scheme to protect vessel identities while allowing data sharing, and a commitment-based approach to ensure relationship-related privacy in data trading between participants. Our security and performance evaluations demonstrate the utility of the proposed approach.

With the continuous increment of city volume and size, a number of traffic-related urban units (e.g., vehicles, roads, buildings, etc.) are emerging rapidly, which plays a heavy burden on the scientific traffic control of smart cities. In this situation, it is becoming a necessity to utilize the sensor data from massive cameras deployed at city crossings for accurate traffic flow prediction. However, the traffic sensor data are often distributed and stored by different organizations or parties with zero trust, which impedes the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it requires challenging efforts to balance the trade-off between data sharing and data privacy to enable cross-organization traffic data fusion and prediction. In light of this challenge, we put forward an accurate LSH (locality-sensitive hashing)-based traffic flow prediction approach with the ability to protect privacy. Finally, through a series of experiments deployed on a real-world traffic dataset, we demonstrate the feasibility of our proposal in terms of prediction accuracy and efficiency while guaranteeing sensor data privacy.

Signcryption technology combines signature and encryption operations in a single step to achieve message authentication and confidentiality. The ordinary signcryption technology cannot realize communication between two different cryptographic systems. Therefore, to implement efficient communication between different cryptosystems and resist quantum attacks, this paper proposes a lattice-based efficient heterogeneous signcryption scheme. The heterogeneous signcryption scheme is proved to be secure assuming the hardness of small integer solution and learning with errors problems. Then this paper applies the lattice-based efficient heterogeneous signcryption scheme to the federated learning system to achieve the transmission of confidential information, and designs an intelligent federated learning system on lattice-based efficient heterogeneous signcryption. This system realizes federated learning and the quantum security of data transmission while preserving private data.

It is urgent to identify the development of the Corona Virus Disease 2019 (COVID-19) in countries around the world. Therefore, visualization is particularly important for monitoring the COVID-19. In this paper, we visually analyze the real-time data of COVID-19, to monitor the trend of COVID-19 in the form of charts. At present, the COVID-19 is still spreading. However, in the existing works, the visualization of COVID-19 data has not established a certain connection between the forecast of the epidemic data and the forecast of the epidemic. To better predict the development trend of the COVID-19, we establish a logistic growth model to predict the development of the epidemic by using the same data source in the visualization. However, the logistic growth model only has a single feature. To predict the epidemic situation in an all-round way, we also predict the development trend of the COVID-19 based on the Susceptible Exposed Infected Removed epidemic model with multiple features. We fit the data predicted by the model to the real COVID-19 epidemic data. The simulation results show that the predicted epidemic development trend is consistent with the actual epidemic development trend, and our model performs well in predicting the trend of COVID-19.

The application of digital tracking mechanism introduces a series of leakage problems of users' personal sensitive information related to the trajectory. Therefore, we propose a secure storage scheme for trajectory data. Firstly, four-dimensional spatiotemporal clustering of the trajectory data is performed to reduce the spatiotemporal complexity of data storage. Secondly, the privacy level of the trajectory data in the clusters is measured individually, which ensures the needs for personalized privacy protection are met. Finally, a noise trajectory (NTR) tree based on differential privacy is constructed, and the allocation of privacy budget and noise addition are optimized. Extensive simulations show that our scheme improves in terms of time efficiency, and achieves a flexible and effective balance between data accuracy and privacy.

Due to the openness nature of wireless medium, security issues have increasingly become a bottleneck that restricts the development of ultra-reliable and low-latency communications (uRLLCs). Physical layer security (PLS) technique has been proposed to fulfill the security and confidentiality of information transmission by exploiting the characteristics of the wireless channel, which caters to the features of uRLLC. Furthermore, PLS also shows great practicality in artificial intelligence field, especially in wireless intelligent networks. However, the previous works on the study of PLS ignored the significance of mobility and limited packet length constraint required by uRLLC for satisfying low latency, in this paper, we investigate the impact of mobility on the secrecy performance of uRLLC by using the Random WayPoint (RWP) model and the Random Direction (RD) model. Specifically, with the tools of stochastic geometry, to observe the impact of key system parameters on the secrecy performance, we establish the closed-form expression of connection outage probability, secrecy outage probability, and decoding error probability-based secrecy transmission capacity (DEP-STC). Furthermore, we derive the condition that achieves a positive DEP-STC under two moving models, which can offer the network designer some greatly significant insights into achieving perfect secrecy. Simulations validate our derived theoretical results, and indicate that RWP moving receiver can obtain a higher security level than RD moving one, while RWP eavesdropper can lead to a lower security.

Abstract Because of a large number of vehicles in Internet of Vehicle(IoV), distributed nodes and complex driving environment, data security and certification speed are easily affected. Blockchain enables different devices that do not trust each other to work together, maintain the general state in the process of information dissemination and sharing, and protect the privacy of devices. However, at present, the speed of vehicle certification in IoV is slow, and the use of idle resources is not considered. To address this problem, this paper provides a blockchain-based vehicle identity verification scheme by using a hybrid identity code verification method to ensure the nodes in the network securely share information. Meanwhile, a task processing algorithm based on time window is proposed to optimize the utilization of idle resources. In addition, the method is evaluated by simulation experiment, and the designed scheme can reduce malicious behavior of a registered vehicle in the network, and can shorten the processing task delay.

With the rapid development of the Internet of Things (IoT), large quantities of data have been generated. Due to the limitation of the network bandwidth, the time and energy consumption of data transmission are increased. Data feature information can be extracted in real-time by the deployment of a data processing center. In this article, a novel dimension reduction approach is proposed in edge computing. First, a four-layer data processing framework is designed for data acquisition. A task assignment algorithm (TAA) is used for the condition when the edge node stops working due to an accident. Second, a threshold strategy is proposed to filter the data and reduce the dimension. Finally, the dimension reduction algorithm based on adaptive maximum linear neighborhood selection (AMLNS) is proposed. The harmonic geodesic distance is introduced to avoid the deformation of the manifold structure in AMLNS algorithm. Particularly, multiple weights are used to construct linear structure, which has a better embedding effect than single weight. The maximum linear neighborhood error weight is used to calculate the data coordinates. Experimental results show that the TAA improves the task completion rate about 15% and 36% over the random assignment method in mobile layer and edge layer, respectively. Compared with the local linear embedding (LLE), the points distribution of AMLNS is more uniform and regular, the execution time of AMLNS is reduced by about 17%. Furthermore, the embedding errors are less than those of LLE.

The cyber-physical cloud systems (CPCSs) release powerful capability in provisioning the complicated industrial services. Due to the advances of machine learning (ML) in attack detection, a wide range of ML applications are involved in industrial CPCSs. However, how to ensure the implementation efficiency of these applications, and meanwhile avoid the privacy disclosure of the datasets due to data acquisition by different operators, remain challenging for the design of the CPCSs. To fill this gap, in this article a privacy-aware deployment method (PDM), named PDM, is devised for hosting the ML applications in the industrial CPCSs. In PDM, the ML applications are partitioned as multiple computing tasks with certain execution order, like workflows. Specifically, the deployment problem is formulated as a multiobjective problem for improving the implementation performance and resource utility. Then, the most balanced and optimal strategy is selected by leveraging an improved differential evolution technique. Finally, through comprehensive experiments and comparison analysis, PDM is fully evaluated.

A least squares support vector machine (LS-SVM) offers performance comparable to that of SVMs for classification and regression. The main limitation of LS-SVM is that it lacks sparsity compared with SVMs, making LS-SVM unsuitable for handling large-scale data due to computation and memory costs. To obtain sparse LS-SVM, several pruning methods based on an iterative strategy were recently proposed but did not consider the quantity constraint on the number of reserved support vectors, as widely used in real-life applications. In this article, a noniterative algorithm is proposed based on the selection of globally representative points (global-representation-based sparse least squares support vector machine, GRS-LSSVM) to improve the performance of sparse LS-SVM. For the first time, we present a model of sparse LS-SVM with a quantity constraint. In solving the optimal solution of the model, we find that using globally representative points to construct the reserved support vector set produces a better solution than other methods. We design an indicator based on point density and point dispersion to evaluate the global representation of points in feature space. Using the indicator, the top globally representative points are selected in one step from all points to construct the reserved support vector set of sparse LS-SVM. After obtaining the set, the decision hyperplane of sparse LS-SVM is directly computed using an algebraic formula. This algorithm only consumes O(N2) in computational complexity and O(N) in memory cost which makes it suitable for large-scale data sets. The experimental results show that the proposed algorithm has higher sparsity, greater stability, and lower computational complexity than the traditional iterative algorithms.

Following the recent advances in the Internet of Things (IoT), it is drawing lots of attention to design distributed algorithms for various network optimization problems under the SINR (Signal‐to‐Interference‐and‐Noise‐Ratio) interference model, such as spanner construction. Since a spanner can maintain a linear number of links while still preserving efficient routes for any pair of nodes in wireless networks, it is important to design distributed algorithms for spanners. Given a constant t > 1 as the required stretch factor, the problem of our concern is to design an efficient distributed algorithm to construct a t‐spanner of the communication graph under SINR such that the delay for the task completion is minimized, where the delay is the time interval between the time slot that the first node commences its operation to the time slot that all the nodes finish their task of constructing the t‐spanner. Our main contributions include four aspects. First, we propose a proximity range and proximity independent set (PISet) to increase the number of nodes transmitting successfully at the same time in order to reduce the delay. Second, we develop a distributed randomized algorithm SINR‐Spanner to construct a required t‐spanner with high probability. Third, the approximation ratio of SINR‐Spanner is proven to be a constant. Finally, extensive simulations are carried out to verify the effectiveness and efficiency of our proposed algorithm.

In this paper, we focus on the design of optimal relay and jammer selection strategy in relay-aided wireless networks. Different from previous works, assuming that the channel state information (CSI) of illegitimate nodes was available and only an eavesdropper existed, we first analyze disadvantages of joint relay and jammer selection (JRJS), average optimal relay selection (AORS), traditional maximum relay selection (TMRS) schemes. Then, we design an optimal relay and jammer selection strategy where the ratio of received SNRs at the destination generated by any two relays is maximized. By applying proposed strategy, computation complexity can be reduced. Moreover, we derive the lower and upper bounds of the secrecy outage probability based on the assumptions of existence of only illegitimate node and symmetric case for mathematical convenience. Finally, simulation shows that the proposed strategy operating with no CSI of illegitimate nodes can work efficiently compared with JRJS, TMRS and AORS strategies.

BACKGROUND: Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions. RESULTS: A five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method. CONCLUSIONS: With the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score.

The performance of multiple input multiple output (MIMO) wireless networks is limited mainly by concurrent interference among sensor nodes. Effective link scheduling algorithms with the technology of successive interference cancellation (SIC) can maximize throughput in MIMO wireless networks. Most previous works on link scheduling in MIMO wireless networks did not consider SIC. In this paper, we propose a MIMO-SIC (MSIC) algorithm under the SINR model. First, a mathematical framework is established for the cross-layer optimization of routing and scheduling, with constraints of traffic balance and link capacity. Second, the interference regions are divided to characterize the level of interference between links. Finally, we propose a distributed link scheduling algorithm based on MSIC to eliminate the interference between competing links in the MIMO network. Experimental results show that the MSIC algorithm can increase the end-to-end throughput per unit by approximately 73% on average compared with non-SIC algorithms.

With the development of Internet of Things, the number of network devices is increasing, and the cloud data center load increases; some delay‐sensitive services cannot be responded to timely, which results in a decreased quality of service (QoS). In this paper, we propose a method of resource estimation based on QoS in edge computing to solve this problem. Firstly, the resources are classified and matched according to the weighted Euclidean distance similarity. The penalty factor and Grey incidence matrix are introduced to correct the similarity matching function. Then, we use regression‐Markov chain prediction method to analyze the change of the load state of the candidate resources and select the suitable resource. Finally, we analyze the precision and recall of the matching method through simulation experiment, validate the effectiveness of the matching method, and prove that regression‐Markov chain prediction method can improve the prediction accuracy.

With the development of Internet of Things (IoT), the number of mobile terminal devices is increasing rapidly. Because of high transmission delay and limited bandwidth, in this paper, we propose a novel three‐layer network architecture model which combines cloud computing and edge computing (abbreviated as CENAM). In edge computing layer, we propose a computational scheme of mutual cooperation between the edge devices and use the Kruskal algorithm to compute the minimum spanning tree of weighted undirected graph consisting of edge nodes, so as to reduce the communication delay between them. Then we divide and assign the tasks based on the constrained optimization problem and solve the computation delay of edge nodes by using the Lagrange multiplier method. In cloud computing layer, we focus on the balanced transmission method to solve the data transmission delay from edge devices to cloud servers and obtain an optimal allocation matrix, which reduces the data communication delay. Finally, according to the characteristics of cloud servers, we solve the computation delay of cloud computing layer. Simulation shows that the CENAM has better performance in data processing delay than traditional cloud computing.

Global broadcasting is a fundamental problem in wireless multi-hop networks. In this paper, we propose two distributed deterministic algorithms for global broadcasting based on the Signal-to-Interference-plus-Noise-Ratio (SINR) model. In both algorithms, an arbitrary node can become the source node, and the rest of the nodes are divided into different layers according to their distance to the source node. A broadcast message is propagated from the source node to all the other nodes in a layer by layer fashion. Our first proposed algorithm (named TEGB) selects a Maximal Independent Set (MIS) for each layer. Subsequently, multiple subsets of the MIS are carefully selected so as to allow the most concurrent transmissions. Our theoretical analysis shows that TEGB has the time complexity of O(D log n), where n is the total number of nodes in the network and D is the diameter of the network. Compared with the popular algorithm DetGenBroadcast proposed in the work of Jurdzinski et al.(2013), TEGB has a logarithmic improvement in running time. Furthermore, we develop the second algorithm (named TBGB) to reduce the number of duplicated broadcast messages at each layer. To be specific, TBGB attempts to form a unidirectional spanning tree of the network. On the spanning tree, only the non-leaf nodes transmit the broadcast message. Therefore, the redundant broadcasts in the same layer are eliminated. Our theoretical analysis shows that TBGB has the time complexity of O(DΔ log n), where Δ is the maximum node degree.

Discrete beeping is an extremely rigorous local broadcast model depending only on carrier sensing. It describes an anonymous broadcast network where the nodes do not need unique identifiers and have no knowledge about the topology and size of the network. Within such a model, time is divided into slots, and nodes can either beep or keep silent at each slot. We consider the problem of constructing a minimum dominating set (MDS) and a minimum connected dominating set (MCDS), respectively, under the discrete beeping model in this paper. By assuming that an upper bound N of the network size is known, we first propose and analyze a distributed synchronous algorithm termed BMDS for constructing a minimum dominating set (MDS) and then propose a distributed synchronous algorithm BCDS for CDS construction based on a maximal independent set (MIS) algorithm and a weakly CDS (WCDS). To our best knowledge, we are the first to study the MCDS construction under the discrete beeping model. We prove that the time complexity of BMDS is O(log2N) rounds with constant approximation ratio of at most 2, and BCDS can converge to a CDS within O(log3N) rounds.

Correct verification is necessary at every design stage of a complex digital system. TED (Tayor Expansion Diagram) can be used not only to bit-level logical function but also to word-level arithmetic function. The method of equivalence checking based on TED is discussed in this paper, then a verifying algorithm for circuit including black box is proposed. Experimental results indicate that a lot of errors can be found at the early design stage using the algorithm in this paper.