This paper proposes CADiS, a causality-driven anomaly detection framework, to address the challenges of root cause identification in high-dimensional Industrial Internet of Things (IIoT) multivariate time series. The essential difference between CADiS and existing correlation-driven deep models lies in its core innovation: it fundamentally redefines anomalies as the structural decay of an underlying causal mechanism, rather than merely capturing symptomatic deviations or spurious correlations. Specifically, the framework first learns a directed and lag-aware causal prior from normal data, compiling it into a structured attention mask to constrain information flow. Then, a Causal-Phase Decomposition (CPD) technique treats each time window as a micro-experiment, comparing an ante-phase with a post-phase to explicitly capture the dynamics of causal attenuation. Inference relies on a unified Causal-Change Score (CCS), which quantifies the degradation of causal association strength, directly revealing the breakdown of the system’s causal logic. Furthermore, the decomposed causal change matrix allows for fine-grained and auditable root cause diagnosis. Extensive experiments on real-world industrial datasets demonstrate that CADiS significantly outperforms strong baselines, achieving theVROCof 89.93% andVPRof 76.93% on SWaT; TheAPRof 18.04% andVROCof 78.38% on SMD, thereby validating its robustness and diagnostic precision.

The issue of association privacy leakage has become increasingly critical during data release and usage. However, traditional privacy protection techniques often struggle to address privacy leakage resulting from implicit associations within the data. In this paper, we propose a novel mechanism based on Cobweb Privacy to safeguard association privacy more comprehensively. Firstly, we design the concept of ϵ-Cobweb Privacy (ϵ-CP) specifically to address association privacy leakage. This concept extends the traditional notion of differential privacy by incorporating associated prior knowledge, thereby offering more effective and comprehensive protection of association privacy. We further demonstrate its privacy guarantees through the theoretical analysis of the relationship between ϵ-CP, differential privacy, and pufferfish privacy. Secondly, we quantify the privacy leakage problem mathematically and examine the utility privacy trade off under various priors. Additionally, we present a universal framework for association privacy protection in data aggregation scenarios using the ϵ-CP mechanism. Finally, this framework is integrated with three different noise addition methods and compared against mechanisms based on differential privacy and pufferfish privacy, and its utility is validated through experiments on both non-temporal and temporal real-world datasets. The results show that ϵ-CP provides distinct advantages in the utility privacy trade-off.

Graph Neural Networks (GNNs) have become a powerful tool for learning representations from graph-structured data, leveraging the relationships between nodes and their features. Despite their success, they often lack interpretability due to the black-box nature of neural networks, and further development may be limited. Moreover, previous GNN-based multi-view methods typically rely on simple feature fusion techniques such as weighted averaging or concatenation, which fail to capture the complex dependencies between views. In this paper, we propose a novel framework, namely Interpretable Multi-View Feature Representation via physical partial differential equation (IMvFR), to address these limitations in the context of multi-view semi-supervised learning. By integrating GNNs with partial differential equations (PDEs), we model the evolution of multi-view feature representations as a dynamic process. This provides a natural and interpretable framework for understanding how information flows between different views, overcoming the black-box nature of traditional GNNs. Additionally, we formulate multi-view feature representations as an initial-value problem within the framework of PDEs, providing a clear and interpretable mechanism for label propagation and feature fusion, thus facilitating the acquisition of global and local information between views. Comprehensive experimental results on eight datasets demonstrate that the proposed method achieves superior performance compared with state-of-the-art methods.

For lung surgery robots, the precise segmentation of pulmonary fissures is very important. Damaging the inter-lobar fissures during surgery can have serious consequences. Accurately segmenting weak and abnormal fissures commonly found in clinical CT scans remains a challenging task. To solve the above problem, we aimed to develop a novel Convolution Transformer for accurate fissure segmentation (SC-Former). The proposed SC-Former adopts an encoder, attention block, and decoder structure. First, we designed an encoder with a hybrid CNNs-transformer block that ingeniously amalgamates coordinate convolution and coordinate transformer to effectively capture both local and global feature information. Second, we introduced the long skip connections of our designed attention block at layers of the decoder-encoder structure to emphasize the field of view for fissures. Third, we added the distance map strategy to alleviate the challenge of training the network to segment the false positives from the complex textures in the lung. Fourth, we developed a multi-scale supervision strategy for independent prediction at various decoder levels, effectively integrating multi-scale semantic information to facilitate the segmentation of weak and abnormal fissures. Because of the lack of open-source inter-pulmonary fissure datasets, we collected 3D CT scans from 400 participants in the clinical trial and created a new high-quality dataset: BMI dataset. Extensive experiments on this dataset revealed the great superiority of our method over several state-of-the-art competitors. The ablation study also validated the effectiveness and robustness of each part of SC-Former.

Federated learning is vulnerable to model poisoning attacks in which malicious participants compromise the global model by altering the model updates. Current defense strategies are divided into three types: aggregation-based methods, validation dataset-based methods, and update distance-based methods. However, these techniques often neglect the challenges posed by device heterogeneity and asynchronous communication. Even upon identifying malicious clients, the global model may already be significantly damaged, requiring effective recovery strategies to reduce the attacker's impact. Current recovery methods, which are based on historical update records, are limited in environments with device heterogeneity and asynchronous communication. To address these problems, we introduce FedHAN, a reliable federated learning algorithm designed for asynchronous communication and device heterogeneity. FedHAN customizes sparse models, uses historical client updates to impute missing parameters in sparse updates, dynamically assigns adaptive weights, and combines update deviation detection with update prediction-based model recovery. Theoretical analysis indicates that FedHAN achieves favorable convergence despite unbounded staleness and effectively discriminates between benign and malicious clients. Experiments reveal that FedHAN, compared to leading methods, increases the accuracy of the model by 7.86%, improves the detection accuracy of poisoning attacks by 12%, and enhances the recovery accuracy by 7.26%. As evidenced by these results, FedHAN exhibits enhanced reliability and robustness in intricate and dynamic federated learning scenarios.

Federated learning, as a distributed learning framework, aims to develop a global model while preserving client privacy. However, heterogeneity of client data leads to fairness issues and reduced performance. Techniques like parameter decoupling and prototype learning appear promising, yet challenges such as forgetting historical data and limited generalization persist. These methods also lack local insights, with locally trained features prone to overfitting, which affects generalization in global parameter aggregation. To address these challenges, we propose FedCPD, a personalized federated learning framework. FedCPD maintains historical information, reduces information loss, and increases personalization through hierarchical feature distillation and cross-layer feature fusion. Moreover, we utilize representation techniques like prototype contrastive learning and prototype alignment to capture diverse client data features, thus improving model generalization and fairness. Experiments show FedCPD outperforms state-of-the-art models, enhancing generalization by up to 10.40% and personalization by up to 4.90%, highlighting its effectiveness and superiority.

While being able to avoid single point failures, emerging decentralized security techniques are facing new challenges of reliability, robustness, and privacy preservation in blockchain-enabled Internet of Things (IoT) systems. To circumvent these issues, a zero-trust security scheme is proposed through distributed access control, enhanced authentication, dynamic authorization, and privacy preservation enabled by the consortium blockchain. The proposed scheme integrates three key components, i.e., a distributed recommendation mechanism, where multiple authorized nodes are utilized as referrers to efficiently confer their trust on a new public entity for enhanced authentication; an anonymous credential generation strategy, which is developed for the new entity to further protect its privacy from linking attacks; and an adaptive reputation update strategy, which is proposed for evaluating the nodes’ behaviors in the system for accountability and dynamic multiple-level authorization. The proposed scheme is implemented in a Hyperledge Fabric and the results show that it significantly enhances security and protects private information.

Federated Learning (FL) is the standard paradigm for privacy-preserving model training across distributed Industrial IoT (IIoT) devices; however, deployment remains hindered by non-IID data, high communication costs, and unstable asynchronous convergence. We present Energy-Aware Asynchronous Federated Learning with Adaptive Layered Personalization (EAFL-ALP), which achieves a 99.8% reduction in per-round traffic while improving accuracy and robustness. The framework comprises three coordinated modules: (1) Adaptive Fractal-Wave Personalisation Model (AFWPM), which for each client, grows an entropy-conditioned fractal branch and prunes it with wave-collapse, yielding a self-similar, capacity-adaptive head that captures data heterogeneity; (2) Layerwise Quantization-Based Reversible Differential Privacy Gradient Compression (LQGCM), a variance-driven block stratified that transmits 88-bit meta tuples only, enabling codebook resonance replay, invertible vector quantization and Laplace-private gradients without any numeric payload or sparsity mask; (3) Energy-Minimisation Aggregation Model (EMAM), a closed-form update that mixes staleness weights, proxy-gradient correction and EMA momentum for stable convergence on lossy links. Experiments on five IIoT benchmarks show that EAFL-ALP increases accuracy by up to 32.1%, accelerates convergence 3.3×, lowers privacy leakage by 34.1%, and reduces communication volume by two orders of magnitude with no loss of model fidelity.

User trajectories are denser and highly dynamic in mobile crowdsensing (MCS) system, rendering traditional privacy budget allocation schemes insufficient. Additionally, the protection of semantic location privacy is often neglected in these schemes, making them vulnerable to inference attacks. To address these deficiencies, a user trajectory privacy protection strategy based on deep reinforcement learning is proposed in this article. First, a differential privacy-based user trajectory privacy protection algorithm (DP-upps) is designed to protect the privacy by perturbing the extracted trajectory feature points. Then, a deep reinforcement learning-based privacy budget allocation algorithm (DRL-pbas) is introduced. The privacy budget is dynamically adjusted by deep reinforcement learning option to continuously adapt to environmental changes and maximize benefits. After that, a DRL-pbas based user privacy protection strategy (DRL-UPPS) is proposed, integrating semantic location privacy protection. This approach combines the previous two algorithms, allowing the privacy budget to be allocated in a way that effectively balances the protection of physical and semantic location privacy and data quality. Ultimately, a large number of simulation experiments are conducted based on real datasets. The experiments demonstrate that DRL-UPPS can effectively balance privacy protection and data quality, resisting the privacy attacks. Compared with other strategies, DRL-UPPS improves comprehensive privacy protection capability by approximately 10% and data utility by approximately 8%.

Communication and sensing integration (CSI) technology underpins efficient data acquisition, real-time sensing, and intelligent decision-making in intelligent transportation systems (ITS). By merging communication and sensing, CSI enables seamless data sharing and collaborative learning within the internet of vehicles (IoV), while tackling the complexities of dynamic, heterogeneous environments. However, IoV systems still confront suboptimal resource allocation, synchronization bottlenecks in federated learning (FL), and the delicate balance between privacy and data utility, limiting scalability and deployment. To address these issues, this article presents a CSI-driven federated learning framework comprising three key modules: the polar-driven resource allocation mechanism (PDRAM), the polar-driven asynchronous federated update mechanism (AFUM), and the deep context-aware dynamic privacy budget allocation model (DC-DPBA). Leveraging polar coding principles, PDRAM optimizes communication channel allocation by prioritizing high-fidelity data on high-speed channels. AFUM adopts a vehicle performance score and a progressive aggregation strategy for asynchronous updates, mitigating synchronization challenges. Meanwhile, DC-DPBA uses deep learning and contextual information to dynamically adjust privacy budgets, striking a balance between data utility and privacy protection. Experimental results show that this framework increases transmission efficiency, model training accuracy, and privacy preservation by 22%, 17%, and 17%, respectively, compared to state-of-the-art approaches, offering a scalable and secure solution for CSI-driven IoV environments.

Brain tumor detection has advanced significantly with the development of deep learning technology. Although multimodal data, such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT), has potential advantages in diagnostics, most existing studies rely solely on a single modality. This is because common fusion methods may lead to the loss of critical information when attempting multimodal fusion. Therefore, effectively integrating multimodal data has become a significant challenge. Additionally, medical image analysis requires large amounts of annotated data, and labeling images is a resource-intensive task that demands experienced professionals to spend a considerable amount of time. To address these challenges, this paper introduces a new unsupervised learning framework named Double-SimCLR. This framework builds on the foundation of contrastive learning and features a dual-branch structure, enabling direct and simultaneous processing of MRI and CT images for multimodal feature fusion. Given the "weak feature" characteristics of CT images (e.g., low soft tissue contrast and low resolution), we incorporated adaptive weight masking technology to enhance CT feature extraction. Moreover, we introduced a multimodal attention mechanism, which ensures that the model focuses on salient information, thereby elevating the precision and robustness of brain tumor detection. Even without substantial labeled data, experimental results demonstrate that Double-SimCLR achieves 93.458% accuracy, 92.463% precision, and a 93.058% F1-score, outperforming state-of-the-art (SOTA) models by 2.871%, 2.643%, and 3.098%, respectively.

As the multiprocessor systems are becoming large-scale, fault-diagnosis is crucial to ensure the reliability of multiprocessor systems. In order to improve the self-diagnosis capability of a multiprocessor system, a pessimistic fault diagnosis scheme such as$t/s$-diagnosis allows some fault-free processors to be mistakenly identified as faulty. All faulty processors in a$t/s$-diagnosable multiprocessor system ($t\leq s$) should be identified into a set with size up to$s$, when the total amount of faulty processors in the system does not exceed$t$. This article focuses on the$t/s$-diagnosis for the$n$-dimensional bijective connection network$X_{n}$. An adaptive$t/s$-diagnosis algorithm APDMM*$t/s$of complexity$O(M(log_{2}\,M)^{2})$under the comparison model is proposed, where$M$is the total amount of nodes in$X_{n}$. Then, the correctness of algorithm APDMM*$t/s$is proved by the fault-tolerant properties of the network itself. Moreover, we calculate the$t/s$-diagnosability of$X_{n}$by theoretical method in mathematics, which is$-\frac{1}{2}y^{2}+(n-\frac{1}{2})y+1$for$2 \leq y \leq n$under comparison model, where$s=-\frac{1}{2}y^{2}+(n-\frac{1}{2})y+y-1$. Furthermore, we apply algorithm APDMM*$t/s$on the hypercube and the real-world network WSN-DS to verify our main results, and analyze the experimental outcomes in terms of true positive rate, false positive rate, accuracy and precision. The experimental results reveal the advantage and high performance of our algorithm APDMM*$t/s$. Besides, we compare the$t/s$-diagnosability of$X_{n}$with traditional accurate diagnosability, and it turns out that as$n$gets larger, the$t/s$-diagnosability of$X_{n}$is significantly better than traditional accurate diagnosability.

As data sharing becomes more prevalent, there is growing interest in multiuser dynamic searchable symmetric encryption (MU-DSSE). It enables multiple authorized users to search the encrypted database while safeguarding data privacy. However, most existing schemes are inefficient due to complex computation operations and unaffordable storage burdens. In addition, some are susceptible to collusion attacks between cloud server and compromised users, leading to the leakage of search privacy from other legitimate users. To overcome these challenges, we propose a practical MU-DSSE scheme featuring an unlinkable key derivation mechanism to thwart collusion attacks. Moreover, the MU-DSSE scheme ensures both forward and backward securities in the dynamic setting. To enhance efficiency, we introduce an innovative identity-based key encapsulation mechanism for distributing authorization information to multiple users, significantly optimizing computation and storage costs on user sides and the data owner. Furthermore, we provide the formal security proof and performance analyses. The experimental results demonstrate that MU-DSSE incurs the constant-size storage cost on user sides and the data owner, and outperforms the existing schemes in practice.

Abstract In certain cloud Electronic Medical Records (EMRs) applications, the data ownership may need to be transferred. In practice, not only the data but also the auditing ability should be transferred securely and efficiently. However, we investigate and find that most of the existing data ownership transfer protocols only work well between two individuals, and they become inefficient when dealing between two communities. The proposals for transferring tags between communities are problematic as well since, they require all members get involved or a fully trusted aggregator facilitates ownership transfer, which are unrealistic in certain scenarios. To alleviate these problems, in this paper we develop a secure auditing protocol with decentralized (t, n)-threshold ownership transfer for cloud EMRs. This protocol is designed to operate efficiently without requiring the mandatory participation of every user or the involvement of any trusted third-party. It is achieved by employing the threshold signature. Rigorous security analyses and comprehensive performance evaluations illustrate the security and practicality of our protocol. Specifically, according to the evaluations and comparisons, the communication and computational consumption is independent of the file size, i.e., it is constant in our protocol for both communities.

With the rapid growth of the number of processors in a multiprocessor system, faulty processors occur in it with a probability that rises quickly. The probability of a subsystem with an appropriate size being fault-free in a definite time interval is a significant and practical measure of the reliability for a multiprocessor system, which characterizes the functionality of a multiprocessor system well. Motivated by the study of subgraph reliability, as well as the attractive structure and fault tolerance properties of$(n, k)$-arrangement graph$A_{n, k}$, we focus on the subgraph reliability for$A_{n, k}$under the probabilistic fault model in this article. First, we investigate intersections of no more than four subgraphs in$A_{n, k}$, and classify all the intersecting modes. Second, we focus on the probability$P(q, A_{n, k}^{n-1, k-1})$with which at least one$(n-1, k-1)$-subarrangement graph is fault-free in$A_{n, k}$, when given a uniform probability$q$with which a single vertex is fault-free, and we establish the$P(q, A_{n, k}^{n-1, k-1})$by adopting the principle of inclusion–exclusion under the probabilistic fault model. Finally, we study the probabilistic fault model involving a nonuniform probability with which a single vertex is fault-free, and we prove that the$P(q, A_{n, k}^{n-1, k-1})$under both models is very close to the asymptotic value by both theoretical arguments and experimental results.

Endogenous security has the ability to discover, eliminate, and solve internal security problems and hidden dangers within the network, and is a superior technology to ensure future network security. The$t/k$-diagnosis strategy, as a strong and adaptive self-diagnosis strategy, is an important part for ensuring endogenous security. Moreover, the folded hypercube ($FQ_{n}$) as a data transmission network (e.g., optical network) topology offers new potential for the construction of large-scale, high data throughput, and low-latency systems, such as computing network, human-cyber-physical systems, and smart grid. However, there are few studies on endogenous security based on$FQ_{n}$networks. Therefore, this article designs an adaptive system-level fault self-diagnosis strategy, namely Fast t/k-Diagnosis Under Maeng-Malek Model (Ftk-DIAG-MM*) to diagnosis the faulty vertices in$FQ_{n}$network under the Maeng-Malek model (MM* mod). Then, we provide a proof of the algorithm correctness theoretically by the fault tolerance of$FQ_{n}$network. It is derived by theoretical derivation that the$t/k$-diagnosability inherent to the$FQ_{n}$network is$(n+1)\break(k+1)-k(k+3)/2$. The simulation experiments demonstrate that the designed Ftk-DIAG-MM* strategy can correctly diagnose all vertices within the range allowed by the diagnosability, and still has a great performance when it exceeds the range allowed by the diagnosability. It greatly enhances the fault diagnosis capability of$FQ_{n}$network in the circumstance of misdiagnosing a few vertices, which provides an important theoretical basis for the reliability and endogenous security of$FQ_{n}$networks.

In service computing, network traffic anomaly detection is pivotal for monitoring and identifying irregularities in network traffic to uphold the security, reliability, and stability of networks and services. In network traffic data, centrality is exhibited as certain nodes more frequently act as communication sources or destinations, or play critical intermediary roles in the network. These structures are also among the targets of network bottlenecks and targeted attacks. Current unsupervised network traffic anomaly detection algorithms, based on low-rank tensor recovery, achieve effective detection performance by comprehensively capturing network information. However, these algorithms often neglect the underlying topological structure, focusing solely on linear data structures, which leads to overlooking the degree of traffic concentration and nonlinear data structures. It reduces the detection efficiency of abnormal traffic generated by targeted attacks. To comprehensively understand the evolution of traffic concentration over time, this study introduces a mathematical formula for tensor eigenvector edge centrality. The formula provides rankings of edge importance based on the significance of nodes and time layers, and the effectiveness of centrality is validated through structural perturbations in the network. On this basis, we design a low-rank tensor recovery model utilizing representation learning to obtain the centrality feature matrix of network traffic data. By encoding centrality for nonlinear proximity information, and incorporating the Laplacian matrix to capture nonlinear structural information in tensor decomposition, the accuracy of anomaly detection is enhanced. Extensive experiments on Abilene and GÈANT network traffic data demonstrate that our proposed algorithm not only achieves higher precision and recall rates in random anomalies but also performs better in detecting anomalous traffic generated by high centrality structures compared to state of art algorithms based on matrix-based anomaly detection and tensor recovery methods.

Abstract The data center network HSDC is a superior candidate for building large-scale data centers, and strikes a good balance among diameter, bisection width, incremental scalability and other important characteristics in contrast to the state-of-the-art data center network architectures. The Hamiltonian property is an important indicator to measure the reliability of a network. In this paper, we study the Hamiltonian properties of HSDC’s logic graph $H_n$. Firstly, we prove that $H_n$ is Hamiltonian-connected for $n\geq 3$. Secondly, we propose an $O(NlogN)$ algorithm for finding a Hamiltonian path between any two distinct nodes in $H_n$, where $N$ is the number of nodes in $H_n$. Furthermore, we consider the Hamiltonian properties of $H_n$ with faulty elements, and prove that $H_n$ is $(n-3)$-fault-tolerant Hamiltonian-connected and $(n-2)$-fault-tolerant Hamiltonian for $n\geq 3$.

Abstract Reliability of a multiprocessor system becomes an important issue for parallel computing. Component diagnosability and component connectivity of a graph play crucial roles in assessing the vulnerability of an interconnection network, which are two significant indicators for the reliability and fault tolerance of a multiprocessor system. Until now, only a little knowledge of results have been known on $r$-component diagnosability and $r$-component connectivity. In this paper, we first propose the $r$-component diagnosability of $n$-dimensional alternating group graph $AG_{n}$ under PMC model. And then we promote our research on $AG_{n}$ by a fairly good construction for general $r$-component connectivity of $AG_{n}$, where $6\leq r\leq n-1$. The theoretical analysis and simulation show that the general $r$-component connectivity of $AG_{n}$ is larger than those of $Q_{n}$, $D_n$ and $FQ_{n}$.

Abstract Reconfigurable intelligent surface (RIS) has great potential in securing wireless transmission, and it can flexibly change the wireless communication environment. However, the employment of RIS increases the difficulty in beamforming of base station (BS), and existing schemes cannot be directly utilized to enhance the security of system. Therefore, a reinforcement learning framework to jointly control BS and RIS for the purpose of enhancing the secure performance is proposed. Specifically, the beamforming ia transformed with artificial noise and reflection control as a Markov decision problem (MDP) in complex domain. Then, we develop the deep deterministic policy gradient based on complex‐valued neural networks (CV‐DDPG) to imitate the rules of complex computation. Additionally, the CV‐DDPG is performed as the agent of reinforcement learning to control the BS and RIS, which enables better utilization of the implied phase information of complex values in the system. The simulation results show that the proposed model promotes the performance of the control and greatly improves the secrecy rate of the legitimate user.

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.

The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the r -component diagnosability ct r (G) of a network G imposes the restriction that the number of components is at least r in the remaining network of G by deleting faulty set X , which enhances the diagnosability of G . In this article, we establish the r -component diagnosability for n -dimensional hierarchical cubic network HCN n , and we show that, under both PMC model and MM* model, the r -component diagnosability of HCN n is rn -½( r -1) r +1 for n ≥ 2 and 1≤ r≤ n-1 . Moreover, we introduce the concepts of 0-PMC subgraph and 0-MM* subgraph of HCN n . Then, we make use of 0-PMC subgraph and 0-MM* subgraph of HCN n to design two algorithms under PMC model and MM* model, respectively, which are practical and efficient for component fault diagnosis of HCN n . Besides, we compare the r -component diagnosability of HCN n with the extra conditional diagnosability, diagnosability, good-neighbor diagnosability, pessimistic diagnosability, and conditional diagnosability, and we verify that the r -component diagnosability of HCN n is higher than the other types of diagnosability.

To secure communications, this article presents a key extraction scheme for smart devices using ambient sounds. Specifically, it is designed for the scenario when smart devices do not have any pre-loaded secrets. Moreover, it can be implemented when smart devices have no access to the online trusted third party or the Network Time Protocol server. In our scheme, smart devices achieve synchronization by making use of ambient sounds. Then, they calculate and obtain the pairing distance and secure distance by applying a band-pass filter. Completing these operations, two smart devices can directly extract a communication key. We analyze the security of our scheme and evaluate the performance of it by implementing the scheme using a few off-the-shelf devices. The experimental results indicate that compared with related schemes, the bit generation rate of our scheme has significant improvement (increases at least 80%), and it can reach 312 bit/s.

In order to evaluate the reliability of a multiprocessor system, the fault diagnosability was introduced and utilized as a significant indicator. In the study of fault diagnosability, researchers usually concentrate on the diagnosability of the global system but ignore its local information. However, the local information also plays a crucial role in the reliability of a multiprocessor system. Thus, an innovative concept of fault diagnosability, called$y$-fault-free-block local fault diagnosability, is put forward to study the fault diagnosability of a multiprocessor system at local vertex, where the$y$-fault-free-block condition requires more than$y$connected vertices. In this article, we characterize several important properties about the$y$-fault-free-block local fault diagnosability of a multiprocessor interconnection network under the MM* model and propose its$y$-fault-free-block local fault diagnosability at local vertex. Furthermore, we apply our results to some well-known networks, and we obtain their$y$-fault-free-block local fault diagnosabilities at local vertex directly under the MM* model, including bijective connection graph, star graph, and$(n,k)$-star graph. Finally, we compare the$y$-fault-free-block local fault diagnosability of a graph at local vertex with other types of diagnosability, including the diagnosability, conditional diagnosability, good-neighbor diagnosability, and pessimistic diagnosability. It can be seen that the$y$-fault-free-block local fault diagnosability at vertex is larger than all the other types of diagnosability.

The fault tolerance of a network can be measured by many parameters. Connectivity is a classic measurement parameter for evaluating the fault tolerance of a network. g-extra connectivity and g-restricted connectivity are generalizations of connectivity, which can better reflect the fault tolerance of a network. Specifically, the g-extra connectivity $\kappa_{g}(G)$ of a graph G is the minimum number of nodes whose removal will disconnect G, and each remaining component has no less than $g+1$ nodes. Furthermore, the g-restricted connectivity $\kappa^{g}(G)$ of G is the minimum number of nodes whose deletion results in a graph being disconnected and the minimum degree of each remaining component is at least g. In general, g-restricted connectivity is not equal to g-extra connectivity of a network. Therefore, many scholars often discuss g-restricted connectivity and g-extra connectivity with regard to different networks separately. In this paper, we show that g-restricted connectivity is equal to g-extra connectivity under some conditions. Then, the relationship we derived can be applied to some known networks such as the data center networks DCell and BCDC, multiprocessor network $(n,k)$-star. In addition, we construct a new network $H(G_{0},G_{1},G_{2};\mathbb{M})$ and prove that our result can be applied to it. In detail, we prove $\kappa^{g}(H(G_{0},G_{1},G_{2};\mathbb{M}))=\kappa_{g}(H(G_{0},G_{1},G_{2};\mathbb{M}))=n+g+1$ for any integers $n\geq 3$ and $ g\displaystyle \leq\lfloor\frac{n-2}{2}\rfloor$.

Abstract As the size of a multiprocessor system grows, the probability that faults occur in this system increases. One measure of the reliability of a multiprocessor system is the probability that a fault-free subsystem of a certain size still exists with the presence of individual faults. In this paper, we use the probabilistic fault model to establish the subgraph reliability for $AG_n$, the $n$-dimensional alternating group graph. More precisely, we first analyze the probability $R_n^{n-1}(p)$ that at least one subgraph with dimension $n-1$ is fault-free in $AG_n$, when given a uniform probability of a single vertex being fault-free. Since subgraphs of $AG_n$ intersect in rather complicated manners, we resort to the principle of inclusion–exclusion by considering intersections of up to five subgraphs and obtain an upper bound of the probability. Then we consider the probabilistic fault model when the probability of a single vertex being fault-free is nonuniform, and we show that the upper bound under these two models is very close to the lower bound obtained in a previous result, and it is better than the upper bound deduced from that of the arrangement graph, which means that the upper bound we obtained is very tight.

Users can obtain intelligent services by sharing information in social networks. Big data technologies can discover underlying benefits from this information. However, stringent security concern is raised at the same time. The public data can be utilized by adversaries, which will bring dire consequences. In this paper, the influence maximization problem is investigated in a privacy protection environment, which aims to find a subset of secure users that can make the spread of influence maximization and privacy disclosure minimization. At first, in order to estimate the risk level for each user, a Bayesian-based individual privacy risk evaluation model is proposed to rank the individual risk levels. Secondly, as the aim is to measure the influence capability for each user, a cascade influence capability evaluation model is designed to rank the friend influence capability levels. Finally, based on these two factors, a privacy protection method is designed for solving the influence maximization with attack constraint problem. In addition, the comparison experiments show that our method can achieve the goal of influence maximization and privacy disclosure minimization efficiently.

A human contact network (HCN) consists of individuals moving around and interacting with each other. In HCN, it is essential to detect malicious users who break the data delivery through terminating the data delivery or tampering with the data. Since malicious users will pay more but gain less when breaking the data delivery of opportunistic contacts, we focus on the non-opportunistic contacts that occur more frequently and stably. It is observed that people contact with each other more frequently if they have more social features in common. In this paper, we build up topology structure for HCN based on social features, and propose a graph theoretical comparison detection model to perform malicious users detection. Then we present an adaptive detection scheme based on Hamiltonian cycle decomposition. Also, we define comparison-0-string and comparison-1-string to improve the detection efficiency. Moreover, we perform scenario simulations on real data to realize the detected process of malicious users. Experiments show that, when the number of malicious users is bounded by the dimension of HCN, our scheme has a detection rate of 100% with both false positive rate and false negative rate being 0%, and the running cost is also very low when compared to baseline approaches. When the number of malicious users exceeds the bound, the detection rate of our scheme decreases slowly, while the false positive rate and false negative rate increase slowly, but they are still better than the baseline approaches.

Data integrity protection, an important feature in cloud storage services, can be achieved using auditing schemes. However, existing public and private auditing schemes are somewhat inefficient in practice. For example, in private auditing schemes, the trusted third-party is generally not able to settle disputes between users and the cloud storage server. In most existing public auditing schemes, it takes a user tens of seconds to generate data tags for every MB of file outsourced, which is clearly impractical particularly on resource-constrained end devices. Since the user is likely to have information than the auditor, we divide the verification phase into private verification and public verification phases. Then, we propose a public/private auditing model and a security model for public/private auditing. In our public/private auditing model, the user uses private verification phase to audit outsourced data promptly in most cases, and the auditor uses public verification phase to audit outsourced data only when dispute occurs or the user is not available to audit. Then, we propose a public/private auditing scheme, and present its security proof in the random oracle model under the discrete logarithm assumption. Experimental findings demonstrate that our scheme only need tens of microseconds to generate data tags for every MB file outsourced. In other words, the efficiency of our scheme is almost as high as existing high-performing private auditing schemes, and our scheme is more effective in comparison to existing efficient public auditing schemes.

Fault diagnosability is utilized as a significant measure that reflects the reliability of a multiprocessor system. However, people frequently pay close attention to the entire systems diagnosability while ignoring the systems important local information. The m-fault-free-neighbor local fault diagnosability (for short, m-FFNLFD) is a novel indicator, which describes the diagnosability of a system at a local node with m fault-free neighbors. In this paper, we propose the m-FFNLFD of general networks at local node under the Preparata Metze Chien model. Moreover, we also characterize some important properties of m-FFNLFD of a multiprocessor system under the comparison model. Furthermore, we apply our proposed conclusions to directly obtain the m-FFNLFD of 11 well-known networks under PMC-M and MM*-M, including hypercubes, locally twisted cubes, k-ary n-cubes, crossed cubes, twisted hypercubes, exchanged hypercubes, star graphs, (n, k)-star graphs, (n, k)-arrangement graphs, data center network DCells and BCDCs. Finally, we compare the m-FFNLFD with both diagnosability and conditional diagnosability, and it is shown that the m-FFNLFD is greater than all the other fault diagnosabilities.

Thet/kt/k-diagnosabilityandhh-extra connectivityare regarded as two important indicators to improve the network reliability. The t/k-diagnosis strategy can significantly improve the self-diagnosing capability of a network at the expense of no more thankfault-free nodes being mistakenly diagnosed as faulty. Theh-extra connectivity can tremendously improve the real fault tolerability of a network by insuring that each remaining component has no fewer than h+1 nodes. However, there is few result on the inherent relationship between these two indicators. In this article, we investigate the reason that caused the serious flawed results in (Liu, 2020), and we propose a diagnosis algorithm to establish the t/k-diagnosability for a large-scale connected networkGunder the PMC model by considering its h-extra connectivity. Let κh(G) be the h-extra connectivity of G. Then, we can deduce that G is κh(G)/h-diagnosable under the PMC model with some basic conditions. All κh(G)faulty nodes can be correctly diagnosed in the large-scale connected network G and at most h fault-free nodes would be misdiagnosed as faulty. The complete fault tolerant method adopts combinatorial properties and linearly many fault analysis to conquer the core of our proofs. We will apply the newly found relationship to directly obtain the κh(G)/h-diagnosability of a series of well known networks, including hypercubes, folded hypercubes, balanced hypercubes, dual-cubes, BC graphs, star graphs, Cayley graphs generated by transposition trees, bubble-sort star graphs, alternating group graphs, split-star networks, k-ary n-cubes and (n,k)-star graphs.

For interconnection network losing processors, it is considerable to calculate the number of vertices in the maximal component in the surviving network. Moreover, the component connectivity is a significant indicator for reliability of a network in the presence of failing processors. In this article, we first prove that when a set$M$of at most$3n-7$processors is deleted from an$n$-star graph, the surviving graph has a large component of size greater or equal to$n!-|M|-3$. We then prove that when a set$M$of at most$4n-9$processors is deleted from an$n$-star graph, the surviving graph has a large component of size greater or equal to$n!-|M|-5$. Finally, we also calculate the$r$-component connectivity of the$n$-star graph for$2\leq r\leq 5$.

The attackers in a network may have a tendency of targeting on a group of clustered nodes, and they hope to avoid the existence of significant large communication groups in the remaining network, such as botnet attack, DDoS attack, and Local Area Network Denial attack. Current various kinds of connectivity do not well reflect the fault tolerance of a network under these attacks. This observation inspires a new measure for network reliability to resist the block attack by taking into account of the dispersity of the remaining nodes. Let$G$be a network,$C\subset V(G)$and$G[C]$be aconnected subgraph. Then$C$is called an$h$h-faulty-blockof$G$if$G-C$is disconnected, and every component of$G-C$has at least$h+1$nodes. The minimum cardinality over all$h$-faulty-blocks of$G$is called$h$h-faulty-block connectivityof$G$, denoted by${FB}\kappa _h(G)$. In this article, we determine${FB}\kappa _h(Q_n)$for$n$-dimensional hypercube$Q_n$($n\geq 4$), a classic interconnection network. We establish that${FB}\kappa _h(Q_n)=(h+2)n-3h-1$for$0\leq h\leq 1$, and${FB}\kappa _h(Q_n)=(h+2)n-4h+1$for$2\leq h\leq n-2$, respectively. Larger$h$-faulty-block connectivity implies that an attacker will have to stage an attack to a bigger block of connected nodes, so that each remaining components will not be too small, which will in turn limit the size of large components. In other words, there will not be great disparity in sizes between any two remaining components, and hence there will less likely be a significantly large remaining communication group. The larger the$h$-faulty-block, the more difficult for an attacker to achieve that goal. As a consequence, the resistance of the network against the attacker will increase. Our experiments also show that as$h$increases, the$h$-faulty-block gets larger, and the size disparity between any two remaining components decreases. In turn, as expected, the size of the largest remaining communication group becomes smaller.

A network's diagnosability is the maximum number of faulty vertices that the network can discriminate solely by performing mutual tests among vertices. The original diagnosability without any condition is often rather low because it is bounded by the network's minimum degree. The h-extra fault diagnosability is an important and widely accepted diagnostic strategy as a new measure of diagnosability, which guarantees that the scale of every component is at least h+1 in the remaining system. Moreover, it increases the allowed faulty vertices, hence enhancing the diagnosability of the network. There have been lots of state-of-the-art literatures concerning the h-extra fault diagnosability. Although there are some methods to theoretically prove the extra fault diagnosability of some other well-known networks under MM* model, these methods have some serious flaws when there exists a 4-cycle in these networks. In this article, we investigate the reason that caused the flawed results in some references, and we derive a different, broadly applicable, and complete fault tolerant method to establish the extra fault diagnosability in an n-dimensional alternating group graph AGnunder MM* model. The complete fault tolerant method adopts combinatorial properties and linearly many fault analysis to conquer the core of our proofs. Moreover, we compare the extra fault diagnosability of AGnwith various types of fault diagnosability, including the diagnosability, strong diagnosability, conditional diagnosability, t/k-diagnosability, and pessimistic diagnosability. It can be seen that the extra fault diagnosability is greater than all the other types of fault diagnosability.

For interconnection network losing processors, usually, when the surviving network has a large connected component, it can be used as a functional subsystem without leading to severe performance degradation. Consequently, it is crucial to characterize the interprocessor communication ability and efficiency of the surviving structure. In this article, we prove that when a subset$D$of at most$6n-17$processors is deleted from an$n$-dimensional alternating group graph$\text{AG}_n$, there exists a largest component with cardinality greater or equal to$|V(\text{AG}_n)|-|D|-3$for$n\geq 6$in the remaining network, and the union of small components is, first, an empty graph; or, second, a 3-cycle, or an edge, or a 2-path, or a singleton; or, third, an edge and a singleton, or two singletons. Then, we prove that when a subset$D$of at most$8n-25$processors is deleted from$\text{AG}_n$, there exists a largest component with cardinality greater or equal to$|V(\text{AG}_n)|-|D|-5$for$n\geq 6$in the remaining network, and the union of small components is, first, an empty graph; or, second, a 5-cycle, or a 4-path, or a 4-claw, or a 4-cycle, or a 3-path, or a 3-claw, or a 3-cycle, or a 2-path, or an edge, or a singleton; or, third, a 4-cycle and a singleton, or a 3-path and a singleton, or a 3-claw and a singleton, or a 2-path and a singleton, two edges, an edge and a singleton, or two singletons; or, fourth, two edges and a singleton, or a 2-path and two singletons, or an edge and two singletons, or three singletons.

Multiple-input and multiple-output (MIMO) radars are vulnerable to deceptive jamming launched by false target generators that send jamming signals with the goal of pretending that the radar echo signals are reflected by faked targets. In this paper, we present a reinforcement learning based energy efficient power control scheme to detect deceptive jamming for frequency diverse array MIMO radars. This scheme enables a radar to choose the transmit power over the antennas without relying on the known deceptive jamming model. Instead, based on the emergency level, the battery level, the echo signal quality, the antenna phase differences, the received jamming power and the previous detection error rate, this scheme improves the detection accuracy and the energy efficiency, and uses a Dyna architecture to train the learning parameters with the simulated jamming detection experiences for faster optimization in the dynamic game against deceptive jamming. Simulation results show that this scheme effectively improves the deceptive jamming detection accuracy and saves the radar energy. Index Terms-MIMO, radar, reinforcement learning, deceptive jamming.

Summary The problem of influence maximization (IM) has become an important research topic due to the rapid growth of mobile social networks. It attempts to identify a set of nodes, referred to as influencers, contributing to the spread of maximum information. In this article, we present the construction of social relation graph based on mobile communication data. And we propose a new centrality measure—activity degree to characterize the activity of nodes. By combining the local attributes of nodes and the behavioral characteristics of nodes to measure node activity degree, which can be used to evaluate the influence of users in mobile social networks, we introduce Susceptible‐Infected‐Susceptible model to simulate the dynamic spreading of information. We take advantage of the two indicators the degree centrality and the betweenness centrality to get a better ranking results. In comparison with spanning graph and initial graph, the results of comparison demonstrate that our algorithm has advantages in the scope of influence propagation.

Cognitive radio networks (CRNs) are vulnerable to spoofing attacks due to their wireless and cognitive nature. Since the traditional cryptographic authentication can hardly prevent such attacks in CRNs, the physical‐layer authentication has been investigated for recent years. To achieve a light‐weight physical‐layer authentication, a rollout partially observable Markov decision process‐based algorithm, named RoPOMDP, is proposed in this study. In general, RoPOMDP formulates the physical‐layer authentication as a zero‐sum game, based on which a hypothesis test upon channel vectors is developed. That allows us to design the gains for both spoofers and receivers based on Bayesian risks for the game, in which the spoofing attack probability is predicted by a non‐linear function approximation utilising v‐support vector regression. Then, a RoPOMDP is employed to estimate the optimal threshold for the test statistic such that spoofing attacks can be detected. The theoretical analysis and simulations indicate that: (i) RoPOMDP improves the spoofing detection accuracy; (ii) as a light‐weight algorithm, the complexity of RoPOMDP is lower than contemporary ones.

The performance of physical layer authentication schemes strongly suffers from the uncertainties and dynamics of communications, which are mainly caused by the time-varying channels with unpredictable interference conditions. In this paper, we propose a multi-dimensional adaptive physical layer authentication scheme to achieve reliable authentication performance in time-varying environments. First of all, the fuzzy theory is explored for modeling multiple physical layer attributes with imperfectness and uncertainties. The designed fuzzy theory-based model is a parametric method that requires less observed samples of the utilized attributes together with less authentication system parameters to be determined compared with the nonparametric methods, demonstrating a compact authentication model. By deriving the false alarm rate and misdetection rate of the designed model, a hybrid learning-based adaptive authentication algorithm is proposed to near-instantaneously update system parameters, thereafter to adapt to the time-varying environment. Hence, our scheme is applicable to the communication environment with uncertainties and dynamics, resulting in a robust authentication scheme. Simulation results show that our solution can significantly improve the authentication performance in the time-varying environment. Compared with some exiting schemes, i.e., the optimal weights-based scheme and neural network-based scheme, our scheme achieves much better authentication performance.

A network’s diagnosability is the maximum number of faulty vertices that the network can discriminate solely by performing mutual tests among the vertices. It is an important measure of a network’s robustness. The g-good-neighbor conditional diagnosability is the maximum cardinality of g-good-neighbor conditional fault-set that the system is guaranteed to identify. The g-good-neighbor conditional diagnosability of EH(s,t) under the PMC model has been proposed by Liu et al. [Liu, X., Yuan, J. and Ma, X. (2014) The g-good-neighbor conditional diagnosability of the exchange hypercube under the PMC model. J. Taiyuan Univ. Sci. Technol., 35, 390–393]. However, the method by Liu et al. [Liu, X., Yuan, J. and Ma, X. (2014) The g-good-neighbor conditional diagnosability of the exchange hypercube under the PMC model. J. Taiyuan Univ. Sci. Technol., 35, 390–393] is too complicated to follow, and it is not complete. We will propose a complete method to establish the g-good-neighbor conditional diagnosability of EH(s,t) under the PMC model by optimizing the structure of the proof in [Liu, X., Yuan, J. and Ma, X. (2014) The g-good-neighbor conditional diagnosability of the exchange hypercube under the PMC model. J. Taiyuan Univ. Sci. Technol., 35, 390–393] and adding the missing case. Also we add a ratio in a table to represent the probability that a faulty set with size s contains all neighbors of any vertex, which is very low. Moreover, we mainly establish the g-good-neighbor conditional diagnosability for exchanged hypercube EH(s,t) under the comparison model.

The database-driven cognitive radio networks (CRNs) are regarded as a promising approach to utilizing limited spectrum resources in large-scale Internet of Things (IoT). However, database-driven CRNs face some security and privacy threats. Firstly, secondary users (SUs) should send identity and location information to the database (DB) to obtain a list of available channels, such that the curious DB might easily misuse and threaten the privacy of SUs. Secondly, malicious SUs might send fake location information to the DB in order to occupy channels with better quantity in advance and so gain benefits. This might also cause serious interference to primary users (PUs). In this paper, we propose a lightweight privacy-preserving location verification protocol to protect the identity and location privacy of each SU and to verify the location of SUs. In the proposed protocol, the SU does not need to provide location information to request an available channel from the DB. Therefore, the DB cannot get the location information of any SU. In the proposed protocol, the base station (BS) selects some SUs as witnesses to generate location proofs for each other in a distributed fashion. This new witness selection mechanism makes the proposed protocol reliable when a malicious SU generates fake location information to cheat the BS and also prevents SU-Witness collusion attacks. The results also show that the proposed protocol can provide strong privacy preservation for SUs and can effectively verify the location of the SUs. The security analysis shows that the proposed protocol can resist various types of attacks. Moreover, compared with previous protocols, the proposed protocol is lightweight because it relies on symmetric cryptography and it is unaffected by the area covered by the DB.

The h-extra node-connectivity of a graph G is the size of a minimal node-set, whose removal will disconnect G, but each remaining component has no fewer h + 1 nodes. Based on h-extra node-connectivity, the h-extra conditional fault-diagnosability of networks has been proposed for a better, more realistic measure of networks' fault-tolerability. It is the maximal x such that G is h-extra conditionally x-fault-diagnosable. This paper will establish a relationship between the h-extra node-connectivity and h-extra conditional fault-diagnosability for a regular graph G, under the classic PMC diagnostic model. We will apply the newly found relationship to a variety of well-known regular networks, to directly obtain their h-extra conditional fault-diagnosability. The significance of the paper's work is that it relates the notions of h-extra node-connectivity and h-extra conditional fault-diagnosability, so that a regular network's h-extra conditional fault-diagnosability may be known once its h-extra node-connectivity is known.

Received signal strength (RSS) in wireless networks is widely adopted for indoor positioning purpose because of its low cost and open access properties. However due to the sophisticated propagation of radio signals, the RSS shows a significant variation during pedestrian walking, which introduces critical errors in deterministic indoor positioning. To solve this problem, the authors present a novel method to improve the indoor pedestrian positioning accuracy by modelling fingerprinting and information on the movement into a hidden Markov models (HMMs). They divide the whole continuous positioning process into specified‐size sub‐processes, which could efficiently reduce the accumulative and resonance error caused by iterative estimation. They use an accelerometer sensor to provide the information on the movement distance to calculate the transition probability of the HMMs. In their experiments, they demonstrate that, compared with the deterministic pattern matching algorithm, the proposed method greatly improves the positioning accuracy and shows robust environmental adaptability.

With the development of cloud storage, how to protect the integrity of the data stored in the cloud becomes the clients’ major concern. Recently, Zhang et al. proposed a certificateless data integrity auditing scheme for cloud-assisted cyber-physical-social systems. However, we find that their scheme has some security flaws, i.e., if a client outsources a file with$m$blocks data and tags, the cloud server can store only one block of them, and pass the integrity auditing of the auditor. We refer to this attack as storing one block attack. Then, we give an improved scheme to amend these flaws.

A network's diagnosability is the maximum number of faulty vertices the network can discriminate solely by performing mutual tests among the vertices. It is an important measure of a network's robustness. The original diagnosability without any condition is often rather low because it is bounded by the network's minimum degree. Several conditional diagnosability have been proposed in the past to increase the allowed faulty vertices, and hence enhancing the diagnosability of the network. The g-good-neighbor conditional diagnosability is the maximum number of faulty vertices a network can guarantee to identify, under the condition that every fault-free vertex has at least g fault-free neighbors (i.e., good neighbors). In this paper, we establish the g-good-neighbor conditional diagnosability for the (n; k)-arrangement graph network An;k. We will show that, under both the PMC model and the comparison model, the An;k's g-good-neighbor conditional diagnosability is [(g + 1)k - g](n - k), which can be several times higher than the An;k's original diagnosability.

In this paper, a coordinated multiple-relays-based cooperative communication scheme is proposed to improve the physical-layer security. In order to benefit the relays in forwarding the signals for defending against the eavesdropping attacks, the interactions between the source and the multiple relays are modeled as a single-leader multiple-followers Stackelberg game. The source plays as the leader to coordinate the relays, including the phase of signals forwarded by the relays and the transmit power of the relays, for maximizing the secrecy capacity of the system. An algorithm is developed for the relays to find an optimal price allocation to achieve the fairness among the multiple relays based on the egalitarian welfare solution, and an approximate optimal strategy of source (i.e., phase coordinated vector and power allocation) is studied. The closed-form intercept probability of the proposed scheme is derived. Numerical studies demonstrate that the proposed scheme can greatly improve the utilities of both the source and multiple relays over that resulted from the Nash equilibrium scheme and rand scheme, which means that the relays are more willing to participate in the cooperative communication, and the source can achieve better secure transmission based on the proposed scheme. It is also shown that the proposed scheme performs much better in defending against the eavesdropping attacks than those existing schemes, for example, the single relay selection scheme, the opportunistic relay selection scheme, the optimal relay scheme, and cooperative jamming scheme.

The g-restricted connectivity (g-RC) is the minimum vertex-set size of a network, whose deletion disconnects the network such that each remaining vertex has at least g neighbors in its respective component. The g-RC is a deterministic indicator of tolerability of a network with failing processors. The g-good-neighbor fault diagnosability (g-GNFD) is the largest set size of correctly identified faulty vertices in a network such that any good vertex has no fewer g good neighbors. This paper establishes the relationship between g-RC and g-GNFD of general regular networks, first under the PMC model and second under the MM* model. Moreover, this paper directly gives the g-GNFD of some well-known special networks by their g-RC and our proposed relationship.

Proximity detection is one of the critical components in Location-based Social Networks (LBSNS), which has attracted much attention recently. With the advent of LBSNS, more and more users' location information will be collected by the service providers. However, with a potentially untrusted server, such a proximity detection service may threaten the privacy of users. In this paper, aiming at achieving enhanced privacy against the untrusted service providers in LBSNS, we introduce a new architecture with dual-servers for the first time and propose a privacy-preserving proximity detection method based on Geohash. In our architecture, the location coordinates of users are converted into a bit-string by dichotomy approximation, and divided into two subsets: prefix and suffix. The social network server firstly selects the candidate neighbors only in the light of the prefix, and then a third-party server is introduced to compute the relative distance of candidate neighbors according to the suffix. Each server can only get a subset of location code, instead of the whole location information of users as the previous work. We also prove that the new construction is secure under the untrusted server model with enhanced privacy. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction.

brings together practitioners, researchers, and scholars from the field of parallel and distributed computing to facilitate the exchange of ideas, enable collaborations, and promote the development of new research directions.

Electronic healthcare (eHealth) systems have replaced traditional paper-based medical systems due to attractive features such as universal accessibility, high accuracy, and low cost. As a major constituent part of eHealth systems, mobile healthcare (mHealth) applies Mobile Internet Devices (MIDs) and Embedded Devices (EDs), such as tablets, smartphones, and other devices embedded in the bodies of individuals, to improve the quality of life and provide more convenient healthcare services for patients. Unfortunately, MIDs and EDs have only limited computational capacity, storage space, and power supply. By taking this into account, we present a new design to guarantee the integrity of eHealth records and the anonymity of the data owner in a more efficient and flexible way. The essence of our design is a general method which can convert any secure Attribute-Based Signature (ABS) scheme into a highly efficient and secure Online/Offline Attribute-Based Signature (OOABS) scheme. We prove the security and analyze the efficiency improvement of the new design. Additionally, we illustrate the proposed generic construction by applying it to a specific ABS scheme.

Various services such as location-based service (LBS) allow mass collection of spatio-temporal data because the ubiquity of cheap embedded sensors on smart phones. Therefore, the individual privacy-preserving is receiving increasing attention during the data publication. However, the inherent inaccuracy of data acquisition equipments, sampling error and low sampling rate may lead to uncertainty. In this paper, we propose a privacy-preserving approach for trajectory publication with considering the uncertainty in trajectory. The correlation between two trajectories are computed according to the temporal overlap similarity, the trajectory direction similarity and the distance between trajectories with uncertainty. Then a greedy algorithm is proposed to achieve k-anonymity based on graph partition. The analysis and experiment evaluations based on the GeoLife trajectory data set show that significant privacy and QoS benefits can be achieved.

Summary With the popularity of mobile smart devices and functional diversification, the infection ways of mobile smartphones worm also become diverse. In mobile networks, mobile devices are suffering from the threat of worms all the time. Once the worm outbreaks, it will bring huge losses to mobile phone users and mobile network operators. Researches on mobile worm propagation model allow us to control and detect potential worm threat, according to the characteristics of worm's outbreak. In the paper, we put forward a worm propagation model based on the mobile network environment. After analyzing the model, we then give the simulation for controlling factors affecting worm propagation. This model allows us to have a certain understanding for the spread on the size and speed of the mobile worm and provide effective methods to control the spread of the mobile worm. Copyright © 2015 John Wiley & Sons, Ltd.

Summary The era of big data is here now, and spectrum resources are increasingly scarce in heterogeneous network environment. The spectrum efficiency and secure transmission of big data are important issues. Cognitive radio has been proposed to address the issue of spectrum efficiency, and is a hot topic in the literatures. In multi‐hop cooperative cognitive radio networks (CCRNs), secondary users need the primary users' authorization to be relays. Most existing centralized route selection schemes ignore the energy allocation, and thus are inefficient. Moreover, the incomplete of information in multi‐hop network leads to many difficulties in cooperation. Inspired by the game theory, a novel strategy is proposed in this paper to defend against insider attacks based on trust. This strategy is denoted as secure routing and resource allocation based on game theory in CCRNs (SRGC). With a reputation updating process and distributed learning algorithm, the proposed strategy can find a ‘best’ route, which is relatively safe for each primary transmitter, and at the same time fully utilizes the spectrum and energy. Using NS2, simulations indicate that SRGC can well fit into CCRNs, improve the network performance and defend against the routing disruption attacks. Compared with other schemes, the SRGC results in a performance with better adaptability to the distributed environment. Moreover, SRGC can maximize the average throughput and minimize the data drop ratios. Copyright © 2015 John Wiley & Sons, Ltd.

In order to fully utilise limited spectrum resources of multiple channels and multiple radios in cognitive radio networks, the authors propose a potential game model for opportunistic spectrum access based on both accurate and inaccurate spectrum state estimation with considering the interference constraints of licensed users. Three algorithms are proposed to achieve equilibrium of the proposed game. First, assuming spectrum sensing results are accurate, a joint strategy fictitious play‐based channel selection algorithm with incomplete information is presented, and it can achieve a pure Nash equilibrium (NE) of the proposed game. Second, in order to make the outcomes of game robust, an evolutionary spectrum access mechanism with complete information is introduced by using evolutionary game theory based on inaccurate spectrum state estimation so that evolutionary stable strategy (ESS) can be achieved. Finally, with incomplete network information, a distributed learning algorithm is proposed to achieve a mixed NE, which is proved to be an ESS. Simulation results show that these algorithms can significantly improve spectrum allocation efficiency while reducing mutual collision.

The$t/k$-diagnosis strategy can significantly enhance the system’s self-diagnosing capability at the expense of no more than$k$fault-free processors (vertices) being mistakenly diagnosed as faulty under the PMC model. It is a generalization of the precise and pessimistic diagnosis strategies of system-level diagnosis on multiprocessor systems. It can detect up to$t$faulty processors (vertices) which might include at most$k$misdiagnosed processors (vertices), where$k$is typically a small number. In the case$k\ge 1$, to our knowledge, there is no known$t/k$-diagnosis algorithm for general regular networks. In this paper, we first propose a general$t/k$-diagnosis ($k\ge 1$) algorithm for some$m$-regular networks. These$m$-regular networks satisfying some conditions could establish the$t/k$-diagnosis algorithm, say$t/k$-$G$-$DIAG$, to determine the$t/k$-diagnosability. The complexity of this algorithm is only$O(N\log N)$(when$N\ge 2^m$) or$O(Nm)$(when$N< 2^m$) where$N$is the number of vertices in the network. Second, we present a complete proof that the network$G$is actually$t/k$-diagnosable. Finally, we establish the$t/k$-diagnosability ($1\le k\le 3$) of some regular networks, including an$n$-dimensionalalternating group graph, an$n$-dimensionalSplit-Star Network, a$l^n$-hypermeshand an$(n,l)$-star graph, which are well-known interconnection networks proposed for multiprocessor systems.

Connectivity is a classic measure for fault tolerance of a network in the case of vertices failures. Extra connectivity and restricted connectivity are two important indicators of the robustness of a multi-processor system in presence of failing processors. An interconnection network's diagnosability is an important measure of its self-diagnostic capability. The conditional diagnosability is widely accepted as a new measure of diagnosability by assuming that any fault-set cannot contain all neighbors of any node in a multiprocessor system. In this paper, we analyze the combinatorial properties and fault tolerance ability for the Split-Star Network, denoted by Sn2, a well-known interconnection network proposed for multiprocessor systems, establish the g-extra connectivity, where 1 ≤ g ≤ 3. We also determine the h-restricted connectivity (h = 1; 2), and prove that the conditional diagnosability of Sn2(n ≥ 4) is 6n - 16 under the comparison model, which is about three times of the Sn2's traditional diagnosability. As a product, the strong diagnosability of Sn2is also obtained.

Extra connectivity is an important indicator of the robustness of a multiprocessor system in presence of failing processors. The g-extra conditional diagnosability and the t/m-diagnosability are two important diagnostic strategies at system-level that can significantly enhance the system's self-diagnosing capability. The g-extra conditional diagnosability is defined under the assumption that every component of the system removing a set of faulty vertices has more than g vertices. The t/m-diagnosis strategy can detect up to t faulty processors which might include at most m misdiagnosed processors, where m is typically a small integer number. In this paper, we analyze the combinatorial properties and fault tolerant ability for an (n, k)-arrangement graph, denoted by An,k, a well-known interconnection network proposed for multiprocessor systems. We first establish that the An,k's one-extra connectivity is (2k - 1) (n - k) - 1 (k ≥ 3, n ≥ k + 2), two-extra connectivity is (3k - 2)(n - k) - 3 (k ≥ 4, n ≥ k + 2), and three-extra connectivity is (4k - 4)(n - k) - 4 ( k ≥ 4, n ≥ k + 2 or k ≥ 3, n ≥ k + 3), respectively. And then, we address the g-extra conditional diagnosability of An,kunder the PMC model for 1 ≤ g ≤ 3. Finally, we determine that the (n, k)-arrangement graph An,kis [(2k - 1)(n - k) - 1]/1-diagnosable (k ≥ 4, n ≥ k + 2), [(3k - 2)(n - k) - 3]/2-diagnosable (k ≥ 4, n ≥ k + 2), and [(4k - 4)(n - k) - 4]/3-diagnosable (k ≥ 4, n ≥ k + 3) under the PMC model, respectively.

Multi-hop wireless networks (MWNs) have been widely accepted as an indispensable component of next-generation communication systems due to their broad applications and easy deployment without relying on any infrastructure. Although showing huge benefits, MWNs face many security problems, particularly the internal multi-layer security threats being one of the most challenging issues. Since most security mechanisms require the cooperation of nodes, characterizing and learning actions of neighboring nodes and the evolution of these actions over time is vital to constructing an efficient and robust solution for security-sensitive applications such as social networking, mobile banking and teleconferencing. In this paper, we propose a new dynamic Cross-layer Reputation computation Model (CRM) to dynamically characterize and quantify actions of nodes. CRM couples an uncertainty-based conventional layered reputation computation model (RCM) with cross-layer design and multi-level security technology to identify malicious nodes and preservation of security against internal multi-layer threats. Simulation results and performance analyses demonstrate that CRM can provide rapid and accurate malicious node identification and management, and implement the preservation of security against the internal multi-layer and bad-mouthing attacks more effectively and efficiently than existing models.

Data sharing has never been easier with the advances of cloud computing, and an accurate analysis on the shared data provides an array of benefits to both the society and individuals. Data sharing with a large number of participants must take into account several issues, including efficiency, data integrity and privacy of data owner. Ring signature is a promising candidate to construct an anonymous and authentic data sharing system. It allows a data owner to anonymously authenticate his data which can be put into the cloud for storage or analysis purpose. Yet the costly certificate verification in the traditional public key infrastructure (PKI) setting becomes a bottleneck for this solution to be scalable. Identity-based (ID-based) ring signature, which eliminates the process of certificate verification, can be used instead. In this paper, we further enhance the security of ID-based ring signature by providing forward security: If a secret key of any user has been compromised, all previous generated signatures that include this user still remain valid. This property is especially important to any large scale data sharing system, as it is impossible to ask all data owners to re-authenticate their data even if a secret key of one single user has been compromised. We provide a concrete and efficient instantiation of our scheme, prove its security and provide an implementation to show its practicality.

The${{t/k}}$-diagnosis is a diagnostic strategy at system level that can significantly enhance the system’s self-diagnosing capability. It can detect up to${{t}}$faulty processors (or nodes, units) which might include at most${{k}}$misdiagnosed processors, where${ {k}}$is typically a small number. Somani and Peleg (, 1996) claimed that an$n$-dimensional Star Graph (denoted${{S_n}}$), a well-studied interconnection model for multiprocessor systems, is${{((k + 1)n - 3k - 2)/k}}$-diagnosable. Recently, Chen and Liu (, 2012) found counterexamples for the diagnosability obtained in, without further pursuing the cause of the flawed result. In this paper, we provide a new, complete proof that an${\mbi {n}}$-dimensional Star Graph is actually${{((k + 1)n - 3k - 1)/k}}$-diagnosable, where${{1 \leq k \leq 3}}$, and investigate the reason that caused the flawed result in. Based on our newly obtained fault-tolerance properties, we will also outline an${ {O(N \log N)}}$diagnostic algorithm (${ {N = n!}}$is the number of nodes in${{S_n}}$) to locate all (up to${ {(k + 1)n - 3k - 1}}$) faulty processors, among which at most${ {k\, (1 \leq k \leq 3)}}$fault-free processors might be wrongly diagnosed as faulty.

In vehicle-to-grid (V2G) networks, service providers are battery-powered vehicles, and the service consumer is the power grid. Security and privacy concerns are major obstacles for V2G networks to be extensively deployed. In 2011, Yang et al. proposed a very interesting privacy-preserving communication and precise reward architecture for V2G networks in smart grids. In this paper, we enhance Yang et al.'s framework with the formal definitions of unforgeability and restrictiveness. Then, we propose a new traceable privacy-preserving communication and precise reward scheme with available cryptographic primitives. The proposed scheme is formally proven secure with well-established assumptions in the random oracle model. Thorough theoretical and experimental analyses demonstrate that our scheme is efficient and practical for secure V2G networks in smart grids.

Extra connectivity, diagnosability, and conditional diagnosability are all important measures for a multiprocessor system's ability to diagnose and tolerate faults. In this paper, we analyze the fault tolerance ability for the alternating group graph, a well-known interconnection network proposed for multiprocessor systems, establish the h-extra connectivity, where 1 ≤ h ≤ 3, and prove that the conditional diagnosability of an n-dimensional alternating group graph, denoted by AGn, is 8n - 27 (n ≥ 4) under the PMC model. This is about four times of the AGn's traditional diagnosability. As a byproduct, the strong diagnosability of AGnis also obtained.

As the size of a multiprocessor computer system grows, the probability of having faulty (i.e., malfunctioning or failing) processors in the system increases. It is then important to quantify how the faults collectively affect the entire system. The reliability of subsystems in a system, defined as the probability that a fault-free subsystem of a certain size still exists when the system has faults, is a measure for the faults' effect on the whole system. It can be used as an indicator of system health. In this paper, we will present two schemes to calculate the reliability of an$(n-1,k-1)$-subgraph in the$(n,k)$-Arrangement Graph$A_{n,k}$, an extensively studied interconnection network proposed for multiprocessor computers. The first scheme will use a probability fault model and the Principle of Inclusion-Exclusion to establish an upper-bound of the reliability, by taking into account the intersection of not more than three subgraphs. The second scheme uses basically the same idea, but completely neglects the intersection among subgraphs to calculate an approximate reliability. The results of the two schemes are compared, and are shown to be in good agreement, especially as the single-node reliability$p$goes low.

SUMMARY This paper presents two routing games, a unicast and a multicast routing game, for wireless sensor networks. We analyze the actions of nodes inside/outside lowest cost path (LCP) and draw their payoff functions. Our simulation shows that this evolutionary game theory scheme has several advantages over a widely used collusion–resistant routing scheme. All nodes, either out of LCP or in LCP, will ultimately choose the strategy ‘transmit’. To improve the payoff, nodes in LCP should either minimize their actual forwarding cost or maximize their claimed cost as long as it remains in the LCP, whereas minimizing the actual claimed cost is not an optimal option for nodes out of LCP. Copyright © 2013 John Wiley & Sons, Ltd.

Modern computing systems have become more easy-to-use, sophisticated and powerful, and have dramatically changed the way we live. However, the same systems add complexity and also introduce new interdependencies, vulnerabilities and privacy issues. There will be more ways to disrupt our life through cyber attacks. It is thus of great importance to consider and develop methods to mitigate those security risks. This special issue presents the recent advances on the security of new generation computing systems including distributed, cloud, and grid systems. We are pleased to present to you nine technical papers dealing with cutting-edge research and technology related to this topic. These papers were selected out of the significantly extended versions of the 173 submissions from 28 countries in the 6th International Conference on Network and System Security (NSS 2012) and a large number of open submissions. The selection has been very rigorous, and only the best papers were selected.

ABSTRACT A cognitive wireless mesh (CogMesh) network can provide network access for users (mesh clients) while holding the open spectrum‐sharing feature. It combines the advantages of both cognitive radio networks and wireless mesh networks: high utilization of the scarce radio spectrum, ease of deployment, maintenance, and low cost. However, the unstable radio environment incurs more handoffs in CogMesh than other wireless networks. Therefore, seamless handoff and a lightweight re‐authentication process are required to achieve a high quality of real‐time applications and low network load caused by re‐authentication. In this paper, we introduce a local joint fast handoff scheme based on proxy signature, which is suitable for CogMesh scenarios. After the mutual authentication between a mesh client and a mesh router, a session tunnel key is derived and shared between them to protect their sessions. Because the proxy signature is used, the IEEE 802.1x authentication architecture remains the same, and there is no need for the authentication server to be involved in the handoff procedure, the re‐authentication delay has been reduced, and the load of CogMesh and the authentication server are lower. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Traffic anomalies caused by Distributed Denial‐of‐Service (DDoS) attacks are major threats to both network service providers and legitimate customers. The DDoS attacks regularly consume and exhaust the resources of victims and hence result in abnormal bursty traffic through end‐user systems. Additionally, malicious traffic aggregated into normal traffic often show dramatic changes in the traffic nature and statistical features. This study focuses on early detection of traffic anomalies caused by DDoS attacks in light of analyzing the network traffic behavior. Key statistical features including variance, autocorrelation, and self‐similarity are employed to characterize the network traffic. Further, artificial neural network and support vector machine subject to the performance metrics are employed to predict and classify the abnormal traffic. The proposed diagnosis mechanism is validated through experiments where the datasets consist of two groups. The first group is the Massachusetts Institute of Technology Lincoln Laboratory dataset containing labeled DoS attack. The second group collected from DDoS attack simulation experiments covers three representative traffic shapes resulting from the dynamic attack rate configuration, namely, constant intensity, ramp‐up behavior, and pulsing behavior. The experimental results demonstrate that the developed mechanism can effectively and precisely alert the abnormal traffic within short response period. Copyright © 2013 John Wiley & Sons, Ltd.

In this article, we present a novel distributed separate coding (DSC) scheme for continuous data collection in wireless sensor networks with a mobile base station (mBS). By separately encoding a certain number of data segments in a combined segment and doing decoding-free data replacement in the buffers of each sensor node, the DSC scheme is shown as an efficient method for continuously collecting data segments with a high success ratio. The proposed DSC scheme has a salient feature: with a minimum buffer size 2 in each sensor node, by querying any m −1 sensor nodes, the mBS can reconstruct the m latest data segments with high probability, where m is the number of latest data segments in a time interval t in which n ( t ) ( m ≤ n ( t )) data segments are generated. The necessary storage space in each sensor node can be adjusted by changing the number of sensor nodes queried by the mBS. Furthermore, the transmission cost for data submission to the mBS can be reduced with some additional storage space in each sensor node. The comprehensive performance evaluation has been conducted through computer simulation. It is shown that the proposed DSC scheme outperforms the existing scheme significantly.

This paper initiates the study of two specific security threats on smart-card-based password authentication in distributed systems. Smart-card-based password authentication is one of the most commonly used security mechanisms to determine the identity of a remote client, who must hold a valid smart card and the corresponding password to carry out a successful authentication with the server. The authentication is usually integrated with a key establishment protocol and yields smart-card-based password-authenticated key agreement. Using two recently proposed protocols as case studies, we demonstrate two new types of adversaries with smart card: 1) adversaries with pre-computed data stored in the smart card, and 2) adversaries with different data (with respect to different time slots) stored in the smart card. These threats, though realistic in distributed systems, have never been studied in the literature. In addition to point out the vulnerabilities, we propose the countermeasures to thwart the security threats and secure the protocols.

Due to the nature of wireless transmission, communication in wireless mesh networks (WMNs) is vulnerable to many adversarial activities including eavesdropping. Pair-wise key establishment is one of the fundamental issues in securing WMNs. This paper presents a new matrix-based pairwise key establishment scheme for mesh clients. Our design is motivated by the fact that in WMNs, mesh routers are more powerful than mesh clients, both in computation and communication. By exploiting this heterogeneity, expensive operations can be delegated to mesh routers, which help alleviate the overhead of mesh clients during key establishment. The new scheme possesses two desirable features: (1) Neighbor mesh clients can directly establish pairwise keys; and (2) Communication and storage costs at mesh clients are significantly reduced.

Using partial observable Markov decision process(POMDP) and game theoretic solutions, we investigate the problem of achieving global optimization for distributed channel selections in cognitive radio networks (CRNs). In order to fully utilize the scarce spectrum resources, we propose two special cases to study the dynamic spectrum access. Firstly, the channel state prediction based on POMDP could reduce the collision of SUs with PUs, Secondly, a potential game(PG) theoretic framework and joint strategy fictitious play(JSFP) have been proposed to determine the access probability of SU, which could reduce the collision with other SUs. It is shown that with the proposed cases, global optimization has been achieved with local information. Specifically, the strategy with two cases mentioned above maximizes the network throughput and minimizes the network collision level. Meanwhile, the JSFP, which works to provides strong guarantees on the resulting asymptotic behavior, is proposed to achieve the global optimum autonomously and rapidly. Simulation results show that the proposed scheme can greatly improve the spectrum efficiency by alleviating mutual collision.

Due to the attractive characteristics of high capacity, high-speed, wide coverage and low transmission power, Wireless Mesh Networks become the ideal choice for the next-generation wireless communication systems. However, the network congestion of WMNs deteriorates the quality of service provided to end users. Game theory optimization model is a novel modeling tool for the study of multiple entities and the interaction between them. On the other hand, cross-layer design is shown to be practical for optimizing the performance of network communications. Therefore, a combination of the game theory and cross-layer optimization, named cross-layer game optimization, is proposed to reduce network congestion in WMNs. In this paper, the network congestion control in the transport layer and multi-path flow assignment in the network layer of WMNs are investigated. The proposed cross-layer game optimization algorithm is then employed to enable source nodes to change their set of paths and adjust their congestion window according to the round-trip time to achieve a Nash equilibrium. Finally, evaluation results show that the proposed cross-layer game optimization scheme achieves high throughput with low transmission delay.

In this paper, we present a novel Group Data Collection (GDC) scheme for wireless sensor networks with a mobile base station by using coding for data storage in the sensor nodes. By separately encoding a certain number of data segments in a combined segment and doing data replacement in each sensor node, the proposed GDC scheme not only provides an efficient storage method for group data, but also achieves a high success ratio of data collection. The number of necessary buffers in each sensor node can be adjusted by changing the frequency of performing data collection. The performance evaluation has been conducted through comprehensive computer simulations. It further demonstrates the feasibility and superiority of the proposed GDC scheme.

SUMMARY Supported by the IEEE 802.15.4 standard, embedded sensor networks have become popular and been widely deployed in recent years. The IEEE 802.15.4 medium access control (MAC) protocol is uniquely designed to meet the desirable requirements of the low end‐to‐end delay, low packet loss, and low power consumption in the low rate wireless personal areas networks (LR‐WPANs). This paper develops an analytical model to quantify the key performance metrics of the MAC protocol in LR‐WPANs with bursty ON–OFF traffic. This study fills the gap in the literature by removing the assumptions of saturated traffic or nonbursty unsaturated traffic conditions, which are unable to capture the characteristics of bursty multimedia traffic in sensor networks. This analytical model can be used to derive the QoS performance metrics in terms of throughput and total delay. The accuracy of the model is verified through NS‐2 ( http://www.isi.edu/nsnam/ns/ ) simulation experiments. This model is adopted to investigate the performance of the MAC protocol in LR‐WPANs under various traffic patterns, different loads, and various numbers of stations. Numerical results show that the traffic patterns and traffic burstiness have a significant impact on the delay performance of LR‐WPANs. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY With the development of wireless mesh networks (WMNs), fault diagnosis in WMNs is becoming a very challenging task. In this paper, a two‐level scheme for fault diagnosis in WMNs is presented. We partition the network into a two‐level topology architecture where level 1 is composed of mesh clients and level 2 consists of mesh routers. A new comparison approach is introduced to diagnose the two levels. On the basis of the new comparison approach, every node in WMNs can be diagnosed either as fault‐free or faulty. Our protocol assumes that the WMN's topology may change during the testing phase and utilize the shortest path spanning tree, which is constructed along with the process of fault diagnosis, to disseminate local messages and global messages in WMNs. The proposed model is only for static fault circumstances. We provide the analysis of correctness, communication complexity, and time complexity of our protocol, and the comparison between our protocol and others through both theoretical proof and practical simulation. The analysis shows that our model has significant advantages over other existing models. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless Mesh Networks (WMNs) have drawn much attention for emerging as a promising technology to meet the challenges in next generation networks. Security and privacy protection have been the primary concerns in pushing the success of WMNs. However, the solutions proposed to ensure the security of the routing protocol and the privacy information in WMNs are still not robust. In this paper, we propose a role based privacy-aware secure routing protocol (RPASRP), which combines a new dynamic reputation mechanism with the role based multi-level security technology and a novel hierarchical key management protocol to defend against the internal attacks and to achieve better security and privacy protection. Simulation results show that RPASRP implements the security and privacy protection against the inside attacks more effectively and efficiently and performs better than the classical hybrid wireless mesh protocol (HWMP) in terms of packet delivery ratio.

Distributed Denial-of-Service (DDoS) attacks are critical threats to both network service providers and legitimate network users. DDoS attacks often overwhelm or exhaust the resources of victims and typically result in abnormal bursty traffic passing through victim systems. In this paper, we develop a mechanism for diagnosing traffic anomalies caused by DDoS attacks on the basis of analyzing the behaviour of network traffic. The traffic in communication networks has been shown to exhibit statistical self-similar phenomena that can be characterized by the so-called Hurst parameter. Therefore, in the proposed mechanism the Hurst parameter coupled by variance and autocorrelation are employed as the key performance metrics to spot the anomalies of network traffic. The proposed diagnosis mechanism is validated through experiments where the datasets consist of two groups. The first group is obtained from the MIT Lincoln Laboratory DOS attack dataset. The second group is collected from our DDoS attack simulation experiments, which cover three representative traffic shapes resulting from three different DDoS attack behaviours, namely, constant intensity, ramp-up behaviour and pulse behaviour. The experimental results show that the developed mechanism can alert the DDoS attack schemes within short respond time.

In this paper, we present a novel data collection scheme for wireless sensor networks by using separate network coding (SNC). By separately encoding a certain number of data segments in a combined data segment and doing decoding-free data replacement, SNC not only provides efficient storage method for continuous data, but also maintains a high success ratio of data collection. The performance evaluation has been conducted through comprehensive computer simulation. It is shown that SNC outperforms the exiting scheme significantly.

Certificate-based encryption (CBE) and certificateless encryption (CLE) are proposed to lessen the certificate management problem in a traditional public-key encryption setting. Although they are two different notions, CBE and CLE are closely related and possess several common features. The encryption in CBE and CLE does not require authenticity verification of the recipient public key. The decryption in both notions requires two secrets that are generated by the third party and the public key owner, respectively. Recently a generic conversion from CLE to CBE was given, but unfortunately its security proof is flawed. This paper provides an elaborate security model of CBE, based on which a provably secure generic construction of CBE from CLE is proposed. A concrete instantiation is also presented to demonstrate the application of our generic construction.

The IEEE 802.15.4 standard defines physical layer and Medium Access Control (MAC) layer protocols for the Low Rate Wireless Personal Areas Network (LR-WPAN). The analytical models of 802.15.4 MAC have been primarily developed under the assumptions of the ideal channels or uniform error channels which fail to capture the characteristics of bursty and correlated channel errors in the practical wireless network environment. In this paper, we propose an analytical model for 802.15.4 MAC in LR-WPAN in the presence of bursty error channels. This model can be adopted to obtain the Quality-of-Service (QoS) performance metrics in terms of throughput, service time, and total delay. Utilizing the analytical model, we investigate the QoS performance of 802.15.4 MAC under various traffic loads, backoff parameters, numbers of stations, and channel conditions.

MANET (mobile ad hoc network) is a collection of wireless mobile nodes forming a temporary communication network without the aid of any established infrastructure. Because mobile nodes are typically constrained by power and computing resources, a selfish node may not be willing to use its computing and energy resources to forward packets that are not directly beneficial to it, even though it expects others to forward packets on its behalf. This paper not only analyzes the effect of two typical kinds of selfish nodes through simulation methods, but also proposes resolving strategy respectively. For type 1 selfish node, this paper proposes CI-DSR (cooperation inspirited dynamic source routing) protocol, which introduces an objective reputation-based strategy into the DSR protocol. For type 2 selfish nodes, a self-saving energy strategy is proposed. Simulations indicate that both of two strategies can effectively tradeoff the selfishness and cooperation