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Unmanned aerial vehicle (UAV) and network function virtualization (NFV) facilitate the deployment of multi-access edge computing (MEC). In the UAV-based MEC (UMEC) network, virtualized network function (VNF) can be implemented as a lightweight container running on UMEC host operating system (OS). However, UMEC network is vulnerable to attack, which can result in resource degradation and even UMEC service disruption. Rejuvenation techniques, such as failover technique and live container migration technique, can mitigate the impact of resource degradation but their effectiveness to improve the resilience of UMEC services should be evaluated. This paper presents a quantitative modeling approach based on semi-Markov process to investigate the resilience of a UMEC service chain consisting of any number of VNFs executed in any number of UMEC hosts in terms of availability and reliability. Unlike existing studies, the semi-Markov model constructed in this paper can capture the time-dependent behaviors between VNFs, between host OSes, and between VNFs and host OSes on the condition that the holding times of the recovery and failure events follow any kind of distribution. We perform the sensitivity analysis to identify potential resilience bottlenecks. The results highlight that migration time is the parameter significantly affecting the resilience, which shed the insight on designing the UMEC service chain with high-grade resilience requirements. In addition, we carry out the numerical experiments to reveal that: (i) the type of failure time distribution has a significant effect on the resilience; and (ii) the resilience increases with decreasing number of VNFs, while the availability increases with increasing number of UMEC hosts and the reliability decreases with increasing number of UMEC hosts, which can provide meaningful guidance for the UAV placement optimization in the UMEC network.

Geopolymers are an economical, durable and environmentally friendly building material that achieves ultimate strength superior to concrete within hours and can withstand extreme high temperatures without degradation, but they lack elasticity and have poor flexural strength. To tackle these challenges, this paper presents a novel nanotechnological and affordable method to produce an elastic geopolymer that can be used as a construction material. Specifically, we report a simple and straightforward strategy that combines electrospinning and the sol-gel process to construct geopolymer nanofibers with superelasticity, extreme durability, and fire resistance. In order to improve the safety level of building construction sites that experience extreme conditions, such as freeze-thaw deterioration, the geopolymer nanofibers can be further functionalized by incorporating various functional components. In this study, Fe3O4 nanoparticles (NPs) adsorbed by an electric double layer structure were dispersed in a geopolymer sol (SAPOs). Elastic geopolymer nanofibers embedded with Fe3O4 nanoparticles were then prepared (Fe3O4/SAPOs) by an electrospinning method. Nanofibers constructed by such a compound method could have superparamagnetic behaviors and stable chemical properties. When Fe3O4/SAPOs are exposed to an alternating magnetic field, eddy currents are generated that cause the geopolymer nanofibers to heat up. This self-heating behavior is a powerful feature that enhances the freeze-thaw resistance of building materials at high altitudes. The nanotechnological route of this study could provide novel insights into the development of advanced construction materials.

Service function chain (SFC) based on network function virtualization (NFV) technology can handle network traffic flexibly and efficiently. The virtual network function (VNF), as the core function unit of SFC, can experience software aging, which reduces the availability and reliability of SFC and even leads to service interruption, after it runs continuously for a long time. Software rejuvenation technique can effectively combat software aging. However, its effectiveness in improving the availability and reliability of SFC needs to be evaluated. Compared with existing models, this paper proposes a semi-Markov model to capture the behaviors of each VNF in a SFC from the occurrence of software aging to recovery by software rejuvenation technique under the condition that the failure times and recovery times follow general distribution, while considering trigger intervals of software rejuvenation technique. We then derive the calculation formulas of the steady-state availability, transient availability, and reliability, which are applied to evaluate the effectiveness of software rejuvenation technique. Finally, we conduct sensitivity analysis and numerical experiments to analyze the effects of system parameters, the number of VNFs and trigger interval of software rejuvenation technique on availability and reliability of SFC, and the effects of time-varying parameters on transient availability of SFC.

The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment. It is crucial to guarantee normal operation of charging piles, resulting in the importance of diagnosing charging-pile faults. The existing fault-diagnosis approaches were based on physical fault data like mechanical log data and sensor data streams. However, there are other types of fault data, which cannot be used for diagnosis by these existing approaches. This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. We aim to find out how to combine existing feature-extraction and machine learning techniques to make the better diagnosis by conducting experiments on realistic dataset. 4 word embedding models are investigated for feature extraction of fault data, including N-gram, GloVe, Word2vec, and BERT. Moreover, we classify the word embedding results using 10 machine learning classifiers, including Random Forest (RF), Support Vector Machine, K-Nearest Neighbor, Multilayer Perceptron, Recurrent Neural Network, AdaBoost, Gradient Boosted Decision Tree, Decision Tree, Extra Tree, and VOTE. Compared with original fault record dataset, we utilize paraphrasing-based data augmentation method to improve the classification accuracy up to 10.40%. Our extensive experiment results reveal that RF classifier combining the GloVe embedding model achieves the best accuracy with acceptable training time. In addition, we discuss the interpretability of RF and GloVe.

As an indispensable part of intelligent transportation system, a traffic-sensitive navigation system can assist drivers in avoiding traffic congestion by providing navigation services. The navigation service provider (NSP) utilizes road condition information from nearby vehicles collected by roadside units (RSUs) to guide a vehicle through an optimal route provided by the navigation services. This paper mainly focuses on tackling three key issues in such navigation system. Firstly, it is essential to ensure the privacy of the vehicles’ personal identifiable information (PII). Secondly, the location, start point destination and route of the vehicle should not be leaked to RSUs and also should not be linked to its PII by RSUs and NSP. Thirdly, the process of vehicles obtaining the navigation services should be lightweight. Because of such problems, this paper proposes a dual privacy-preserving lightweight navigation system for vehicular networks through designing a novel signature scheme and combining other cryptographic primitives to keep dual privacy including PII and route related information. And the correctness analysis of the proposed system, security proof of the designed signature scheme adopted in the proposed system, and privacy analysis of the system are thoroughly provided. In addition, the performance of the proposed system is evaluated and compared with the existing systems to illustrate that the proposed system is more efficient than the existing systems.

Background Poor sleep raises the risk of non-alcoholic fatty liver disease (NAFLD) and hastens disease progression. It is critical to figure out what factors impact the sleep quality of NAFLD patients. The present study aimed to investigate the role of anxiety symptoms in accounting for the impact of rumination on sleep quality and the moderating role of resilience on the associations of rumination with anxiety symptoms and sleep quality. Methods In the cross-sectional study, 285 NAFLD patients completed the Chinese version of the Pittsburgh Sleep Quality Index, the Ruminative Responses Scale, the Generalized Anxiety Disorder 7-item scale, and the 14-item Resilience Scale to measure sleep quality, rumination (including brooding and reflection), anxiety symptoms, and resilience, respectively. The PROCESS macro for SPSS v4.0 procedure was applied to perform moderated mediation analysis. Results The roles of anxiety symptoms in accounting for the positive associations of brooding, reflection and rumination with poor sleep quality were revealed. It was found that there was a significant moderating role of resilience on the positive associations of brooding, reflection and rumination with anxiety symptoms, which were gradually reduced as resilience increased. The direct associations between brooding, reflection and rumination and poor sleep quality were not significantly moderated by resilience. Thus, a moderated mediation model involving anxiety symptoms and resilience for explaining the impact of rumination on poor sleep quality was supported among patients with NAFLD. Conclusions Rumination (including brooding and reflection) could be positively related to poor sleep quality, and anxiety symptoms had a significant role in accounting for the relationship among patients with NAFLD. Resilience showed a moderating role that could attenuate the positive association between rumination and anxiety symptoms. Interventions aimed at alleviating rumination, reducing anxiety symptoms, and enhancing resilience could improve the sleep quality of NAFLD patients.

Natural-fiber-reinforced geopolymer composites are environmentally friendly materials that have garnered considerable attention. In practice, the drying shrinkage and uneven dispersion of fibers in geopolymers result in low flexural strength, which limits the use of large-scale composites. In this study, high-flexural strength geopolymer composites with self-assembled nanofiber networks were hybridized using in situ polymerization. Composites with different fiber contents were evaluated via morphology and mechanical strength assessments. A mixed nanofiber and geopolymer solution was created to achieve uniform dispersion, and then the geopolymers were optimized at the nanoscale level using a bottom-up approach to achieve self-assembled nanofiber networks with good interfacial bonding. Due to the water retention properties of the nanofibers and subsequent gel material, the networks facilitated internal curing of the geopolymers, thus preventing drying shrinkage. This ultimately reduced the weight loss and increased the density of the composites. In addition, high flexural strength properties were observed in composites with a fiber content of 0.5%, due to the combination of fiber reinforcement and porosity. Thus, this work introduces new considerations for geopolymer applications.

The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples, the legitimate input added with imperceptible and well-designed perturbations that can fool DNNs easily in the DNN testing/deploying stage. Previous adversarial example generation algorithms for adversarial white-box attacks used Jacobian gradient information to add perturbations. This information is too imprecise and inexplicit, which will cause unnecessary perturbations when generating adversarial examples. This paper aims to address this issue. We first propose to apply a more informative and distilled gradient information, namely integrated gradient, to generate adversarial examples. To further make the perturbations more imperceptible, we propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly, which can restrict the total perturbations and perturbation points simultaneously. Meanwhile, to address the non-differentiable problem of $L_1$, we explore a proximal operation of $L_1$ thirdly. Based on these three works, we propose two Integrated gradient based White-box Adversarial example generation algorithms (IWA): IFPA and IUA. IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset in order to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured datasets, and we compare them with five baseline generation algorithms. The results show that our proposed algorithms do craft adversarial examples with more imperceptible perturbations and satisfactory crafting rate. $L_2$ restriction is more suitable for unstructured dataset and $L_1$ restriction performs better in structured dataset.

Proof-of-Work (PoW) blockchain systems like Bitcoin and Ethereum are vulnerable to selfholding attack. The prior modeling-based works about this attack only considered Bitcoin and assumed that there were at most two honest pools in a perfect network (no natural fork in such networks). However, a blockchain network is imperfect due to block propagation delay, which can lead to forking. Moreover, there may be more than two pools under attack. This paper aims for a quantitative analysis of an imperfect PoW blockchain network system under selfholding attack. We develop a novel stochastic model and derive formulas to evaluate the effect of selfholding attack on miner revenue, system security and system performance. Our work can be used to analyze the scenario where there are any number of pools suffering selfholding attack in both Ethereum and Bitcoin. The model in this paper can capture the behaviors of a more realistic and more general scenario, compared with the existing models. Moreover, our model and formulas can also be applied to evaluate a blockchain system, which uses a similar reward mechanism and is vulnerable to selfholding attack. Our work can help design a more secure blockchain incentive mechanism and an in-pool reward mechanism.

Selfish mining, as a serious threat to blockchain, has been attracting attentions from academic and industry. Stochastic modeling has been explored to quantitatively investigate selfish mining in imperfect blockchain networks. However, prior modeling-based analysis approaches have some of the following issues: (1) only focus on Bitcoin or Ethereum, or (2) ignore extended forks and just consider natural forks, or (3) only compute the mining revenue without assessing the performance and security of the blockchain system when the system suffers from selfish mining. In this paper, we aim to address these issues. We build a Markov chain to make quantitative analysis of selfish mining in imperfect Bitcoin and Ethereum networks with natural and extended forks. Formulas are derived to calculate the mining revenue for the selfish pool (comprising selfish miners) and honest miners, respectively. Moreover, we derive the formulas of performance metrics (namely, transactions per second and stale block ratio) and the formula of security metric (namely, the probability of double-spending success) of the system. These quantitative results can help understand the impact of selfish mining on imperfect blockchain networks and then help the detection of selfish mining.

Connected vehicles (CVs) are getting increasing attention in intelligent transportation systems (ITSs). In terms of the stringent security and anonymity issues, a novel anonymous and decentralized payment platform is demanded. Bitcoin is regarded as a potential solution. But the existing Bitcoin and its various improvements lack a beforehand anti-fraud Bitcoin deposit scheme, which is necessary to prevent potential fraud risks from Bitcoin to CVs. This paper aims to build a novel anti-fraud deposit scheme as a seamless bridge between CV networks and the Bitcoin payment platform. We propose a Bitcoin-to-Connected-Vehicle deposit scheme (Bit2CV) with an outsourcing endorsement in order to build an anti-fraud deposit transaction ( dtr ). Firstly, Bit2CV leverages a special Bitcoin-opcode-OP_RETURN based method to record the dtr , the CV’s request, and the endorsement together on Blockchain ledger. This method can achieve security features, including non-repudiation, privacy-friendly endorsement, future audit, and anti-fraud. Meanwhile, Bit2CV is fully decentralized without any involvement of centralized authorities and fully compatible with the current Bitcoin network. We conduct the security analysis and also simulations for performance evaluation. In a typical scenario of our simulation, Bit2CV total time cost is less than 477.03 ms, and the size of the endorsement is 1,732 bytes, which is much less and smaller than transaction confirmation time and the average block size, respectively. These designs and results demonstrate that Bit2CV is feasible and practical.

Vehicle platooning can be applied to cooperative downloading and uploading (CDU) services through the cooperation between lead vehicle and non-lead vehicles. CDU service can be completed cooperatively by containers constructed in vehicles of the vehicle platooning system. Containers in vehicles may suffer from potential attacks which can lead to resource degradation and further cause the decrease in the dependability and security of CDU service. Failover technique can be deployed in an in-vehicle system and then performs protective operation for mitigating the impact of resources degradation and guaranteeing service resilience. This paper aims to quantitatively analyze the availability, reliability and security of CDU service in the vehicle platooning system deploying failover technique. We construct an analytical model to describe the interaction between resources degradation and rejuvenation behaviors of the vehicle platooning system. Then we derive the formulas of availability, Mean Time to Failure (MTTF) for evaluating reliability and Mean Security Capacity to Failure (MSCF) for evaluating security. Simulation and analytical experiments are applied to demonstrate the approximate accuracy of our model and investigate the impact of system parameters on metrics, respectively, which helps service providers make decisions on how to organize vehicles to perform CDU service and how to set system parameter values for maximizing their benefits.

This paper is aimed at solving the thermo-elastic problems with the gravity load by the line integration boundary element method (LIBEM). The boundary-only discretization, as one of the significant advantages of BEM, will disappear due to considering the thermal and gravity loads, specifically, domain integrals turn up in the original boundary integral equations (BIEs). Hence, the LIBEM, owning the dimension reduction ability, is introduced to reduce domain integrals dimensionally to ensure that no domain discretization appears during the numerical simulation. Furthermore, as a well-known isogeometry characterization method, the non-uniform rational B-spline (NURBS), is exerted during geometrical construction. Thus, the boundary shape of the numerical models will not change during the refinement and the error of model discretization will be prevented. It is worth mentioning that, to ensure boundary conditions are imposed without difficulty, the field approximation is conducted by the Lagrangian basis functions (LBFs) owning the characteristic of the Kronecker delta function.

Internet of Things (IoT) jobs not only require computational resources but also are delay-sensitive and security-sensitive. Edge computing emerges as a promising paradigm to improve the quality of experience for IoT users. Edge computing faces many security threats, perhaps even more than traditional data centers. With a growing amount of data offloaded to Edge Data Centers (EDCs), the EDC performance needs to be considered and evaluated carefully for improving the vulnerable EDC resource utilization while satisfying IoT job requirements. This paper develops an analytical model, which can capture the dynamics of an EDC system with the following features: (i) The system is under heterogeneous workloads; (ii) the system is subject to attacks, which prevent equipment units in the system from providing service and (iii) the jobs in the system are delay-sensitive. Namely, the job processing fails before the processing is completed. Based on the proposed model, we develop formulas for performance and profit metrics and conduct a series of simulation experiments to verify the correctness and accuracy of our model. Finally, through our model, we evaluate the performance of the EDC, and we offer solutions for EDC administrators to maximize profit.

The fantastic growth in cybersecurity attack frequency and sophistication over the years advances the development of Moving Target Defense (MTD) technology. Migration-based dynamic platform technique (DPT), one of MTD techniques, is expected to significantly improve cyberspace security by migrating service across multiple platforms according to the predefined policy. However, the existing random migration policies cause unnecessary cost when the service platform is not under attack, which indicates the necessity of combining DPT with traditional detection-based defense mechanism to make migration decision. In this paper, we propose a Continuous-Time Markov Decision Process (CTMDP)-based dynamic platform defense model against multi-stage attacks, which can determine the optimal service migration timing based on the system reward. To maximize the expected total discounted reward of the system, we utilize the value iteration algorithm to determine the optimal policy which defines what action to be taken in a specific state. Experiments are carried out to demonstrate that our CTMDP-based dynamic platform defense model obtains higher expected total discounted reward than using random migration policies. We also investigate the effects of platform numbers and discount factors on the system reward.

Recently, researchers are being more interested in performing operations directly on the encrypted database with the help of the $O$ rder- $P$ reserving $E$ ncryption (OPE) scheme. This mechanism enables executing many types of queries efficiently, such as range query and comparison, since it can preserve the relative order of underlying plaintext on ciphertexts. However, traditional OPE schemes cannot achieve ideal security against IND-OCPA ( IND istinguishability under $O$ rdered $C$ hosen- $P$ laintext $A$ ttack) in a linear length of static encoding. Popa’s mutable scheme (namely, mOPE) is an effective solution to perform the range query in the database environment. In this paper, we propose a novel scheme, $B$ oundary $O$ rder- $P$ reserving $E$ ncryption (BOPE), to achieve high performance under ideal security. BOPE comprises two algorithms. One is a searching algorithm in which we propose a data structure, the boundary tree, to optimize the algorithm by cutting the scope of each iteration and reducing the rounds of interaction. The second algorithm is an updating algorithm for stale encoding, which determines whether to update the lookup table according to the type of each query, in order to avoid the time cost of redundant updates. We implemented and evaluated BOPE on a practical environment where we have achieved a performance increase of more than 10% from mOPE.

This work proposes Practical Byzantine Fault Tolerance (PBFT) ordering service needed for block formation in permissioned blockchain environments. Contrary to current PBFT implementations that only provide a single point of entry to the ordering service, we allow each ordering node to act as an entry point that proposes and conducts the consensus process of including new record in the distributed ledger. To ensure atomicity of record insertion in distributed ledger, we have developed a bandwidth reservation protocol that uses a modification of CSMA/CA protocol to regulate access to the broadcast medium formed by the P2P network of TCP connections between orderers. We have modeled record insertion service time in a cluster where ordering nodes have random position within Cartesian coordinate system. We have also modeled total request access time to the ledger which includes waiting time in the orderer's queue and record insertion time. These models are used to evaluate system performance under variable request rate ordering service, variable number of nodes and variable physical cluster dimensions. Our results show the interaction between decreased request waiting time in orderer's queue and increased contention among orderers when the number of orderers increases for the given total request arrival rate. This interaction is also investigated for two different physical cluster sizes which affect record insertion time. The interplay of request rate, number of orderers and physical cluster size determines system capacity expressed in total request rate. Our model can be used to make the trade-off between the required system capacity, number of orderers, and physical cluster dimensions under constraints on Byzantine fault rate.

With the growing demand of data processing/storage from Internet of Things (IoT) users and the increasing maturity of Cloud-Edge technologies, it becomes more and more critical to develop an effective and efficient performance evaluation approach in order to enhance the performance of Cloud-Edge services. Analytic modeling is an effective evaluation approach. The existing modeling researches on edge and/or cloud computing either ignored workload heterogeneity or ignored delay constraint of IoT tasks. This paper develops a hierarchical model for capturing the behaviors of Cloud-Edge datacenters, which provides service to tasks with different service priorities and requesting different number of service resources. Formulas for calculating performance measures of interest are also developed. The approximate accuracy of the proposed analytic model is verified through comparing numerical results and discrete-event simulation results.