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1.
Fog Computing (FC) based IoT applications are encountering a bottleneck in the data management and resource optimization due to the dynamic IoT topologies, resource-limited devices, resource diversity, mismatching service quality, and complicated service offering environments. Existing problems and emerging demands of FC based IoT applications are hard to be met by traditional IP-based Internet model. Therefore, in this paper, we focus on the Content-Centric Network (CCN) model to provide more efficient, flexible, and reliable data and resource management for fog-based IoT systems. We first propose a Deep Reinforcement Learning (DRL) algorithm that jointly considers the content type and status of fog servers for content-centric data and computation offloading. Then, we introduce a novel virtual layer called FogOrch that orchestrates the management and performance requirements of fog layer resources in an efficient manner via the proposed DRL agent. To show the feasibility of FogOrch, we develop a content-centric data offloading scheme (DRLOS) based on the DRL algorithm running on FogOrch. Through extensive simulations, we evaluate the performance of DRLOS in terms of total reward, computational workload, computation cost, and delay. The results show that the proposed DRLOS is superior to existing benchmark offloading schemes. 相似文献
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In complex working site, bearings used as the important part of machine, could simultaneously have faults on several positions. Consequently, multi-label learning approach considering fully the correlation between different faulted positions of bearings becomes the popular learning pattern. Deep reinforcement learning (DRL) combining the perception ability of deep learning and the decision-making ability of reinforcement learning, could be adapted to the compound fault diagnosis while having a strong ability extracting the fault feature from the raw data. However, DRL is difficult to converge and easily falls into the unstable training problem. Therefore, this paper integrates the feature extraction ability of DRL and the knowledge transfer ability of transfer learning (TL), and proposes the multi-label transfer reinforcement learning (ML-TRL). In detail, the proposed method utilizes the improved trust region policy optimization (TRPO) as the basic DRL framework and pre-trains the fixed convolutional networks of ML-TRL using the multi-label convolutional neural network method. In compound fault experiment, the final results demonstrate powerfully that the proposed method could have the higher accuracy than other multi-label learning methods. Hence, the proposed method is a remarkable alternative when recognizing the compound fault of bearings. 相似文献
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Deep Neural Network (DNN) is widely used in engineering applications for its ability to handle problems with almost any nonlinearities. However, it is generally difficult to obtain sufficient high-fidelity (HF) sample points for expensive optimization tasks, which may affect the generalization performance of DNN and result in inaccurate predictions. To solve this problem and improve the prediction accuracy of DNN, this paper proposes an on-line transfer learning based multi-fidelity data fusion (OTL-MFDF) method including two parts. In the first part, the ensemble of DNNs is established. Firstly, a large number of low-fidelity sample points and a few HF sample points are generated, which are used as the source dataset and target dataset, respectively. Then, the Bayesian Optimization (BO) is utilized to obtain several groups of hyperparameters, based on which DNNs are pre-trained using the source dataset. Next, these pre-trained DNNs are re-trained by fine-tuning on the target dataset, and the ensemble of DNNs is established by assigning different weights to each pre-trained DNN. In the second part, the on-line learning system is developed for adaptive updating of the ensemble of DNNs. To evaluate the uncertainty error of the predicted values of DNN and determine the location of the updated HF sample point, the query-by-committee strategy based on the ensemble of DNNs is developed. The Covariance Matrix Adaptation Evolutionary Strategies is employed as the optimizer to find out the location where the maximal disagreement is achieved by the ensemble of DNNs. The design space is partitioned by the Voronoi diagram method, and then the selected point is moved to its nearest Voronoi cell boundary to avoid clustering between the updated point and the existing sample points. Three different types of test problems and an engineering example are adopted to illustrate the effectiveness of the OTL-MFDF method. Results verify the outstanding efficiency, global prediction accuracy and applicability of the OTL-MFDF method. 相似文献
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Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the capability of its own onboard sensors. Trust evaluation and privacy preservation are two primary concerns for facilitating the distributed data fusion in cooperative vehicular safety applications. They have conflicting requirements and a good balance between them is urgently needed. Meanwhile, the computation, communication, and storage overheads will all influence the applicability of a candidate scheme. In this paper, we propose a Lightweight Privacy-Preserving Trust Evaluation (LPPTE) scheme which can primely balance the trust evaluation and privacy preservation with low overheads for facilitating the distributed data fusion in cooperative vehicular safety applications. Furthermore, we provide exhaustive theoretical analysis and simulation evaluation for the LPPTE scheme, and the results demonstrate that the LPPTE scheme can obviously improve the accuracy of fusion results and is significantly superior to the state-of-the-art schemes in multiple aspects. 相似文献
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The Internet of Things (IoT) envisions a world covered with billions of smart, interacting things capable of offering all sorts of services to near and remote entities. The benefits and comfort that the IoT will bring about are undeniable, however, these may come at the cost of an unprecedented loss of privacy. In this paper we look at the privacy problems of one of the key enablers of the IoT, namely wireless sensor networks, and analyse how these problems may evolve with the development of this complex paradigm. We also identify further challenges which are not directly associated with already existing privacy risks but will certainly have a major impact in our lives if not taken into serious consideration. 相似文献
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随着移动互联网的迅猛发展,社交网络平台充斥着大量带有情绪色彩的文本数据,对此类文本中的情绪进行分析研究不仅有助于了解网民的态度和情感,而且对科研机构和政府掌握社会的情绪变化及走向有着重要作用。传统的情感分析主要对情感倾向进行分析,无法精确、多维度地描述出文本的情绪,为了解决这个问题,文中对文本的情绪分析进行研究。首先针对不同领域文本数据集中情绪标签缺乏的问题,提出了一个基于深度学习的可迁移情绪分类的情感分析模型FMRo-BLA,该模型对通用领域文本进行预训练,然后通过基于参数的迁移学习、特征融合和FGM对抗学习,将预训练模型应用于特定领域的下游情感分析任务中,最后在微博的公开数据集上进行对比实验。结果表明,该方法相比于目前性能最好的RoBERTa预训练语言模型,在目标领域数据集上F1值有5.93%的提升,进一步加入迁移学习后F1值有12.38%的提升。 相似文献
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在物联网中节点恶意行为会促使节点的信任度大幅降低,如何确保高信任度并加强网络安全性已经成为首要问题,提出一种物联网节点动态行为信任度评估方法.首先依据多实体贝叶斯建立信任模型,可以控制恶意节点对物联网的攻击和入侵;采用贡献资源数值权重来抑制自私节点,并对其初始化处理;通过对信任的传递与合成计算出推荐信任值,能够减少运算... 相似文献
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汪京培 《网络安全技术与应用》2014,(12):89-90
信任管理模型是物联网可信性评估问题的潜在解决方案,目前关于物联网信任模型的研究主要集中在定性分析和无线传感器网络上,可操作的定量分析成果很少。本文主要研究了在物联网分层架构下,智能物品追踪场景下的可信建模问题。从信任的建立、信任的建模、信任的计算、信任的传递、信任的决策5个典型的信任管理生命周期依次量化建模,建立物联网的量化信任管理模型。仿真结果表明所提模型相对于以往信任模型具有更好的鲁棒性和任务执行效率。 相似文献
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Trust evaluation computes trust values by collecting and processing trust evidence. It plays an important role in trust management that automatically ensures trust relationships among system entities and enhances system security. But trust evidence collection and process may cause privacy leakage, which makes involved entities reluctant to provide personal evidence that is essential for trust evaluation. Current literature pays little attention to Privacy-Preserving Trust Evaluation (PPTE). Existing work still has many limitations, especially on generality, efficiency and reliability. In this paper, we propose two practical schemes to guard privacy of trust evidence providers based on additive homomorphic encryption in order to support a traditional class of trust evaluation that contains evidence summation. The first scheme achieves better computational efficiency, while the second one provides greater security at the expense of a higher computational cost. Accordingly, two trust evaluation algorithms are further proposed to flexibly support different application cases. Specifically, these algorithms can overcome attacks raised by internal malicious evidence providers to some extent even though the trust evaluation is partially performed in an encrypted form. Extensive analysis and performance evaluation show the security and effectivity of our schemes for potential application prospect and their efficiency to support big data process. 相似文献
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可信是制约物联网发展与应用的瓶颈,本文重点探讨了物联网模式下的可信问题,基于物联网模式下LED显示集成平台架构的研究,提出了一种LED显示终端集成平台可信安全系统框架,设计了基于USBKey的LED显示集成平台可信安全系统,通过在LED显示软件平台增加TPM硬件模块,实现对终端用户的可信认证,以确保引导过程中软件的完整性,从而提高物联网模式下LED显示集成平台的可信性。 相似文献
11.
Android has stood at a predominant position in mobile operating systems for many years. However, its popularity and openness make it a desirable target of malicious attackers. There is an increasing need for mobile malware detection. Existing analysis methods fall into two categories, i.e., static analysis and dynamic analysis. The dynamic analysis is more effective and timely than the static one, but it incurs a high computational overhead, thus cannot be deployed in resource-constrained mobile devices. Existing studies solve this issue by outsourcing malware detection to the cloud. However, the privacy of mobile app runtime data uploaded to the cloud is not well preserved during both detection model training and malware detection. Numerous efforts have been made to preserve privacy with cryptography, which suffers from high computational overhead and low flexibility. To address these issues, in this paper, we propose an Intel SGX-empowered mobile malware detection scheme called EPMDroid. We also design a probabilistic data structure based on cuckoo filters, named CuckooTable, to effectively fuse features for detection and achieve high space efficiency. We conduct both theoretical analysis and real-world data based tests on EPMDroid performance. Experimental results show that EPMDroid can speed up malware detection by up to 43.8 times and save memory space by up to 3.7 times with the same accuracy, as compared to a baseline method. 相似文献
12.
在物联网中的认证和密钥协商过程中,如果用户的身份信息以明文的形式传输,攻击者可能追踪用户的行动轨迹,从而造成信息泄漏。针对大多数基于身份的认证和密钥协商协议不能保护用户隐私的问题,提出一个基于身份的匿名认证和密钥协商协议。在设计的认证和密钥协商方案中,用户的身份信息以密文的形式传输,解决了用户的隐私问题。 相似文献
13.
随着物联网概念的提出,各国政府专家、企业和技术人员都开始着手研究和建设物联网的工作。物联网安全和隐私问题必然会影响其建设与发展。为了解除物联网发展过程中的障碍,同时为物联网的安全与隐私保护提供相关措施,分析了物联网体系架构所面临的安全威胁,并从感知层、传输层和应用层分别对安全威胁进行详细的研究和总结,最后针对物联网面临的各类安全威胁给出了对应的安全措施。 相似文献
14.
Individuals communicate and form relationships through Internet social networking websites such as Facebook and MySpace. We study risk taking, trust, and privacy concerns with regard to social networking websites among 205 college students using both reliable scales and behavior. Individuals with profiles on social networking websites have greater risk taking attitudes than those who do not; greater risk taking attitudes exist among men than women. Facebook has a greater sense of trust than MySpace. General privacy concerns and identity information disclosure concerns are of greater concern to women than men. Greater percentages of men than women display their phone numbers and home addresses on social networking websites. Social networking websites should inform potential users that risk taking and privacy concerns are potentially relevant and important concerns before individuals sign-up and create social networking websites. 相似文献
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聚类挖掘中隐私保护的几何数据转换方法 总被引：4，自引：0，他引：4
目前,尽管数据挖掘在许多领域都发挥了巨大的作用,但同时它也带来了一系列越来越值得重视的问题,如隐私的保护、信息的安全等。讨论了数据挖掘中的隐私保护问题,提出了一种几何数据转换方法,并将其用于聚类数据挖掘中的隐私保护。实验结果表明该方法可以较好地实现数据挖掘应用中的隐私保护。 相似文献
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The Industrial Internet of Things (IIoT) interconnects a large number of interconnected sensors, actuators, and edge computing devices in the manufacturing systems, where the massive data collected in the manufacturing process has the characteristics of multi-dimensional, heterogeneous, and time series. An effective data representation manner, which can fuse such complex information and enable cognitive manufacturing decision-making from a global perspective, is necessary and challenging. To solve this issue, this paper proposes a knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing and applies it in a Cyber-Physical Production System (CPPS) scenario. Based on the digital thread of manufacturing process data, a multi-layer manufacturing knowledge graph is established, including device sensing data, production processing data, and business processing data. With the established knowledge graph, a cognition-driven approach is proposed with a perception-cognition dual system, which achieves perception analysis and cognition decision-making in the resource allocation of the manufacturing process. Finally, responding to the orders of personalized products in a workshop is taken as an illustrative example. The performance of allocating resources of workshop devices under dynamic demand changes shows the advantages of the proposed approach. The proposed manner will lay the foundation for a human-like cognition for processing massive real-time industrial information in CPPS, thus paving a pathway towards the era of cognitive manufacturing. 相似文献
19.
In data mining and knowledge discovery, there are two conflicting goals: privacy protection and knowledge preservation. On the one hand, we anonymize data to protect privacy; on the other hand, we allow miners to discover useful knowledge from anonymized data. In this paper, we present an anonymization method which provides both privacy protection and knowledge preservation. Unlike most anonymization methods, where data are generalized or permuted, our method anonymizes data by randomly breaking links among attribute values in records. By data randomization, our method maintains statistical relations among data to preserve knowledge, whereas in most anonymization methods, knowledge is lost. Thus the data anonymized by our method maintains useful knowledge for statistical study. Furthermore, we propose an enhanced algorithm for extra privacy protection to tackle the situation where the user’s prior knowledge of original data may cause privacy leakage. The privacy levels and the accuracy of knowledge preservation of our method, along with their relations to the parameters in the method are analyzed. Experiment results demonstrate that our method is effective on both privacy protection and knowledge preservation comparing with existing methods. 相似文献
20.
The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. However, due to the relatively long distance between remote cloud and end nodes, cloud computing cannot provide effective and direct management for end nodes, which leads to security vulnerabilities. In this paper, we propose a novel trust evaluation model based on the trust transitivity on a chain assisted by mobile edge nodes, which is used to ensure the reliability of nodes in the Internet of Things and prevent malicious attacks. The mobile edge nodes offer a new solution to solve the above problems with relatively strong computing and storage abilities. Firstly, we design calculation approaches to different trust chains to measure their trust degrees. Secondly, we propose an improved Dijkstra’s algorithm for collecting trust information of sensor nodes by mobile edge nodes. Finally, the experimental results show that our trust model based on mobile edge nodes can evaluate sensor nodes more precisely and enhance the security on the Internet of Things. 相似文献