Mobile edge-enabled trust evaluation for the Internet of Things

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.     

On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks

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.     

LPPTE: A lightweight privacy-preserving trust evaluation scheme for facilitating distributed data fusion in cooperative vehicular safety applications

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.     

Evolving privacy: From sensors to the Internet of Things

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.     

物联网量化信任管理模型研究

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。     

物联网安全与隐私保护研究

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。     

物联网本体存储数据可撤销加密仿真研究

针对物联网密钥不可随机拆分与撤销,本体存储数据容易遭到外界入侵,导致用户隐私信息泄露,提出物联网本体存储数据可撤销加密方法。根据数据可撤销加密的理论确定双线性映射函数,通过加密撤销的困难假设构建了物联网本体存储数据可撤销模型。对存储数据进行可撤销运算,采用了周期更新思想,将用户密钥属性、用户身份以及时间进行划分,并根据访问树的节点数据对用户存储数据进行加密计算。通过主要密钥随机拆分撤销运算,根据结果对存储数据可撤销加密进行安全认证。仿真结果表明,采用可撤销加密方法能够实现物联网密钥的随机拆分,缩短用户密钥更新周期,数据安全性更高。     

Transfer reinforcement learning method with multi-label learning for compound fault recognition

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.     

The Neural Knowledge DNA Based Smart Internet of Things

Abstract

The Internet of Things (IoT) has gained significant attention from industry as well as academia during the past decade. Smartness, however, remains a substantial challenge for IoT applications. Recent advances in networked sensor technologies, computing, and machine learning have made it possible for building new smart IoT applications. In this paper, we propose a novel approach: the Neural Knowledge DNA based Smart Internet of Things that enables IoT to extract knowledge from past experiences, as well as to store, evolve, share, and reuse such knowledge aiming for smart functions. By catching decision events, this approach helps IoT gather its own daily operation experiences, and it uses such experiences for knowledge discovery with the support of machine learning technologies. An initial case study is presented at the end of this paper to demonstrate how this approach can help IoT applications become smart: the proposed approach is applied to fitness wristbands to enable human action recognition.     

物联网模式下LED显示可信集成平台框架设计

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。     

物联网与嵌入式技术

本文从物联网及其体系结构谈起,介绍了嵌入技术及嵌入式处理器,并对嵌入式技术在物联网中的作用及发展前景做了阐述。     

基于区块链的物联网任务协作信任管理方案

在物联网智能设备任务协作场景中,为解决设备交互不受信以及存在恶意设备破坏协作的问题,提出一种基于区块链的信任管理方案。半分布式的架构克服了集中式和分布式架构的不足,任务协作过程中兼顾了双方的信任,并设计了审查机制确保任务评价的真实可靠。信任的衡量中使用狄利克雷分布模拟任务评价的多样性,构建信任为数个状态,并利用马尔可夫链评估信任状态和全局信任。仿真结果表明,提出的方案可以客观衡量设备的信任,同时在检测恶意设备、提高协作任务成功率及避免误判方面具有优势,因此可以有效确保任务交互的可信和协作环境的安全。     

物联网的应用和前景展望

物联网被视为互联网的应用扩展,应用创新是物联网的发展的核心,以用户体验为核心的创新是物联网发展的灵巍。沥诞网作为未来发展的趋势,将给世界和社会带来巨大变革,并改变人们的生活方式。     

物联网的机遇与挑战

作为未来的发展趋势之一,物联网将在各个方面决定未来世界和人类社会的发展方向。由于物联网的发展还处于起步阶段,人们对这一领域的了解往往不够全面,甚至存在一些误区。因此,通过介绍物联网的发展史和相关基本概念,希望使读者能够对这一全新领域有一个全面准确的认识。同时对当前物联网发展中存在的困难进行了比较系统的分析,指出了今后的发展方向。     

A comprehensive approach to privacy in the cloud-based Internet of Things

In the near future, the Internet of Things is expected to penetrate all aspects of the physical world, including homes and urban spaces. In order to handle the massive amount of data that becomes collectible and to offer services on top of this data, the most convincing solution is the federation of the Internet of Things and cloud computing. Yet, the wide adoption of this promising vision, especially for application areas such as pervasive health care, assisted living, and smart cities, is hindered by severe privacy concerns of the individual users. Hence, user acceptance is a critical factor to turn this vision into reality.To address this critical factor and thus realize the cloud-based Internet of Things for a variety of different application areas, we present our comprehensive approach to privacy in this envisioned setting. We allow an individual user to enforce all her privacy requirements before any sensitive data is uploaded to the cloud, enable developers of cloud services to integrate privacy functionality already into the development process of cloud services, and offer users a transparent and adaptable interface for configuring their privacy requirements.     

物联网中传感节点数据异常检测方法研究

研究物联网中节点数据异常的检测问题,提高检测的准确率。针对当物联网中的传感节点采集到的外界数据内容随机性较大,在数据存储形式、格式等特征上存在较大差异时,造成各个节点数据之间很难取得一致。传统方法检测无法准确根据差异信息建议统一待检测特征,造成检测的准确度不高的问题。为了解决这一问题,提出一种基于特征模糊聚类概率搜索的节点数据异常检测算法,通过计算节点数据与异常数据之间的特征模糊聚类概率,运用模糊概率因素判定非法节点数据。实验证明,改进方法能快速、完整地将检测节点异常数据,取得了理想的效果。     

物联网时代的嵌入式系统机遇

嵌入式系统是物联网平台的半边天.物联网、云计算给嵌入式系统带来新的机遇.单片、嵌入、物联是嵌入式系统的三个基本特性,体现了嵌入式系统的三个时代特征.物联网是多学科的大科技概念,要有多学科视野,嵌入式系统应该用自己的语言来诠释物联网.目前,物联网平台的许多嵌入式技术已经成熟,建设物联网的大型国家工程迫在眉睫.     

UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework

Traditional clustering algorithms can be applied for the pre-sorting step of radar signal sorting. It can effectively dilute the pulse stream and prevent the dense pulse stream from interfering pulse repetition interval (PRI) extraction. However, the pre-sorting deviation will cause interference and missing pulses during the main sorting process. To solve this problem, we deploy the unmanned aerial vehicle (UAV) swarm to monitor reconnaissance areas and put forward a novel deep transfer learning based signal sorting method. The UAV swarm can collect the pulses from different time and spatial domains, and interference and missing pulses in main sorting processing can be relieved dramatically. In our model, we pre-train our model with the data collected from multiple source areas, which corresponds to different areas detected by different parts of UAV swarms. Then we fine-tune our model with the data of the target area. The experimental results prove that the signal sorting accuracy of methods based on deep transfer learning, i.e., YOLO-MobileNet, F-RCNN and cascade RCNN, are higher than that of the baseline methods. In addition, the signal sorting accuracy of traditional methods based on deep learning can be greatly improved with the help of transfer learning.     

基于Netty和Kafka的物联网数据接入系统

当前在物联网应用中,大量采集终端被用于感知环境、定位服务、状态监测等应用,并源源不断地上传数据,在提产助效的同时,给远程服务端数据收集及实时处理带来巨大挑战。利用Netty网络通信库构造高性能的收集端网络通信处理服务并设计出可供采集终端与收集端使用的通信协议,剥离出网络通信业务中耗时操作并将数据推送至流式消息处理系统Kafka中,再由Kafka消费者负责后续数据持久化、实时分析工作。由此实现一个支持高并发、低延迟的数据接入系统。实验结果表明,在万级别连接情况下,该系统能正常工作且保持较快的响应速度。     

物联网的研究现状及其产业化

物联网(IOT)作为近年来发展最为迅猛的技术受到各方面的关注.本文首先从物联网的概念出发,全面介绍物联网的历史及概念;然后就其各种标准及标准化的进程进行介绍和分析,并对其发展现状做概括性的描述;最后对其产业化的发展道路进行分析和展望.