A trusted consensus fusion scheme for decentralized collaborated learning in massive IoT domain

In a massive IoT systems, large amount of data are collected and stored in clouds, edge devices, and terminals, but the data are mostly isolated. For many new demands of various intelligent applications, self-organized collaborated learning on those data to achieve group decisions has been a new trend. However, in order to reach the goal of group decisions, trust problems on data fusion and model fusion should be solved since the participants may not be trusted. We propose a consistent and trust fusion method with the consortium chain to reach a consensus, and complete the self-organized trusted decentralized collaborated learning. In each consensus process, consensus candidates check others’ trust levels to ensure that they tends to fuse consensus with users with high trust, where the trust levels are evaluated by scores according to their historical behaviors in the past consensus process and stored in the public ledger of blockchain. A trust rewards and punishments method is designed to realize trust incentive consensus, the candidates with higher trust levels have more rights and reputation in the consensus. Simulation results and security analysis show that the method can effectively defend malicious users and data, improve the trust perception performance of the whole federated learning network, and make the federated learning more trusted and stable.     

Efficient Blockchain-Empowered Data Sharing Incentive Scheme for Internet of Things

In recent years, with many devices continuously joining the Internet of Things (IoT), data sharing as the main driver of the IoT market has become a research hotspot. However, the users are reluctant to participate in data sharing due to security concerns and lacking incentive mechanisms in the current IoT. In this context, blockchain is introduced into the data sharing of IoT to solve the trust problem of users and provide secure data storage. However, in the exploration of building a secure distributed data sharing system based on the blockchain, how to break the inherent performance bottleneck of blockchain is still a major challenge. For this reason, the efficient blockchain-based data sharing incentive scheme is studied for IoT. In the scheme, an efficient data sharing incentive framework based on blockchain is proposed, named ShareBC. Firstly, ShareBC uses sharding technology to build asynchronous consensus zones that can process data sharing transactions in parallel and deploy efficient consensus mechanisms on the cloud/edge servers and asynchronous consensus zones in sharding, thus improving the processing efficiency of data sharing transactions. Then, a sharing incentive mechanism based on a hierarchical data auction model implemented by a smart contract is presentedto encourage IoT users to participate in data sharing. The proposed mechanism can solve the problem of multi-layer data allocation involved in IoT data sharing and maximize the overall social welfare. Finally, the experimental results show that the proposed scheme is economically efficient, incentive-compatible, and real-time, with scalability, low cost, and good practicability.     

区块链赋能的高效物联网数据激励共享方案

近年来,随着大量设备不断地加入物联网中,数据共享作为物联网市场的主要驱动因素成为了研究热点.然而,当前的物联网数据共享存在着出于安全顾虑和缺乏激励机制等原因导致用户不愿意参与共享数据的问题.在此背景下,区块链技术为解决用户的信任问题和提供安全的数据存储被引入到物联网数据共享中.然而在构建基于区块链的安全分布式数据共享系统的探索中,如何突破区块链固有的性能瓶颈仍然是一个关键挑战.为此,本文研究了基于区块链的高效物联网数据激励共享方案.该方案首先提出了一个高效的区块链物联网数据激励共享框架,称为ShareBC.ShareBC利用分片技术构建能够并行处理数据共享交易的异步共识区,并在云/边缘服务器上和分片异步共识区上部署高效的共识机制,从而提高数据共享交易的处理效率.然后,为激励物联网用户参与数据共享,提出了一种基于智能合约实现的层次数据拍卖模型的共享激励机制.该机制解决了物联网数据共享中涉及的多层数据分配有效性问题,能够最大限度地提高整体社会福利.最后,实验结果证明了该方案的经济效益、激励兼容性和实时性以及可扩展性,且具有较低的计算成本和良好的实用性.     

物联网节点动态信任度评估方法

针对现有物联网（IoT）信任度评估方法未考虑信任的时效性、非入侵因素对直接信任度评估的影响以及缺乏对推荐节点可靠度的评估,造成信任评估准确度低且不能有效应对节点恶意行为的不足,提出一种IoT节点动态信任度评估方法（IDTEM）。首先,设计节点服务质量持续因子评估节点行为,并采用动态信任衰减因子表达信任的时效性,改进基于贝叶斯的直接信任度评估方法;其次,从推荐节点价值、评价离散度与节点自身的信任度值三个方面评估推荐节点可靠度,并据此优化推荐信任度权重计算方法;同时,设计推荐信任反馈机制,通过服务提供节点完成服务后的实际信任度与推荐信任度的反馈误差实现对协同恶意推荐节点的惩罚;最后,基于熵计算节点自适应权重,得到节点综合信任度值。实验结果表明,同基于贝叶斯理论的面向无线传感器网络的信誉信任评估框架（RFSN）模型及基于节点行为的物联网信任度评估方法（BITEM）相比,IDTEM可较好地识别恶意服务和抑制恶意推荐行为,且具有较低的传输能耗。     

基于感知源的数据驱动信任评测模型

为解决物联网数据源头的可靠问题,构建一种基于感知源的数据驱动信任评测模型.模型以监测模块为评测单元,由中继节点完成其所在监测模块内感知节点的信任评测,通过感知节点自身数据之间的关系实现直接信任的计算,利用监测模块内各邻居节点之间关系实现推荐信任的计算,再结合历史信任,输出感知节点的综合信任.同时与模型预设的可疑阈值和异常阈值进行对比,更新历史信任和信任列表,实现感知节点的异常检测,利用预警检测误差和失信检测误差对模型的检测效果进行评价,统计结果表明模型能够保持较低的平均误差.将信任机制引入到数据融合过程,用综合信任作为加权因子,从而提高了数据融合的准确度.最后,通过实验仿真对信任评测模型进行评价,结果表明引入信任评测模型后延长了节点开始死亡的时间,随着节点的更新迭代,失信节点越来越少,在一定程度上提高了节点的存活率,延长了网络的生命周期.     

物联网中基于信任抗On-off攻击的自适应安全机制

为了降低静态安全机制中不必要的数据源认证开销和防御信任阈值机制中存在的On-off攻击,在物联网（IoT）环境下提出了一种基于信任的自适应安全机制。首先,根据节点在信息交互中的行为表现建立节点间的信任评估模型,进而给出节点总体信任值的度量方法;然后,对于总体信任值高于信任阈值的节点,采用基于信任的自适应检测算法实时地检测这些节点总体信任值的变化情况;最终,中继节点根据自适应检测的结果决定是否验证接收到的消息。仿真实验结果和分析表明,该机制降低了中继节点的能量开销,同时对物联网中的On-off攻击起到较好的防御作用。     

Reinforcement Learning Based Data Fusion Method for Multi-Sensors

In order to improve detection system robustness and reliability, multi-sensors fusion is used in modern air combat. In this paper, a data fusion method based on reinforcement learning is developed for multi-sensors. Initially, the cubic B-spline interpolation is used to solve time alignment problems of multi-source data. Then, the reinforcement learning based data fusion (RLBDF) method is proposed to obtain the fusion results. With the case that the priori knowledge of target is obtained, the fusion accuracy reinforcement is realized by the error between fused value and actual value. Furthermore, the Fisher information is instead used as the reward if the priori knowledge is unable to be obtained. Simulations results verify that the developed method is feasible and effective for the multi-sensors data fusion in air combat.      

A verifiable trust evaluation mechanism for ultra-reliable applications in 5G and beyond networks

With the development of Internet of Thing (IoT) joint 5G and Beyond Networks, Mobile Edge Users (MEUs) can act as mobile data collectors to collect data for various applications. However, some malicious MEUs reporting false or malicious data can cause serious harm to applications, especially for ultra-reliable applications. A novel Verifiable Trust Evaluation joint UAV (VTE-UAV) mechanism is proposed to select trustworthy MEUs to conduct the task for ultra-reliable applications. The VTE-UAV strategy adopts two novel trust evaluation methods, one is the aggregation-based MEU trust evaluation mechanism, when malicious evaluation objects are in the minority, the mechanism takes most evaluation results as baseline data. The other is an active trust acquisition mechanism, it takes the data obtained by Unmanned Aerial Vehicles (UAVs) as baseline data to actively validate the authenticity of the data. Through these two cross evaluation strategies, we obtain more accurate trust evaluation results. Finally, this paper transforms the trust evaluation optimization problem into the optimization of the accuracy of trust evaluation with reducing the cloud payment and the dispatch cost of UAVs. Extensive experiments have verified the validity of the VTE-UAV strategy. Compared with the previous strategies, the VTE-UAV improves the cloud recruitment performance by 7.74%-25.91%, increases the accuracy of trust evaluation of IoT devices by 2.24%-11.72%, and reduces the cloud payment and the cost of UAVs by 3.11%-10.20% and 58.23%, respectively.     

DPOS共识机制的改进方案

针对委托权益证明（DPOS）共识机制节点投票不积极以及恶意节点勾结现象提出了一种改进方案。首先,引入非结构化网络信任模型,根据每个节点的历史记录和其他节点的推荐值计算综合信任值。根据综合信任值进行投票,使得选择的节点更可信。引入推荐算法,节点的权益得到了分散,降低了中心化程度。其次,加入了奖惩机制,针对积极投票的节点给予信用值的奖励,使其有机会成为共识节点,针对恶意节点给予信任值的惩罚。实验结果表明,基于综合信任值投票计算的DPOS共识机制能够快速剔除错误节点,维护系统稳定性,具有较高的安全性。     

权限可控传递的物联网共享设备委托授权访问机制

为了解决在共享模式下设备所有者在委托授权时权限敏感度保护以及中间代理滥用授权的问题,综合基于信任度访问控制模型和代理签名的特征,提出了一种基于信任度的可控部分权限委托授权机制。该机制采用基于角色和信任度值的授权策略,通过代理签名实现可控的部分权限委托传递。经安全分析证明,该机制可满足权限传递所需的可验证性、不可否认性、可区分性、可识别性和不可滥用性等安全属性,确保了设备所有者权限的可控安全传递,有效防止中间代理过度授权的问题。     

基于信誉评估机制和区块链的移动网络联邦学习方案

联邦学习是一种新兴的分布式机器学习技术,通过将训练任务下放到用户端,仅将训练得到的模型参数发送给服务端,整个过程并不需要参与方直接共享数据,从而很大限度上规避了隐私问题。然而,这种学习模式中移动用户间没有预先建立信任关系,用户之间进行合作训练时会存在安全隐患。针对上述问题,提出一种基于信誉评估机制和区块链的移动网络联邦学习方案,该方案允许服务端利用主观逻辑模型对参与训练的移动用户进行信誉评估,并且基于区块链智能合约技术为其提供可信的信誉意见共享环境和动态访问策略接口。理论和实验分析结果表明,此方案可以使服务端选择可靠的用户进行训练,同时能够实现更公平和有效的信誉计算,提高联邦学习的准确性。     

基于机器学习的动态信誉评估模型研究

为在开放网络环境中建立资源消费者(用户)和资源提供者(主机)之间的信任关系,提出基于机器学习的动态信誉评估模型 .模型中用户的信誉级别可以根据其行为和一些其他监测数据动态变化,而资源的信誉级别也可以根据用户对资源所提供服务的评价动态变化 .给出了用于生成评估规则和信誉级别的模糊信誉级别评估算法(FTEA),算法采用基于规则的机器学习方法,具有从大量输入数据中自学习以获取评估规则的能力 .实验结果表明,1000组输入数据能够生成理想的规则库,并且算法执行时间随输入判定因素数目成指数形式增长,因此需要选择5～6个因素和1000个左右的样本数据以进行系统实现 .     

物联网中基于信任的频谱资源分配机制

随着无线网络的快速发展,物联网中频谱资源的高效分配问题亟需解决,为此,提出一种基于信任的频谱资源分配机制TSRA.借鉴拍卖理论建立频谱资源拍卖系统模型,根据信任理论确定用户间的信任关系以缩小客户网络范围,利用属性加密理论保护交易数据.在此基础上,采用改进的蚁群算法为用户合理规划资源分配路径,从而实现频谱资源的多目标分配.实验结果表明,该机制可以为用户的交易数据提供细粒度的保护,且具有较高的社会效益和较低的系统计算与通信开销.     

Content-centric data and computation offloading in AI-supported fog networks for next generation IoT

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.     

基于特征迁移学习的综合能源系统小样本日前电力负荷预测

基于历史数据和深度学习的负荷预测已广泛应用于以电能为中心的综合能源系统中以提高预测精度,然而,当区域中出现新用户时,其历史负荷数据往往极少,此时,深度学习难以适用.针对此,本文提出基于负荷特征提取和迁移学习的预测机制.首先,依据源域用户历史负荷数据,融合聚类算法和门控循环单元网络构建源域数据的特征提取和分类模型;然后,利用该模型提取当前待预测目标域小样本的特征及其类别信息,进而给出基于特征相似度和时间遗忘因子的特征融合策略;最后,依据融合特征,给出基于迁移学习和特征输入的负荷预测.将所提算法应用于卡迪夫某区域的高中和住宅用电预测中,实验结果表明了该算法在综合能源系统小样本电力负荷预测中的有效性.     

基于DICE的证明存储方案

信息技术的不断发展和智能终端设备的普及导致全球数据存储总量持续增长,数据面临的威胁挑战也随着其重要性的凸显而日益增加,但目前部分计算设备和存储设备仍存在缺乏数据保护模块或数据保护能力较弱的问题.现有数据安全存储技术一般通过加密的方式实现对数据的保护,但是数据的加解密操作即数据保护过程通常都在应用设备上执行,导致应用设备遭受各类攻击时会对存储数据的安全造成威胁.针对以上问题,本文提出了一种基于DICE的物联网设备证明存储方案,利用基于轻量级信任根DICE构建的可信物联网设备为通用计算设备(统称为主机)提供安全存储服务,将数据的加解密操作移至可信物联网设备上执行,消除因主机遭受内存攻击等风险对存储数据造成的威胁.本文工作主要包括以下3方面:(1)利用信任根DICE构建可信物联网设备,为提供可信服务提供安全前提.(2)建立基于信任根DICE的远程证明机制和访问控制机制实现安全认证和安全通信信道的建立.(3)最终利用可信物联网设备为合法主机用户提供可信的安全存储服务,在实现数据安全存储的同时,兼顾隔离性和使用过程的灵活性.实验结果表明,本方案提供的安全存储服务具有较高的文件传输速率,并具备较高...     

基于评价向量的P2P电子商务信任模型

针对现有P2P信任模型中交易因素考虑不全面、缺乏恶意节点识别机制而导致无法防御恶意节点共谋攻击和耗费网络带宽等问题,提出一种新的P2P信任模型EVTM,该模型采用向量化的方式表示评价的标准,引入时间衰减因子和惩罚机制,促使模型中交易节点积极地、正确地评价,从而构造一个诚信、可靠的交易环境。仿真实验进一步证明EVTM不仅可以根据用户的不同需求对交易对象做出更合理的信任评估,而且能有效地避免恶意节点的共谋攻击,从而降低交易的风险,减少损失。     

一种信任增强的Web服务综合评估模型

基于信任的网络交易业务日益增多,然而Web服务环境下的信任评估模型还不健全,存在Web服务请求方身份不明、信任度量因素考虑不周全及信任评估主观性强等问题。针对上述问题,提出了一种Web服务环境下信任增强的综合评估模型——WS-TECEM（Trust Enhanced Comprehensive Evaluation Model for Web Services）。WS-TECEM在传统模型的基础上,引入信任关系强度、第一手、第二手、第三手信誉看法及第三方信誉推荐者的可信度等思想,提出一种信任粒度更细、信任指数更强的评估模型,仿真实验表明,WS-TECEM能更全面、更准确地度量Web服务环境下交互双方的可信度。     

垂直学习社区基于学习兴趣与风格的社会化推荐算法

垂直学习社区包含了海量的学习资源,出现了信息过载现象,个性化推荐是解决这个难题的方法之一.但垂直学习社区中评分数据稀疏而文本、社交信息丰富,传统的协同过滤推荐算法不完全适用.基于用户产生的文本和行为信息,利用作者主题模型构建新的用户学习兴趣相似度衡量模型;根据用户交互行为信息综合考虑信任与不信任因素构建用户全面信任关系计算全面信任度;通过分析用户多维度学习行为模式,自动识别用户学习风格;最后提出融合兴趣相似度、全面信任度及学习风格的社会化推荐算法.用垂直学习社区网站CSDN实际数据集进行了实验分析.结果表明本文提出的推荐方法能更好向用户推荐其感兴趣的学习资源,有效地提高了推荐精度,进而提高用户学习效果.     

A trust evaluation scheme for complex links in a social network: a link strength perspective

Potential security threats pose a significant challenge to evaluating the trustworthiness of complex links among users in a social network. Traditional trust computation methods typically consider user comments or interaction, thereby reflecting the trustworthiness between users according to their past experiences. However, the tie strength, which reflects the closeness of user relationships, is also a potential factor for estimating the trustworthiness of links among users. To incorporate this indicator, we propose a trust evaluation scheme for complex links comprising two aspects: the reliability and strength of links among uses. Our main contributions are (1) a trust calculation method, including direct trust for directly linked users and indirect trust for indirectly linked users, which is established based on the comment factor, forwarding factor, and approving factor; (2) a link strength evaluation method to determine the trustworthiness of direct and indirect links between users considering comment stability, mutual trust, interaction frequency, and common neighbours and community similarity, and (3) a link trust evaluation algorithm based on the link trust matrix synthesizing the reliability and strength of links. The experimental results and analysis show that our proposed scheme is feasible and effective in improving the performance of trust evaluation in a social network.