A framework for collaborative computing and multi-sensor data fusion in body sensor networks

Body Sensor Networks (BSNs) have emerged as the most effective technology enabling not only new e-Health methods and systems but also novel applications in human-centered areas such as electronic health care, fitness/welness systems, sport performance monitoring, interactive games, factory workers monitoring, and social physical interaction. Despite their enormous potential, they are currently mostly used only to monitor single individuals. Indeed, BSNs can proactively interact and collaborate to foster novel BSN applications centered on collaborative groups of individuals. In this paper, C-SPINE, a framework for Collaborative BSNs (CBSNs), is proposed. CBSNs are BSNs able to collaborate with each other to fulfill a common goal. They can support the development of novel smart wearable systems for cyberphysical pervasive computing environments. Collaboration therefore relies on interaction and synchronization among the CBSNs and on collaborative distributed computing atop the collaborating CBSNs. Specifically, collaboration is triggered upon CBSN proximity and relies on service-specific protocols allowing for managing services among the collaborating CBSNs. C-SPINE also natively supports multi-sensor data fusion among CBSNs to enable joint data analysis such as filtering, time-dependent data integration and classification. To demonstrate its effectiveness, C-SPINE is used to implement e-Shake, a collaborative CBSN system for the detection of emotions. The system is based on a multi-sensor data fusion schema to perform automatic detection of handshakes between two individuals and capture of possible heart-rate-based emotion reactions due to the individuals’ meeting.     

基于移动Agent的网络控制系统的研究

随着控制系统复杂性的日益扩大,控制系统的网络化是控制系统发展的必然。从网络化发展的角度来研究控制系统的发展,将更有利于自动控制、计算机网络和通讯技术的融合,为实现控制和管理信息一体化提供了一条新思路。把计算机网络和普通的控制系统相结合,并利用移动Agent技术,提出了一个基于移动Agent的网络控制系统方案。     

一种基于移动Agent的入侵检测系统

由于基于主机的入侵检测系统的局限性,使得基于移动Agent的入侵检测技术显得日益重要。本文从讨论基于主机的入侵检测系统的局限性出发,分析了入侵检测系统中主机检测器基于移动Agent的技术,提出了一种基于移动Agent的入侵检测系统模型,并对模型进行了详细分析和设计。     

基于移动Agent的投保中介的电子商务框架

商业保险的中介一直存在诸多弊端 ,本文讨论了从电子商务角度来解决这一问题的设想 ,并且提出一种基于移动Agent的保险电子商务框架。应用移动Agent来组织保险电子商务的投保业务流程 ,并利用了P3P隐私保护协议来保证隐私安全 ,从而较好地解决了中介弊端的问题     

能量有效的无线传感器网络协同覆盖

在节点随机分布的无线传感器网络目标覆盖中,考虑到单个节点有时难以完成对目标的感知,利用节点的概率感知模型和漏检率的概念,提出了节点协同覆盖的思想,并建立了协同覆盖模型;详细分析并推导了协同覆盖感知概率、节点数目和节点参与协同覆盖的最低感知概率之间的关系;在协同覆盖模型的基础上,考虑节点能量消耗的因素,设计了优化网络使用寿命的协同覆盖算法ECTC;仿真结果表明,该算法在改善网络感知概率的同时,延长了网络的使用寿命。     

基于移动Agent电子商务安全模型

本文提出了解决基于移动MA电子商务的安全问题的新模式--信任模型,介绍了信任模型的创建步骤,分析信任模型的原理及信任模型在典型的基于移动MA电子交易中的应用。     

Feature space mapping for sensor fusion

In the context of a random process scene environment model, a method is presented for fusing data from multiple sensors into a simplified, ordered space for performing electronic vision tasks. The method is based on a new discriminating measure called the tie statistic that is introduced to quantify sensor/feature performance and to provide a mapping from sensor/feature measurement space to a simplified and ordered decision space. The mapping process uses the tie statistic to measure the closeness of an unknown sample probability density function (pdf) to a known pdf for a decision class. Theorems presented in this article relate the tie statistic to minimum probability of error decision making and to the well known Kolmogorov-Smirnov distance. As examples of the sensor/feature fusion method, the tie mapping process is applied to the object location (cueing) and the texture recognition problems.     

生物启发的无线传感执行网络协同方法

本文针对无线传感执行网络(wireless sensor and actor network,WSAN),在昆虫生物种群协作机理的启发下,提出了一种WSAN协同方法,以改善网络传输可靠性、节能与执行效率.首先,针对传感器节点与执行器节点之间的协作,提出以信息传递能耗和剩余能量衡量中继节点选择概率的效能协同机制,以及包含区域中继、影响因子和学习因子3个保障单元的组织协同机制.进而,针对执行器--执行器间的协作,引入学习因子作为组织协同机制中的一个保障单元.最后,给出了算法执行的流程,并通过仿真验证了方法的优越性.仿真结果表明,采用本文提出的协同机制,不但优化了WSAN信息传递路径,而且在执行器节点间协作中选择优势节点参与信息处理和决策,降低了网络能耗,同时提高了网络执行效率.     

基于协同表示的多特征融合岩石分类

针对传统的岩石薄片成分分析耗时、识别率不高等问题,提出了一种基于协同表示(CR)的岩石薄片成分分析方法。首先,分析探讨了岩石薄片中颗粒纹理特性,证明将薄片图像的分层多尺度局部二值化(HMLBP)特征与灰度共生矩阵(GLCM)特征相融合能有效地表征岩石薄片中颗粒的纹理。然后,为降低识别阶段时间复杂度,采用主成分分析(PCA)方法将新特征降维到100维。最后,采用基于协同表示的分类器(CRC)进行分类识别。与基于稀疏表示的分类器(SRC)分别采用样本字典中某一个样本单独编码表征预测样本不同,基于协同表示的分类器采用样本字典中的所有样本协同编码表征预测样本,借助不同样本的同一属性提高识别率。实验结果表明该方法的识别速度较基于稀疏的分类器识别方法提高300%,识别率提高2%;在实践应用中能较好地区分岩石薄片中的石英成分和长石成分。     

Evidentialist foundationalist argumentation for multi-agent sensor fusion

In this paper, an approach to evidence-based argumentation called Evidentialist Foundationalist Argumentation (EFA) is formally defined in terms of the ASPIC framework. The EFA framework is then used as the basis for general argument patterns applied to the problem domain of Sensor Fusion. These general Sensor Fusion argument patterns serve as templates for concrete arguments constructed by agents in an in situ Sensor Web. These agents use EFA to solve specific instances of the Decentralized Sensor Fusion problem by strategically sharing evidence from their arguments using a Share on Disagreement protocol. Using real-world data, the performance of this multiagent system is compared to the performance of another multiagent system employing a Kalman Filtering approach. The results are statistically analyzed using omega-squared effect sizes produced by ANOVA with p values <  0.05. The EFA based system is found to outperform the Kalman Filtering system in terms of accuracy with mostly high and medium effect sizes. The Kalman Filtering system is found to outperform the EFA based system in terms of communication costs with mostly low effect sizes.     

Cokriging for cross-attribute fusion in sensor networks

Optimisation of the number of required measurement points and their location is an important research topic in sensor networks. Finding the optimal positions increases spatial coverage and reduces deployment costs. This paper presents an approach for the case that two attributes have to be measured with a different number of available sensors. The proposed cokriging method performs cross-attribute fusion in sensor networks by being based on the analysis of multi-variable spatial correlations. To the best of our knowledge, this scientific work is the first one considering kriging and cokriging interpolations as IF methods. The single-variable ordinary kriging and bi-variable methods were applied to experimental data. The combination of humidity and temperature data in a refrigerated container is used as exemplary case, humidity measurements are considered to be the expensive attribute to measure. The average estimation error for intermediate points was estimated as a function of the number of humidity sensors. When variability is high, data fusion using the bi-variable method produced results as accurate as the single-variable one, without the necessity of deploying a large number of humidity measuring points, by complementing the estimation with temperature measurements.     

Multi sensor image fusion for surveillance applications using hybrid image fusion algorithm

Multimedia Tools and Applications - Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. In...     

智能微尘传感网的轻量协作式认证算法*

提出一种轻量协作式认证算法（LCAA）,该算法使用简单的对称密钥及其生成函数和节点唤醒机制为智能微尘传感网通信提供了基本的安全,通过与其他认证算法协作,可以进一步提高网络的安全性,并可应用于普通无线传感网络中。通过仿真实验证明,该算法具有良好的安全性和能效,适用于智能微尘传感网。     

A deep learning-enabled human-cyber-physical fusion method towards human-robot collaborative assembly

Human-robot collaborative (HRC) assembly has become popular in recent years. It takes full advantage of the strength, repeatability and accuracy of robots and the high-level cognition, flexibility and adaptability of humans to achieve an ergonomic working environment with better overall productivity. However, HRC assembly is still in its infancy nowadays. How to ensure the safety and efficiency of HRC assembly while reducing assembly failures caused by human errors is challenging. To address the current challenges, this paper proposes a novel human-cyber-physical assembly system (HCPaS) framework, which combines the powerful perception and control capacity of digital twin with the virtual-reality interaction capacity of augmented reality (AR) to achieve a safe and efficient HRC environment. Based on the framework, a deep learning-enabled fusion method of HCPaS is proposed from the perspective of robot-level fusion and part-level fusion. Robot-level fusion perceives the pose of robots with the combination of PointNet and iterative closest point (ICP) algorithm, where the status of robots together with their surroundings could be registered into AR environment to improve the human's cognitive ability of complex assembly environment, thus ensuring the safe HRC assembly. Part-level fusion recognizes the type and pose of parts being assembled with a parallel network that takes an extended Pixel-wise Voting Network (PVNet) as the base architecture, on which assembly sequence/process information of the part could be registered into AR environment to provide smart guidance for manual work to avoid human errors. Eventually, experimental results demonstrate the effectiveness and efficiency of the approach.     

一种基于移动Agent的云端(Cloud-P2P)数据复合销毁机制

云端融合计算(Cloud-P2P)融合了云计算与对等计算环境的所有节点资源,实现了最大范围的协作与资源共享。数据销毁机制是保障用户数据的安全性和可控性的重要措施之一,然而云端计算环境本身的特性也给数据的有效销毁带来了困难。针对云端数据存储系统对数据的主动销毁、定时销毁和自销毁等复合需求,提出一种基于移动Agent的数据复合销毁机制,该方法不依赖第三方,利用移动Agent技术实现对过期、废弃型数据及时、有效、灵活的销毁,并在恶意主体对数据实施攻击时主动实施防御性数据销毁,有效增强了用户数据的安全性。针对节点上数据的具体销毁,还提出一种新颖的“数据折叠”的数据覆写方法,它充分利用数据本身进行销毁,有效降低了系统的开销。     

基于传感器数据融合的航空发动机电子控制器

提出了一种基于矩阵奇异分解法的传感器数据融合方法及其在某型航空发动机电子控制器设计上的应用。试验结果表明:该传感器数据融合算法简单有效,分别消除了发动机中进口空气温度T1传感器近7.8℃和涡轮后燃气温度T4传感器近19.2℃的测量误差,提高了发动机电子控制器工作的可靠性,并适应于未来航空发动机分布式控制的发展方向。     

Scalable structure-free data fusion on wireless sensor networks

Recent advancements in sensor technology, wireless networks and consequently wireless sensor networks and the increase in their applications in different fields have led to their great importance. One of the most important challenges of such networks is the distributed management of the huge amount of data produced by sensors in network to reduce data traffic in network and minimize the energy consumption. In this research, a distributed, dynamic fusion algorithm is introduced. Since the proposed method is dynamic, the number of neighbors sending data to a node is not known in advance. So in order to increase the chances of different data to meet, the node waiting time is calculated. By the end of waiting time, the node performs data fusion and sends the fused data to the best neighbor chosen by the proposed best neighbor algorithm. This procedure continues until data reaches the sink. The proposed algorithm, while being scalable and convergent, outperforms similar methods in terms of number of transmissions, traffic load and energy consumption.