基于物联网技术的网络异构传感数据融合方法

融合网络异构数据时，现有融合方法存在融合精度低和融合时间长问题，因此引入物联网技术，提出一种新的融合方法。首先，引入小波变换的方法，过滤网络异构传感器数据噪声；其次，利用物联网技术，对物联网簇内数据聚类；最后，通过最小二乘法运算，实现汇聚网络异构传感器数据融合。实验结果证明，新的融合方法可有效提高融合结果的精度，缩短融合时间，提升融合效率，与现有融合方法相比具备更高的实际应用价值。     

物联网下多无线传感网络中不同信道信息融合方法

物联网结构下多个无线传感器组成的不同网络差异较大。当差异化的数据量急剧增加时，多信道信息之间易产生信息孤岛，导致信息融合的能耗大、精确度和节点存活率低。为此，提出一种物联网下多无线传感网络中不同信道信息融合方法。将传感器网络与多输入多输出(Multiple-Input Multiple-Output, MIMO)通信系统结合，构建传感器模型。通过物联网技术采集数据信息，利用传感器信息融合模型对收集的信息进行处理后，传送到融合中心，完成多信道信息的融合。仿真结果表明，所提方法在实施多信道信息融合过程中，传输能耗最低达到0.14 J,信息融合节点存活率和精度值分别达到97%和92%,证明所提方法的能耗小、信息融合节点存活率和精确度高。     

WSNs应用于农作物生长环境监测模型研究

在农业领域引入物联网技术,可以从根本上改变传统农业生产模式——从依赖于孤立机械、以人力为中心的生产模式转向以信息和软件为中心的现代农业生产模式。在分析无线传感器网络及物联网技术基础上,研究建立了一种物联网技术下无线传感器网络应用于农作物生长环境监测的系统模型,通过无线传感器网络实时、动态、精准地获取农作物生长区域环境数据,并自行组网将采集到的感应数据传递到上层网关接入点,由网关通过广域物联网信息传输技术提交到应用服务中心,经农业工作者分析处理后再对农作物环境进行精准、自动化的管理与控制。     

基于无线传感器网络的矿井应急通信系统的研究

文章介绍了一种基于无线传感器网络的矿井应急通信系统的设计方案,阐述了无线传感器网络的结构、特点及无线传感器网络技术、移动通信技术在矿井应急通信系统中的应用设计。该矿井应急通信系统利用无线传感器网络进行紧急状态下矿井数据的采集和收集,利用成熟、信号覆盖面广的移动通信技术进行信号的传输,将数据实时地传送到处理中心和相关的责任人,便于对紧急情况的决策处理。     

基于物联网网关的传感器接入方案设计

物联网被看作信息领域一次重大的发展和变革机遇,物联网网关是连接传感器网络与互联网网络的接入与控制设备,传感器收集数据并通过网关实现与互联网的互连及进一步的处理和转发。本文首先介绍了物联网技术背景,在此基础上提出了基于物联网网关的传感器接入方案,并以LDAP作为开发环境,实现了传感器接入的信息存储方案。     

基于物联网的农业感知数据融合应用研究

随着物联网技术的不断发展,在农业领域中物联网技术得到了广泛的应用.农业物联网主要是通过传感器技术,对农业领域生产和管理的数据信息进行收集,所以农业感知数据的融合在农业物联网应用中是非常重要的.对农业物联网技术及农业感知数据的特点进行了分析,对农业物联网中农业感知数据融合的方法进行了研究,结合农业物联网技术应用的实际情况,对基于物联网的农业感知系统的硬件和软件进行了设计和研究.     

地震微观前兆预报网络系统设计研究(2-2)——基岩传感器网络和数据融合技术

提出了地震微观前兆预报网络系统设计的一整套详细方案,包括地震模型、基岩传感器网络和数据融合、信号处理技术和网络系统设计.本文介绍传感器网络和智能数据融合技术,包括:(1)无线传感器网络.(2)多传感器数据融合技术;(3)多平台多传感器目标跟踪解耦相关算法;(4)传感器网络中合作信号和信息处理技术;(5)传感器网络中分布目标的分类和跟踪.     

基于多传感器的语义数据融合方法研究

物联网已经渗透到各个领域,并对各领域数据进行自动采集和应用。但异构数据的存储和融合一直是物联网的技术难题,使数据信息不能被理解,阻碍传感器的应用。因此设计高质量的数据融合算法,具有重大实际应用价值。本课题拟通过语义,根据物联网传感器数据融合的理论分析与本体建模的方法,以及传感器本体和语义融合的一般方法,提出基于多传感器的语义数据融合方法,促进语义在物联网多传感器数据融合中的应用,为物联网系统的智能控制和决策分析打下基础,加快物联网在各领域的快速发展和深入应用,有一定的经济和社会效益。     

地震微观前兆预报网络系统设计研究(2-2)——基岩传感器网络和数据融合技术

提出了地震微观前兆预报网络系统设计的一整套详细方案,包括地震模型、基岩传感器网络和数据融合、信号处理技术和网络系统设计。本文介绍传感器网络和智能数据融合技术,包括：（1）无线传感器网络;（2）多传感器数据融合技术;（3）多平台多传感器目标跟踪解耦相关算法;（4）传感器网络中合作信号和信息处理技术;（5）传感器网络中分布目标的分类和跟踪。     

基于物联网技术的货车载荷实时监测系统设计

针对目前货车载荷监测主要依靠固定式称重的现状,设计了一种基于物联网技术的货车载荷实时监测系统。系统以嵌入式系统和多传感器融合算法为核心,由信号采集,信号处理与传输,云服务器等单元组成。采用主控芯片STM32F105RCT6和电阻式应变片传感器,实现数据采集与数据处理;处理后的信息通过无线通信模块发送至云端,云端服务器利用神经网络技术对所得到的数据进行拟合处理,实现了用户在远程情况下对货车的载重量进行实时的监测与管控。实验测试证明,本系统能够准确,高效地采集到货车载荷量,并且所测货车载荷量的精度在2.75%以内,无线传输效果稳定,并能将载荷数据实时上传至云平台,反馈于用户。本系统综合运用了智能传感器、物联网和云计算等技术,具有实时强、精度高、装配便捷等特点。     

Real-time data analytics and event detection for IoT-enabled communication systems

Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and filtering, as well as stream reasoning for decision support. Our main contributions are the following, (i) we define and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; (ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; (iii) we define and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, (iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.     

SeaCloudDM: a database cluster framework for managing and querying massive heterogeneous sensor sampling data

Recent advances in sensor networks and communication technologies have made the Internet of Things (IoT) a hot research issue. An IoT system can sample and manage the historical and present states of various kinds of physical and virtual objects such as vehicles, lakes, mountains, dams, city traffic conditions, atmosphere qualities, and so forth. It is well acknowledged that IoT will greatly change the way how people live and work. However, IoT also brings about great challenges to the data management community. For instance, the data to be managed in IoT are highly dynamic and heterogeneous. Meanwhile, since the sensor sampling data are managed in a centralized manner, the data size can be huge. Moreover, sensor data are intrinsically spatial-temporal data which may involve complicated spatial-temporal computations in query processing. To meet these challenges, we propose a novel Sea-Cloud-based Data Management (SeaCloudDM) mechanism in this paper. The experimental results show that the SeaCloudDM mechanism provides satisfactory performances in managing and querying massive sensor sampling data, and is thus a viable solution for IoT data management.     

A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion

Internet of Things (IoT) aims to create a world that enables the interconnection and integration of things in physical world and cyber space. With the involvement of a great number of wireless sensor devices, IoT generates a diversity of datasets that are massive, multi-sourcing, heterogeneous, and sparse. By taking advantage of these data to further improve IoT services and offer intelligent services, data fusion is always employed first to reduce the size and dimension of data, optimize the amount of data traffic and extract useful information from raw data. Although there exist some surveys on IoT data fusion, the literature still lacks comprehensive insight and discussion on it with regard to different IoT application domains by paying special attention to security and privacy. In this paper, we investigate the properties of IoT data, propose a number of IoT data fusion requirements including the ones about security and privacy, classify the IoT applications into several domains and then provide a thorough review on the state-of-the-art of data fusion in main IoT application domains. In particular, we employ the requirements of IoT data fusion as a measure to evaluate and compare the performance of existing data fusion methods. Based on the thorough survey, we summarize open research issues, highlight promising future research directions and specify research challenges.     

基于序列模型的无线传感网入侵检测系统

随着物联网(IoT)的快速发展，越来越多的IoT节点设备被部署，但伴随而来的安全问题也不可忽视。IoT的网络层节点设备主要通过无线传感网进行通信，其相较于互联网更开放也更容易受到拒绝服务等网络攻击。针对无线传感网面临的网络层安全问题，提出了一种基于序列模型的网络入侵检测系统，对网络层入侵进行检测和报警，具有较高的识别率以及较低的误报率。另外，针对无线传感网节点设备面临的节点主机设备的安全问题，在考虑节点开销的基础上，提出了一种基于简单序列模型的主机入侵检测系统。实验结果表明，针对无线传感网的网络层以及主机层的两个入侵检测系统的准确率都达到了99%以上，误报率在1%左右，达到了工业需求，这两个系统可以全面有效地保护无线传感网安全。     

基于D-S证据理论的传感器网络数据融合算法

在传感器网络中，多个传感器对于同一目标的识别结果经常会发生冲突，本文采用基于Dempster—Sharer证据推理理论的数据融合方法来解决这一问题。然而，采用D—S证据组合公式计算融合结果，计算量过于巨大，对处理能力有限的感知结点来说负担过重，此外，计算所造成的延时也将严重影响系统的实时性和同步性．本文提出了一个基于矩阵分析的快速融合算法，该算法采用了D—S证据理论的思想，计算得到的融合结果与D—S证据组合公式计算得到的融合结果相同．本文用数学归纳法证明了这一结论，经过模拟实验验证，和直接采用D—S证据组合公式相比，该算法的计算量和所需的计算时间明显减少．     

基于Arduino平台的物联网网关设计

本文在Arduino平台的基础上详细设计并实现了一种新型的IoT网关,它使用了面向对象的设计方式屏蔽了与感知层、网络接入层以及和云平台交互的细节,并提供了通用的交互接口和互操作协议.本文在最后,利用ZigBee无线传感器网络作为感知层,YeeLink云平台作为服务器,使用设计的IoT网关搭建了一个具体的IoT系统,并通过对系统进行功能测试和性能测试验证了IoT网关的可用性,其中丢包率为0.2％,平均时延为9.906 ms,可以很好满足多种应用.     

IOTSim: A simulator for analysing IoT applications

A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.     

嵌入式机房多功能模块智能监控系统设计

为充分提高嵌入式机房运维能力,及时排除异常情况,提出嵌入式机房多功能模块智能监控系统。设计信号采集层、数据传输层、后台监控层的多层架构模式。硬件结构设计用户登录模块、传感器运行模块、数据记录模块和监控显示模块,四个功能模块在多层架构下工作。软件部分通过数据传输程序连接终端设备与云计算中心,利用数据处理程序完成功能模块参数设置,完成嵌入式机房运行数据自动监控。实验结果表明,所设计系统数据采集精度高,可以实时响应接入设备,及时获取预警信息,实现嵌入式机房自动智能监控。