Design and Implementation of Secure Nodes in the Based-Internet-of-Things Intelligent Household

As one of the most important uses of the Internet of things (IOT), the intelligent household is becoming more and more popular. There are many fragile nodes in the intelligent household and they are bound to encounter some potential risks of hostile attacks, such as eavesdropping, denial of service, error instructs, non-authorized access or fabrication and others. This paper presents a method of design and implement of secure nodes for the intelligent household based on the IOT technology, besides giving the hardware model of nodes, the management of key, the access authentication of network, the transmission of encrypted data, and the alarm based on intrusion detection and other security mechanisms. That is, to improve the security of the based-IOT intelligent household from the viewpoint of nodes security. A test platform is built and the results of simulation prove that the proposed method can effectively improve the security of the intelligent household from access safety and transmission security.

Intelligent Aquila Optimization Algorithm-Based Node Localization Scheme for Wireless Sensor Networks

In recent times,wireless sensor network(WSN)finds their suitability in several application areas,ranging from military to commercial ones.Since nodes in WSN are placed arbitrarily in the target field,node localization(NL)becomes essential where the positioning of the nodes can be determined by the aid of anchor nodes.The goal of any NL scheme is to improve the localization accuracy and reduce the localization error rate.With this motivation,this study focuses on the design of Intelligent Aquila Optimization Algorithm Based Node Localization Scheme(IAOAB-NLS)for WSN.The presented IAOAB-NLS model makes use of anchor nodes to determine proper positioning of the nodes.In addition,the IAOAB-NLS model is stimulated by the behaviour of Aquila.The IAOAB-NLS model has the ability to accomplish proper coordinate points of the nodes in the network.For guaranteeing the proficient NL process of the IAOAB-NLS model,widespread experimentation takes place to assure the betterment of the IAOAB-NLS model.The resultant values reported the effectual outcome of the IAOAB-NLS model irrespective of changing parameters in the network.

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence(AI)techniques have received significant attention among research communities in the field of networking,image processing,natural language processing,robotics,etc.At the same time,a major problem in wireless sensor networks(WSN)is node localization,which aims to identify the exact position of the sensor nodes(SN)using the known position of several anchor nodes.WSN comprises a massive number of SNs and records the position of the nodes,which becomes a tedious process.Besides,the SNs might be subjected to node mobility and the position alters with time.So,a precise node localization(NL)manner is required for determining the location of the SNs.In this view,this paper presents a new quantum bird migration optimizer-based NL(QBMA-NL)technique for WSN.The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes.The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season.In addition,an objective function is derived based on the received signal strength indicator(RSSI)and Euclidean distance from the known to unknown SNs.For demonstrating the improved performance of the QBMA-NL technique,a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.

基于Node-red的云端智能生产线控制系统设计与实现

针对传统生产线在刚性生产过程中面临的成本过高、远程实施难、兼容性低等关键问题,基于目前中小企业的技术基础相对薄弱,无法将云计算平台、数据存储和处理等设备及技术运用到实际生产中的现状,本文利用西门子S7-1200 PLC作为核心控制器,基于Node-red平台设计了成本低、兼容性高的云端智能生产线监控系统,便于实现对于生产线的监视和控制,提高生产效率与产品质量。现场运行表明,该系统运行稳定,满足智能生产线的设计要求,同时具备部署成本低、兼容性高的突出优势。

基于NodeMCU的智能浇灌系统设计

当前,智能化技术不断发展,为了提升现代农田的智能化管理水平,设计以NodeMCU(节点微控制单元)作为中央控制器的智能浇灌系统,实现农田灌溉智能化。YL-69土壤湿度传感器用于检测土壤含水量,DHT11温湿度传感器用于检测环境温度和湿度,中央控制器负责数据的采集与处理,通过内置的ESP8266芯片连接Wi-Fi将数据无线传输至网页端进行实时监测与控制,并对比系统设置的阈值指标,反馈至微控制器,微控制器通过控制继电器打开水泵对土地浇灌,并且搭建数据库进行数据记录与分析。该系统结构简单,可靠性较高,可实现浇灌的无人化管理,具有重要的现实意义。

基于AT89C2051单片机的VCN-MIO智能节点模板电路设计

设计基于AT89C2051单片机的VCN-MIO智能节点模板电路,主要为VCN-MIO智能节点模板的总体设计:TP/FT-10F主控核心模块电路设计、VCN-MIO底板与扩展模块的电路设计、节点故障诊断和抗干扰设计等。应用实践证明,该VCN-MIO智能节点模板的智能节点数大大增多;通信、控制调度、实时性、可靠性大大增强;性价比大大提高;能够满足目前现代工业过程控制领域复杂测控系统的高要求。

基于NodeBuilder的智能节点开发设计

阐述了节点开发设计的过程,在硬件设计部分重点说明了神经元芯片如何扩展外部存储器,在软件设计部分主要论述了节点的接口文件和应用程序的定义。

ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network

A wireless sensor network(WSN)consists of several tiny sensor nodes to monitor,collect,and transmit the physical information from an environment through the wireless channel.The node failure is considered as one of the main issues in the WSN which creates higher packet drop,delay,and energy consumption during the communication.Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets.In this paper,Artificial Neural Network(ANN)based Node Failure Detection(NFD)is developed with cognitive radio for detecting the location of the node failure.The ad hoc on-demand distance vector(AODV)routing protocol is used for transmitting the data from the source node to the base station.Moreover,the Mahalanobis distance is used for detecting an adjacent node to the node failure which is used to create the routing path without any node failure.The performance of the proposed ANN-NFD method is analysed in terms of throughput,delivery rate,number of nodes alive,drop rate,end to end delay,energy consumption,and overhead ratio.Furthermore,the performance of the ANN-NFD method is evaluated with the header to base station and base station to header(H2B2H)protocol.The packet delivery rate of the ANN-NFD method is 0.92 for 150 nodes that are high when compared to the H2B2H protocol.Hence,the ANN-NFD method provides data consistency during data transmission under node and battery failure.

基于NODE.JS/GPRS/ZigBee的水产养殖远程测控系统的设计

为了满足水产养殖智能化的要求[1],设计了水产养殖水质参数的远程实时监控系统。该系统由水质参数无线监测部件RTU、远程数据管理服务器和客户终端APP组成。RTU采用STM32微处理器方案,实现水质数据的采集和GPRS无线远程数据的传输;远程数据管理服务器,可以接收所有RTU传输过来的数据,进行智能判断并且保存,供APP客户随时访问。利用Zig Bee和NODE.JS[2~3]技术,此系统不仅满足了无线数据采集和数据传输的相关指标要求,而且有效地解决了水产养殖系统中布线困难,通信费用过高、连接并发数不高和实时性不强等问题。

Artificial intelligence:A new budding star in gastric cancer

The pursuit of health has always been the driving force for the advancement of human society,and social development will be profoundly affected by every breakthrough in the medical industry.With the arrival of the information technology revolution era,artificial intelligence(AI)technology has been rapidly developed.AI has been combined with medicine but it has been less studied with gastric cancer(GC).AI is a new budding star in GC,and its contribution to GC is mainly focused on diagnosis and treatment.For early GC,AI’s impact is not only reflected in its high accuracy but also its ability to quickly train primary doctors,improve the diagnosis rate of early GC,and reduce missed cases.At the same time,it will also reduce the possibility of missed diagnosis of advanced GC in cardia.Furthermore,it is used to assist imaging doctors to determine the location of lymph nodes and,more importantly,it can more effectively judge the lymph node metastasis of GC,which is conducive to the prognosis of patients.In surgical treatment of GC,it also has great potential.Robotic surgery is the latest technology in GC surgery.It is a bright star for minimally invasive treatment of GC,and together with laparoscopic surgery,it has become a common treatment for GC.Through machine learning,robotic systems can reduce operator errors and trauma of patients,and can predict the prognosis of GC patients.Throughout the centuries of development of surgery,the history gradually changes from traumatic to minimally invasive.In the future,AI will help GC patients reduce surgical trauma and further improve the efficiency of minimally invasive treatment of GC.

基于NodeMCU的智能玻璃百叶窗控制系统的设计

基于Node MCU设计了智能玻璃百叶窗控制系统,使用PWM控制信号控制数字舵机的旋转角度,根据传感器反馈信号自动对百叶窗进行开合调整,使用PID控制算法提高了控制精度及稳定性。通过传感器实时监测室内外光线及温度等环境参数,以优化室内光线及温度,提高舒适度与能源利用率。该系统通过WiFi连接实现与手机端的通信,支持手动控制与远程控制,便于用户随时随地对百叶窗进行调节,可广泛应用于住宅、办公室、商业建筑等场所,具有较高的实用价值及市场潜力,为智能家居的发展提供了新思路。

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Major fields such as military applications,medical fields,weather forecasting,and environmental applications use wireless sensor networks for major computing processes.Sensors play a vital role in emerging technologies of the 20th century.Localization of sensors in needed locations is a very serious problem.The environment is home to every living being in the world.The growth of industries after the industrial revolution increased pollution across the environment.Owing to recent uncontrolled growth and development,sensors to measure pollution levels across industries and surroundings are needed.An interesting and challenging task is choosing the place to fit the sensors.Many meta-heuristic techniques have been introduced in node localization.Swarm intelligent algorithms have proven their efficiency in many studies on localization problems.In this article,we introduce an industrial-centric approach to solve the problem of node localization in the sensor network.First,our work aims at selecting industrial areas in the sensed location.We use random forest regression methodology to select the polluted area.Then,the elephant herding algorithm is used in sensor node localization.These two algorithms are combined to produce the best standard result in localizing the sensor nodes.To check the proposed performance,experiments are conducted with data from the KDD Cup 2018,which contain the name of 35 stations with concentrations of air pollutants such as PM,SO_(2),CO,NO_(2),and O_(3).These data are normalized and tested with algorithms.The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm,particle swarm optimization,and machine learning algorithms such as decision tree regression and multi-layer perceptron.Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology.Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.

Node-RED用于简化调整传送系统运行逻辑

生产线运行逻辑更改时通常需要对其PLC程序进行调整,该任务一般需受过培训的工程师使用专业的工程电脑来完成。面对专业技术人员培养周期较长的现状,如何简化生产线运行逻辑调整难度,降低现场自动化编程的学习成本成为当务之急。对Node-RED这一面向对象的编程工具在某药瓶传送系统运行逻辑调整时进行应用,使操作者通过较低的学习成本即可完成对该传送系统运行逻辑的调整,可以极大提高现场工作的效率。

改进流向算法的无线传感器网络覆盖优化

针对标准流向算法易陷入局部最优和收敛精度低等问题,提出一种融合莱维(Levy)飞行和入侵杂草策略的改进流向算法.首先,该算法在选择水流流向时引入Levy飞行机制,使水流沿最优水流位置方向做Levy飞行运动,避免陷入局部最优;其次,利用入侵杂草策略,对每一代水流进行繁殖、空间扩散和竞争操作,增加水流的多样性,扩大搜索范围,提高全局寻优能力.最后,将改进流向算法应用于无线传感器网络覆盖优化中,并与标准流向算法及其他改进算法进行实验对比.仿真结果表明,相比标准流向算法及其他改进算法,所提改进流向算法的覆盖性能有大幅提升,覆盖率可达98.52%,可实现更均匀的节点分布和更低的部署成本.

基于DTN路由的多通路精准灌溉系统布局设计

为进一步提升我国智能灌溉装备的综合作业效率,从多通路实施与精准化控制角度展开布局设计。以灌溉系统的作业原理及田间信息获取特征为出发点,建立灌溉网络的DTN路由算法模型,实施网络节点的规划分配与通道布置,形成精准性的逻辑控制流程及节点网络相互执行关系。进行系统性的灌溉试验,结果表明:基于DTN路由算法的多通路精准灌溉系统布局正确可行,信息投递成功率可达94.13%,节点转发率可达96.07%,各信息传递的实时准确,各阀门组件的动作协调,综合灌溉效率实现了高于90%的优化目标,是DTN路由控制理念在灌溉装备领域的应用创新思路之一,具有较好的推广意义。

基于核密度估计模型优化手术调度的设计与应用

目的针对现有手术患者转运平台转运流程中,手术状态节点变动存在人工操作依赖性和滞后性、手术衔接不高效的问题,改进手术患者转运平台的手术流转流程。方法结合集成蓝牙低能耗技术的智能手环、人员定位系统,运用核密度估计模型估算手术平均时长,从而改进手术患者转运平台的手术流转流程,并对模型的有效性进行验证。结果应用改进的手术转运平台后,首台延迟率、手术衔接时间较改进前下降6.2%、8.0 min,日均麻醉利用率和日间手术利用率较应用前提高了6.9%、6.4%,且差异均有统计学意义(P<0.01)。结论通过应用改进后的手术患者转运平台,手术室手术衔接效率更高,手术调度更加智能化,可有效提高手术室效率。

基于深度强化学习的移动通信网络空洞节点智能定位方法

在定位移动通信网络空洞节点时,易受路径选择阶段的局部最优问题影响,使定位效果难以达到理想状态.为此,提出基于深度强化学习的移动通信网络空洞节点智能定位方法.在确定移动通信网络中不同节点之间的位置信息阶段时,采用加权处理的方式对每跳距离信息进行量化,再结合加权平均跳距和节点与锚节点的距离,确定移动通信网络空洞节点定位步距.在空洞节点定位阶段引入深度强化学习中的灰狼算法,并从快速收敛的角度出发,利用模拟退火和混沌映射对原始的灰狼算法进行优化,将最终灰狼奔走围攻对象作为移动通信网络空洞节点的定位结果.测试结果表明,本文设计的定位方法能够降低空洞节点定位阶段的路径开销,在定位精度方面有良好表现,定位效率也明显提升.

基于无线传感网络的电气设备温度智能监测系统

为防止由于温度异常造成的电气设备故障,提出基于无线传感网络的电气设备温度智能监测系统。利用无线传感网络及ZigBee网络采集节点,低功耗采集并传输电气设备温度信息到监测中心,利用DNN网络构建电气设备温度监测智能分析模型,实现电气设备温度的实时输出与处理。实验表明,该系统的信息传输功耗较低;可精准监测电气设备温度,可通过界面形式展现监测结果。

多级智慧供应链网络节点动态优先级排序系统

多级智慧供应链网络中的节点错排问题,使得数据信息的传输受到影响,导致节点动态优先级排序精准度较低。为此,设计了多级智慧供应链网络节点动态优先级排序系统。在多级SDN拓扑网络中,确定供应链节点的排列方式,完成多级智慧供应链网络的一般性结构设计。对网络节点实施无标度处理,根据动态参数标准化执行结果,计算优先级特征向量与排序介数的取值范围,实现对节点对象的排序处理,完成多级智慧供应链网络节点动态优先级排序系统的设计。实验结果表明,设计系统作用下,能够有效解决多级智慧供应链网络中的节点错排问题,提高多级智慧供应链网络节点动态优先级排序精准度。

影像学方法在鼻咽癌颈淋巴结转移诊断中的应用进展

鼻咽癌是一种恶性度较高的癌症,早期颈部淋巴结转移为其典型特征之一,显著影响患者预后。应用影像学方法进行颈部淋巴结检查可尽早筛查出鼻咽癌。本文简要概述了鼻咽癌颈部淋巴结转移的主要影像学特征,探讨了MRI、CT、超声等常用影像学诊断方法和PET、荧光成像等新技术在鼻咽癌颈淋巴结转移诊断中的研究进展,以及人工智能辅助影像诊断转移性颈部淋巴结的最新应用。随着人工智能等新技术的不断发展,影像学方法在鼻咽癌颈部淋巴结转移诊断中的应用将更加精准,可为疾病的早期发现、精准治疗及预后评估提供有力支持。