一种基于NB-IoT的智能井盖监测系统设计

针对城市井盖保有量大、安全事故频发、人工巡检困难、管理组织混乱等问题,提出一种基于窄带物联网(NB-IoT)技术的窨井盖自动监测系统。该系统以STM32作为主控模块,包含倾角、水位等传感器,实现对井盖状态以及井内重点数据的采集,配合云端服务器和客户端完成井盖数据远程可视化显示。结果表明,所提系统可以实现故障井盖自动报警、精确定位,降低人工巡检的难度,让城市井盖管理更加智能化,让故障检修更加便捷化,提高城市管理的智能化水平。

面向物联网的NB-IoT信号优化方法研究

随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖不均等挑战。这些挑战不仅影响用户体验,还限制了物联网应用的进一步发展。因此,研究面向物联网的NB-IoT信号优化方法具有重要意义。文章深入研究面向物联网的NB-IoT信号优化方法,提出多种有效的优化策略和技术手段。

基于双模式端址跳变SD-IoT主动防御方法

由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。

Intelligent Internet of Things with Reliable Communication and Collaboration Technologies

The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.

Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.

Exploring Multi-Task Learning for Forecasting Energy-Cost Resource Allocation in IoT-Cloud Systems

Cloud computing has become increasingly popular due to its capacity to perform computations without relying on physical infrastructure,thereby revolutionizing computer processes.However,the rising energy consumption in cloud centers poses a significant challenge,especially with the escalating energy costs.This paper tackles this issue by introducing efficient solutions for data placement and node management,with a clear emphasis on the crucial role of the Internet of Things(IoT)throughout the research process.The IoT assumes a pivotal role in this study by actively collecting real-time data from various sensors strategically positioned in and around data centers.These sensors continuously monitor vital parameters such as energy usage and temperature,thereby providing a comprehensive dataset for analysis.The data generated by the IoT is seamlessly integrated into the Hybrid TCN-GRU-NBeat(NGT)model,enabling a dynamic and accurate representation of the current state of the data center environment.Through the incorporation of the Seagull Optimization Algorithm(SOA),the NGT model optimizes storage migration strategies based on the latest information provided by IoT sensors.The model is trained using 80%of the available dataset and subsequently tested on the remaining 20%.The results demonstrate the effectiveness of the proposed approach,with a Mean Squared Error(MSE)of 5.33%and a Mean Absolute Error(MAE)of 2.83%,accurately estimating power prices and leading to an average reduction of 23.88%in power costs.Furthermore,the integration of IoT data significantly enhances the accuracy of the NGT model,outperforming benchmark algorithms such as DenseNet,Support Vector Machine(SVM),Decision Trees,and AlexNet.The NGT model achieves an impressive accuracy rate of 97.9%,surpassing the rates of 87%,83%,80%,and 79%,respectively,for the benchmark algorithms.These findings underscore the effectiveness of the proposed method in optimizing energy efficiency and enhancing the predictive capabilities of cloud computing systems.The IoT plays a critical role in driving these advancements by providing real-time data insights into the operational aspects of data centers.

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

Vector Dominance with Threshold Searchable Encryption (VDTSE) for the Internet of Things

The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.

A Few-Shot Learning-Based Automatic Modulation Classification Method for Internet of Things

Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.

一种基于IOT的智能电子围栏设计

针对电子围栏系统安全、经济和检测精度需求,提出一种智能电子围栏监控方案。设计电子围栏系统功能结构、系统架构,并在此基础上设计软硬件组成方案;提出电子围栏回波信号的预处理、特征提取和侵入行为检测模型;以某公司提供的电子围栏数据为基础,验证所提方案有效性。结果表明:该特征提取模型能够有效提取信号特征,提升分类性能,回波检测模型分类准确率为89.3%;该方案为电子围栏系统安全管理及稳定运行提供了一定借鉴。

Potential Benefits and Obstacles of the Use of Internet of Things in Saudi Universities: Empirical Study

Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of things (devices) that can be connected through the internet. The purpose: this paper aims to explore the concept of the Internet of Things (IoT) generally and outline the main definitions of IoT. The paper also aims to examine and discuss the obstacles and potential benefits of IoT in Saudi universities. Methodology: the researchers reviewed the previous literature and focused on several databases to use the recent studies and research related to the IoT. Then, the researchers also used quantitative methodology to examine the factors affecting the obstacles and potential benefits of IoT. The data were collected by using a questionnaire distributed online among academic staff and a total of 150 participants completed the survey. Finding: the result of this study reveals there are twelve factors that affect the potential benefits of using IoT such as reducing human errors, increasing business income and worker’s productivity. It also shows the eighteen factors which affect obstacles the IoT use, for example sensors’ cost, data privacy, and data security. These factors have the most influence on using IoT in Saudi universities.

面向高吞吐量的NB‒IoT低轨卫星物联网资源调度

窄带物联网(NB-IoT)作为一种低功耗广域网技术,专门设计用于连接大量低功耗设备。基于该技术的低轨卫星物联网有着较低的传输损耗和时延,并且可以通过星座方式对地球实现无缝覆盖。然而,低轨卫星具有高度动态性,并且面临来自不同用户的QoS需求,这些因素使得现有资源调度算法的吞吐量性能受到了极大的影响。针对这些挑战,本文在海量物联网用户请求无线资源且时频资源有限的场景下,综合考虑卫星信道特性、不同用户间的可靠性与时延要求以及卫星高动态性引起的差分多普勒等因素,提出了一种面向高吞吐量的NB-IoT低轨卫星物联网资源调度算法。仿真结果表明,相比现有方法,提出的资源调度算法在系统吞吐量方面表现出显著的性能提升。

基于NB-IoT的农业物联网智能灌溉控制系统设计与开发

本文针对农业灌溉中存在的水资源浪费和传统灌溉方式效率低下等问题,提出了一种新型的智能灌溉控制系统。该系统利用NB-IoT技术实现远程监控控制,通过感知节点采集土壤湿度、温度、光照等数据,并结合先进的数据分析算法,实现对灌溉系统的智能调控。实验结果显示,该系统为农业生产提供可持续发展的解决方案,在提高灌溉效率和减少水资源浪费方面效果显著。

一套基于NB-IoT的城市智能停车监控系统

智慧城市的第一步是智慧出行。智能停车系统为智慧出行提供基础设施保障。NB-IoT技术以其低功耗、广覆盖、低成本、窄带宽需求等特征,被广泛应用于物联网。文中在研究基于NB-IoT技术的智能停车解决方案基础上,提出并落地了基于信令DPI的端到端智能质量监控系统,以提升智能停车解决方案的可靠性与准确性。

NB-IoT和eMTC远程无线物联网通信开发应用

探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。详细介绍基于NB-IoT的智慧水表系统和基于eMTC的车辆跟踪系统的设计与实现,展示这些系统在提高城市管理效率、物流监控等方面的积极作用。针对网络覆盖与信号质量、数据安全与隐私保护、功耗与续航等关键技术挑战,提出相应的解决方案。最后总结NB-IoT和eMTC的广阔应用前景和市场潜力,并对未来技术发展和应用趋势进行展望。

Mi-IoT智能互联设备在实验室日常管理中的应用

随着物联网和智能家居技术的蓬勃发展,实验室作为科研创新的重要场所,其智能化水平对于提高科研效率、优化实验环境至关重要。米家作为智能家居领域的知名品牌,其智能设备在实验室中的应用潜力巨大。以米家智能设备为例,探讨其在实验室中的应用管理,旨在提高实验室的智能化水平,优化实验环境,提升测试效率。首先,概述了智能设备的基本概念;然后,详细分析了米家智能设备在实验室中的具体应用;接着,探讨了智能设备的集成与管理平台;最后,对应用效果进行了展望。

基于自适应控制算法的NB-IoT网络性能优化研究

随着物联网技术的快速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)技术因其低功耗、广覆盖、大容量等特性,成为物联网的重要连接方式。针对NB-IoT网络性能优化问题,提出基于自适应控制算法的优化方案,提高其可靠性、容量及能效。通过仿真实验,验证该方案的有效性和性能优势。此外,基于该算法,采用终端感知、网络通信、数据处理以及应用表现4层系统设计架构,设计基于自适应控制算法的NB-IoT物联网系统,满足不断增长的物联网应用需求。

基于eDRX模式的NB-IoT阀控水表的研究

当前市场上NB-IoT电池供电阀控水表使用PSM模式,关阀期间通过增加上报频率,尽可能降低阀控响应延时。但上报频率的增加会造成功耗的显著增加,功耗和阀控响应延时两个变量相互制约,为满足电池使用寿命,一般阀控期间上报频率1小时,阀控响应实时性低,时延达1小时。针对该问题,提出一种在不增加功耗的前提下,缩短阀控响应时延的方法。该方法通过基于NB-IoT技术的eDRX模式,采用网络快速释放方法并优化通讯交互逻辑,实现缩短阀控响应时延并降低整机功耗。测试结果显示,与现有PSM模式的阀控水表相比,电池使用年限正常,阀控响应时延由1小时降低至40.96秒,对阀控水表产品未来的发展具有一定的参考意义。

面向物联网的NB-IoT低功耗通信协议优化研究

文章深入研究物联网中的窄带物联网(Narrow Band Internet of Things,NB-IoT)技术,重点探讨基于省电模式(Power Saving Mode,PSM)机制的优化方法,分析NB-IoT的数学原理,研究一种基于PSM机制的动态调整算法,并利用MATLAB平台上的Simulink工具进行仿真实验。实验结果显示,相比传统方法,文章方法能显著降低平均功耗,在实际应用中具有有效性和稳定性。