Internet of Lamps for Future Ubiquitous Communications:Integrated Sensing,Hybrid Interconnection,and Intelligent Illumination

The ubiquitous deployment and restricted consumption are the requirements restricting the development of Internet of Things.Thus,a promising technology named Internet of Lamps(Io L)is discussed in this paper to address these challenges.Compared with other communication networks,the remarkable advantage of Io L is that it can make full use of the existing lighting networks with sufficient power supply.The lamps can be connected to the Internet through wired power line communication and/or wireless communication,while the integration of integrated sensing,hybrid interconnection,and intelligent illumination is realized.In this paper,the Io L is discussed from three aspects including sensing layer,network layer,and application layer,realizing the comprehensive upgrade based on the conventional communication and illumination systems.Meanwhile,several novel technologies of Io L are discussed based on the requirements of sensing,communication,and control,which have put forward practical solutions to the issues faced by Io L.Moreover,the challenges and opportunities for Io L are highlighted from various parts of the system structure,so as to provide future insights and potential trends for researchers in this field.

Examining the Interplay between Trait Mindfulness and Thriving at Work among Teachers in Western China:The Mediating Role of General Self-Efficacy

This study explores the nuanced relationship between trait mindfulness and thriving at work among educators in Western China,emphasizing the mediating role of general self-efficacy.Employing a sample of 378 primary and secondary school teachers,this research utilizes the Five Facet Mindfulness Questionnaire(FFMQ),Thriving at Work Scale(TWS),and the General Self-Efficacy Scale(GSES)to conduct a thorough investigation.The findings indicate a significant positive correlation between trait mindfulness and thriving at work,between trait mindfulness and general self-efficacy,and between general self-efficacy and thriving at work.Additionally,trait mindfulness was found to have a positive predictive effect on both thriving at work and general self-efficacy,with general self-efficacy also showing a positive predictive effect on thriving at work.Importantly,general self-efficacy was identified as playing a partial mediating role in the relationship between trait mindfulness and thriving at work.These results underscore the importance of cultivating mindfulness and self-efficacy among teachers to enhance their enthusiasm for work,suggesting potential pathways for professional development and well-being in the educational sector.

面向Internet的分布式遥感影像切割算法

为了实现遥感影像的网络快速发布,提出了一种高效的影像金字塔模型的构建算法,影像的切割与重采样均在内存中运算完成,尽可能地减少I/O操作,从而提高其执行效率。提出了面向Internet的分布式遥感影像的切割算法,并给出了基于B/S架构和基于C/S架构的两种实现方式。为了保证算法的效率与分布式结构,利用C++与Java联合编程进行实现。通过实例对算法性能进行测试,证明了算法的可操作性、实用性和高效性。

INTERNET上的地理信息科学资源

Internet具有重要的地理信息科学资源。地理信息的标准是保证网络传播地理信息的前提条件,也是推动地理信息科学数据共享的基础。借助于搜索工具,点击与地理信息科学相关的一些网站或者关键词,就可以寻找到所需要的地理信息资源。众多的RS、GIS、GPS、制图、数字地球、全球变化、资源环境以及各种出版物和研究机构等网站,可以为地理信息科学的研究提供重要条件,网络资源需要进一步的挖掘和应用。

MEC Enabled Cooperative Sensing and Resource Allocation for Industrial IoT Systems

In industrial Internet of Things systems,state estimation plays an important role in multisensor cooperative sensing.However,the state information received by remote control center experiences random delay,which inevitably affects the state estimation performance.Moreover,the computation and storage burden of remote control center is very huge,due to the large amount of state information from all sensors.To address this issue,we propose a layered network architecture and design the mobile edge computing(MEC)enabled cooperative sensing scheme.In particular,we first characterize the impact of random delay on the error of state estimation.Based on this,the cooperative sensing and resource allocation are optimized to minimize the state estimation error.The formulated constrained minimization problem is a mixed integer programming problem,which is effectively solved with problem decomposition based on the information content of delivered data packets.The improved marine predators algorithm(MPA)is designed to choose the best edge estimator for each sensor to pretreat the sensory information.Finally,the simulation results show the advantage and effectiveness of proposed scheme in terms of estimation accuracy.

Data Traffic Reduction with Compressed Sensing in an AIoT System

To provide Artificial Intelligence(AI)services such as object detection,Internet of Things(IoT)sensor devices should be able to send a large amount of data such as images and videos.However,this inevitably causes IoT networks to be severely overloaded.In this paper,therefore,we propose a novel oneM2M-compliant Artificial Intelligence of Things(AIoT)system for reducing overall data traffic and offering object detection.It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing(CS)rate,an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing,and a YOLOv5 deep learning function for object detection,and an IoT server.By analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device,we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor devices.In addition,we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall,precision,mAP50,and mAP,and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing rate.Consequently,if proper compressed sensing rates are chosen,the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.

Indoor Location Sensing Systems Based on Radio Channel Fingerprinting

This paper reviews and analyses various simplified-RFID (Radio Frequency Identification) indoor location systems, and proposes an improved implementation based on the propagation channel "fingerprinting" principle. The focus of the design aims to provide accurate location estimation, while minimising infrastructural requirements. The proposed approach is based on the LANDMARC (Indoor Location Sensing Using Active RFID) with Virtual Reference tags (VIRE) and implemented with dynamic linear interpolation and lagrange interpolation schemes. According to the simulation results, the proposed dynamic linear interpolation ensures much better performance on location-estimation error.

Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

Application research of narrow band Internet of things buoy and surface hydrodynamics monitoring

This paper applies the narrow band Internet of things communication technology to develop a wireless network equipment and communication system, which can quickly set up a network with a radius of 100 km on water surface. A disposable micro buoy based on narrow-band Internet of things and Beidou positioning function is also developed and used to collect surface hydrodynamic data online. In addition, a web-based public service platform is designed for the analysis and visualization of the data collected by buoys. Combined with the satellite remote sensing data, the study carries a series of marine experiments and studies such as sediment deposition tracking and garbage floating tracking.

Relay-Assisted Secure Short-Packet Transmission in Cognitive IoT with Spectrum Sensing

Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming(MRC/ZFB)scheme to guarantee the secrecy perfor-mance of dual-hop short-packet communications in cognitive IoT.This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ra-tio(SNR)regime and the infinite blocklength.In ad-dition,the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput.The nu-merical results verify the impact of the system pa-rameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a se-crecy constraint.It is shown that the proposed scheme achieves better secrecy performance than other bench-mark schemes.

Remote Sensing Image Encryption Using Optimal Key Generation-Based Chaotic Encryption

The Internet of Things(IoT)offers a new era of connectivity,which goes beyond laptops and smart connected devices for connected vehicles,smart homes,smart cities,and connected healthcare.The massive quantity of data gathered from numerous IoT devices poses security and privacy concerns for users.With the increasing use of multimedia in communications,the content security of remote-sensing images attracted much attention in academia and industry.Image encryption is important for securing remote sensing images in the IoT environment.Recently,researchers have introduced plenty of algorithms for encrypting images.This study introduces an Improved Sine Cosine Algorithm with Chaotic Encryption based Remote Sensing Image Encryption(ISCACE-RSI)technique in IoT Environment.The proposed model follows a three-stage process,namely pre-processing,encryption,and optimal key generation.The remote sensing images were preprocessed at the initial stage to enhance the image quality.Next,the ISCACERSI technique exploits the double-layer remote sensing image encryption(DLRSIE)algorithm for encrypting the images.The DLRSIE methodology incorporates the design of Chaotic Maps and deoxyribonucleic acid(DNA)Strand Displacement(DNASD)approach.The chaotic map is employed for generating pseudorandom sequences and implementing routine scrambling and diffusion processes on the plaintext images.Then,the study presents three DNASD-related encryption rules based on the variety of DNASD,and those rules are applied for encrypting the images at the DNA sequence level.For an optimal key generation of the DLRSIE technique,the ISCA is applied with an objective function of the maximization of peak signal to noise ratio(PSNR).To examine the performance of the ISCACE-RSI model,a detailed set of simulations were conducted.The comparative study reported the better performance of the ISCACE-RSI model over other existing approaches.

新型电力系统专用传感与边缘智能关键技术及其展望

电力物联网在电力系统的实时传感、透明感知与全局调控中发挥着重要作用。随着新型电力系统的不断演化,传统的设备传感与静态独立的智能系统难以应对多样化业务需求激增、多元化数据爆发式增长、传感器通信可靠性差与能效低下和算法模型迭代成本高等难题。因此,高度多元集成、绿色持续可靠、云边端深度协同的新型电力系统专用传感与边缘智能有望成为新范式。该文首先以新型电力系统专用传感与边缘智能的基本内涵为基础,构建了专用传感与边缘智能融合贯通的体系架构。然后重点阐述了国产化高频新型传感技术、绿色供能的高性能嵌入式芯片技术、高可靠性灵活组网技术、轻量化模型推理技术、云边协同资源共享技术和在线学习持续迭代技术。并进一步展望了专用传感与边缘智能在数据治理、隐私保护、软件定义硬件、业务-算力微服务、多层分布式泛在智能和电力大模型等发展方向,以期为新型电力系统建设提供参考。

服务天基感知业务的卫星互联网模拟验证系统设计与分析

提出具有遥感、星间星地组网、星载边缘计算功能的低轨卫星与地面云计算中心和天基、临近空间、空中、陆地、海面等各类型终端构成的卫星互联网总体架构,给出包括资源层、虚拟化层、服务层、服务管理层及应用层在内的星载边缘计算功能架构。针对星载计算能力有限、天基网络拓扑高动态等典型特征,在提出星载边缘计算模拟验证系统构建方法的基础上,设计服务天基感知业务的卫星互联网模拟验证系统,开展天基感知信息处理分发试验,并根据不同信息处理与分发方式对网络资源占用和用户服务时延的对比,分析不同能力条件下卫星互联网对天基感知业务服务的能力水平。

物联网专业混合式实验教学模式探索

针对当前物联网专业高校在实验与实践教学活动开展过程中面临的师资力量有限、实验内容设计质量不高、自主训练不足且缺乏思维锻炼能力等问题,以新工科人才建设思想为指导,以培养工程实践思维和创新能力提升为目标,基于SPOC、虚拟仿真等现代化教学手段和先进的教学理念,探索虚实结合、多径培养的新型教学模式。通过案例引导、虚实结合、课堂翻转等多样化的教学手段提高学生参与课程的积极性和学习热情,为国内其他高校新工科专业的实践教学建设与开展提供新的解决思路与教学方法。

地理信息呈现对媒介空间的两级建构——基于空间生产的理论视角

在当前的空间研究中,空间已不再是单纯的社会容器,而成为包含社会、地域、文化等在内的多元存在。互联网作为一种典型的空间类目,在技术革新的过程中不仅产生新的空间关系,还规训着人们的社会实践与媒介实践行为。文章从列斐伏尔的空间生产理论视角出发,探析地理信息对电子媒介空间的生产建构,发现IP属地位置的全量显示建构了基于话题讨论的一级媒介空间,同时还催生了基于地理主观感知的二级再现性空间。该空间不是现实地理的简单照搬,而是一种基于社会关系的复杂腾挪,具有勾连人、地理和社会的功能。此时地理位置不仅影响人们对于地方感的认知,而且介入电子空间的两级建构,重塑传播模式。

互联网的思想政治教育功能与社会功能对居民社会公平感的影响:有调节的中介效应模型

本文采用中国综合社会调查(CGSS2017)的数据,选取了18个题项作为研究变量进行研究,以社会信任作为中介变量,社会阶层为调节变量,构建互联网的思想政治教育功能与社会功能对居民社会公平感的影响关系模型。研究结果显示,一是互联网的思想政治教育功能与社会功能对居民社会公平感具有显著的正向影响。二是社会阶层在互联网的思想政治教育功能与社会功能对居民社会公平感的关系中起中介作用。三是社会阶层在社会信任与居民社会公平感的关系中起调节作用,社会阶层负向调节了互联网的思想政治教育功能与社会功能通过社会信任对居民社会公平感的间接效应。该研究不仅揭示了互联网的思想政治教育功能与社会功能对居民社会公平感的作用机制,还具有公共政策价值,即政府关于互联网政策的制定可在提升社会信任和社会阶层的方向拓展,回应居民对社会公平的关切,进而提升居民的社会公平感。

基于SIRM算法的卫星结构板智能化生产管控系统设计

针对物联网技术在智能制造系统中的应用,通过在卫星结构研制设计过程中应用物联网标识和感知技术实现智能化生产管控.分析卫星结构研制流程中实物、信息的操作现状,提出了一种SIMR(Snapshot Integration Multipath Routing)路由算法的卫星研制流程自主感知优化技术,保证流量传输均衡和信息快速回传,解决卫星研制实物流与信息流难以深度融合的问题.围绕卫星零件、装配体、电子标签等实物定义了智能化生产管控系统,将系统分解为实物操作序列与数据操作序列,进而给出了基于物联网的卫星结构智能化生产管控系统架构,明确了本方法在卫星结构板成形生产和装配系统管控环节的应用模式,为提升卫星结构实物状态管控与全流程研制能力提供了一种可行的方法.

一种小型化人工表面等离激元传感系统——实现自适应和高灵敏的气体检测

Resonantly enhanced dielectric sensing has superior sensitivity and accuracy because the signal is measured from relative resonance shifts that are immune to signal fluctuations.For applications in the Internet of Things(IoT),accurate detection of resonance frequency shifts using a compact circuit is in high demand.We proposed an ultracompact integrated sensing system that merges a spoof surface plasmon resonance sensor with signal detection,processing,and wireless communication.A softwaredefined scheme was developed to track the resonance shift,which minimized the hardware circuit and made the detection adaptive to the target resonance.A microwave spoof surface plasmon resonator was designed to enhance sensitivity and resonance intensity.The integrated sensing system was constructed on a printed circuit board with dimensions of 1.8 cm×1.2 cm and connected to a smartphone wirelessly through Bluetooth,working in both frequency scanning mode and resonance tracking mode and achieving a signal-to-noise ratio of 69 dB in acetone vapor sensing.This study provides an ultracompact,accurate,adaptive,sensitive,and wireless solution for resonant sensors in the IoT.

基于物联网技术的智能建筑管理系统研究

随着科技的不断进步和社会的发展,智能建筑管理系统在现代城市建设中扮演着越来越重要的角色,同时物联网技术的迅猛发展也为智能建筑管理系统的开发实现提供广阔的空间。文章从物联网技术与智能建筑管理系统简述入手,分析了物联网技术概述、智能建筑管理系统的定义和基本原理,进而探讨了物联网技术在智能建筑管理系统中的具体应用,并总结了其存在的问题及发展趋势。文章旨在为互联网及建筑领域的研究人员和实践者提供参考和启示,在未来的城市建设中发挥更加重要和广泛的作用。