Free-walking:Pedestrian inertial navigation based on dual foot-mounted IMU

The inertial navigation system(INS),which is frequently used in emergency rescue operations and other situations,has the benefits of not relying on infrastructure,high positioning frequency,and strong real-time performance.However,the intricate and unpredictable pedestrian motion patterns lead the INS localization error to significantly diverge with time.This paper aims to enhance the accuracy of zero-velocity interval(ZVI)detection and reduce the heading and altitude drift of foot-mounted INS via deep learning and equation constraint of dual feet.Aiming at the observational noise problem of low-cost inertial sensors,we utilize a denoising autoencoder to automatically eliminate the inherent noise.Aiming at the problem that inaccurate detection of the ZVI detection results in obvious displacement error,we propose a sample-level ZVI detection algorithm based on the U-Net neural network,which effectively solves the problem of mislabeling caused by sliding windows.Aiming at the problem that Zero-Velocity Update(ZUPT)cannot suppress heading and altitude error,we propose a bipedal INS method based on the equation constraint and ellipsoid constraint,which uses foot-to-foot distance as a new observation to correct heading and altitude error.We conduct extensive and well-designed experiments to evaluate the performance of the proposed method.The experimental results indicate that the position error of our proposed method did not exceed 0.83% of the total traveled distance.

Mechanical behavior of iron ore tailings under standard compression and extension triaxial stress paths

The disposal of mining tailings has increasingly focused on the use of dry stacks.These structures offer more security since they use filtered and compacted material.Because of the construction method and the heights achieved,the material that compounds the structure can be subjected to different stress paths along the failure plane.The theoretical framework considered in the design of these structures generally is the critical state soil mechanics(CSSM).However,the data in the literature concerning the uniqueness of critical state line(CSL)is still controversial as the soil is subjected to different stress paths.With respect to tailings,this question is even more restricted.This paper studies two tailings with different gradings due to the beneficial processes over extension and compression paths.A series of drained and undrained triaxial tests was conducted over a range of initial densities and stress levels.In the q-p'plane,different critical stress ratio(M)values were obtained for compression and extension stress paths.However,the critical state friction angle is very similar with a slightly higher critical state friction angle for extension tests.Curved stress path dependent CSLs were obtained in the n-lnp0 plane with the extension tests below the CSL defined in compression.Regarding the fines content,the studied tailings presented very similar M and critical state friction angle values.However,the fines content af-fects the volumetric behavior of the studied tailings and the CSLs on the n-lnp0 plane shift downwards with the increasing fines content for compression and extension tests.In relation to dilatancy analysis,the fines content did not present an evident influence on the dilatancy of the materials.However,different values of mean stress ratio N were obtained between compression and extension tests and can corroborate the existence of non-unique CSLs for these materials.

网络协议测试的符号化一致性关系研究

协议的一致性测试可以验证协议实现的正确性,一致性关系是测试生成的基础。网络协议的特点之一是控制消息中会携带大量数据。针对网络协议的该特点提出了符号化的一致性关系模型。建立了网络协议的输入输出符号变迁系统,并且依据数据符号实例化策略建立了对应的语义模型,在该模型中系统的活动是集成控制和数据的复杂活动。在此输入输出符号变迁模型的基础上提出了一种符号化的一致性关系以生成测试套。为了便于测试套的自动生成,对所提出的一致性关系进行了简化。在上述过程中数据的处理通过符号化的变量进行建模,这样可以使用统一符号进行数据处理,而不必总是关心具体的数值,只需在适当的时机进行符号的实例化。文中所提出的一致性关系模型充分利用了符号化的变量,增加了该模型的抽象程度,避免了处理过程的复杂性,且可以指导测试套的自动生成。最后使用IPv6的邻居发现协议对该一致性关系进行了说明。

美军后勤物联网系统建设的做法及启示

物联网在军事物流中的应用就是军事物流物联网。美军联合全资产可视化系统就是一个物流物联网系统。美军物流物联网建设可以划分为"应用引入、系统建设和集成优化"三个阶段,在建设过程中遵循了"需求导向、应用牵引"的原则、"分步实施、重点突破"的原则、"统一组织、融合集成"的原则。我军物流物联网建设的重点一是要注重顶层规划,加强统一领导;二是要夯实基础性工作;三是进行客观细致的需求分析。四是进行科学高效的管理。

一种基于移动家庭医生平台的医患共享式电子血压计的准确性评估

目的:评估一款基于物联网移动家庭医生平台的医患共享式电子血压计的准确性。方法:采用Excel随机数表法抽取100例住院患者,分别应用医患共享式电子血压计与台式水银血压计检测其入院时血压,并进行对照比较,采用英国高血压协会(BHS)标准对比分析两种血压计测量结果的差异性。结果:两种血压计所测得的收缩压和舒张压的差异均无统计学意义;经BHS标准分析,两种血压计测得的收缩压和舒张压的差值在≤5 mmHg、≤10 mmHg、≤15 mmHg范围内的例数占总例数的百分比分别为70%、91%、98%和76%、94%、100%,评分等级为A,均达到BHS标准规定的医用电子血压计的等级要求,其中有2次测量的收缩压差值>15 mmHg。结论:基于物联网的医患共享式电子血压计的准确率达到了相关国际论证的精度标准,可用于普通人或患者的远程家庭医生平台模式的血压测量。

基于无线信道指纹的室内位置感应系统分析(英文)

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.

物联网环境中基于车联网与3G网络的跨层安全相关广播业务结构(英文)

As Vehicle Ad Hoc Networks (VANETs) is part of the applications of the Internet of Things (IoT), and Vehicles in VANETs periodically broadcast the beacon message for status advertisement to provide public safety, the impacts of the network parameters on the reliability of broadcast messages are investigated and discussed; meanwhile, a cross-layer safety-critical broadcast service architecture is proposed to obtain an optimized set of packet loss rate and delay based on the Neural Networks (NN) and Back Propagation (BP) algorithm to dynamically adjust the transmission rate-power pairs. Simulation results illustrate that the proposed mechanism can effectively improve the reliability performance while maintaining the fairness among vehicles.

Practical Timing Synchronization for Network Dynamics in Large Machine-to-Machine Networks

Efficient multi-machine cooperation and network dynamics still remain open that jeopardize great applications in largescale machine-to-machine(M2M) networks. Among all possible machine cooperation controls, to synchronize tremendous machines in a timing-efficient brings one of the greatest challenge and serves as the foundation for any other network control policies. In this paper, we propose a linear-time synchronization protocol in large M2M networks. Specifically, a closed-form of synchronization rate is provided by developing the statistical bounds of the second smallest eigenvalue of the graph Laplacian matrix. These bounds enable the efficient control of network dynamics, facilitating the timing synchronization in networks. Through a practical study in Metropolis, simulation results confirm our theoretical analysis and provide effective selection of wireless technologies, including Zigbee, Wi-Fi, and cellular systems, with respect to the deployed density of machines. Therefore, this paper successfully demonstrates a practical timing synchronization, to make a breakthrough of network dynamic control in real-world machine systems, such as Internet of Things.

A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.

A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity threats.In this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic cryptosystem.During the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping attacks.The correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically guaranteed.The simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

Federated Learning Security and Privacy-Preserving Algorithm and Experiments Research Under Internet of Things Critical Infrastructure

The widespread use of the Internet of Things(IoTs)and the rapid development of artificial intelligence technologies have enabled applications to cross commercial and industrial band settings.Within such systems,all participants related to commercial and industrial systems must communicate and generate data.However,due to the small storage capacities of IoT devices,they are required to store and transfer the generated data to third-party entity called“cloud”,which creates one single point to store their data.However,as the number of participants increases,the size of generated data also increases.Therefore,such a centralized mechanism for data collection and exchange between participants is likely to face numerous challenges in terms of security,privacy,and performance.To address these challenges,Federated Learning(FL)has been proposed as a reasonable decentralizing approach,in which clients no longer need to transfer and store real data in the central server.Instead,they only share updated training models that are trained over their private datasets.At the same time,FL enables clients in distributed systems to share their machine learning models collaboratively without their training data,thus reducing data privacy and security challeges.However,slow model training and the execution of additional unnecessary communication rounds may hinder FL applications from operating properly in a distributed system.Furthermore,these unnecessary communication rounds make the system vulnerable to security and privacy issues,because irrelevant model updates are sent between clients and servers.Thus,in this work,we propose an algorithm for fully homomorphic encryption called Cheon-Kim-Kim-Song(CKKS)to encrypt model parameters for their local information privacy-preserving function.The proposed solution uses the impetus term to speed up model convergence during the model training process.Furthermore,it establishes a secure communication channel between IoT devices and the server.We also use a lightweight secure transport protocol to mitigate the communication overhead,thereby improving communication security and efficiency with low communication latency between client and server.

Dataflow Management in the Internet of Things: Sensing,Control, and Security

The pervasiveness of the smart Internet of Things(IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection,etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition,promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated,which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

Memristor-Based Signal Processing for Edge Computing

The rapid growth of the Internet of Things(IoTs)has resulted in an explosive increase in data,and thus has raised new challenges for data processing units.Edge computing,which settles signal processing and computing tasks at the edge of networks rather than uploading data to the cloud,can reduce the amount of data for transmission and is a promising solution to address the challenges.One of the potential candidates for edge computing is a memristor,an emerging nonvolatile memory device that has the capability of in-memory computing.In this article,from the perspective of edge computing,we review recent progress on memristor-based signal processing methods,especially on the aspects of signal preprocessing and feature extraction.Then,we describe memristor-based signal classification and regression,and end-to-end signal processing.In all these applications,memristors serve as critical accelerators to greatly improve the overall system performance,such as power efficiency and processing speed.Finally,we discuss existing challenges and future outlooks for memristor-based signal processing systems.

Intelligent Monitoring System for Biogas Detection Based on the Internet of Things: Mohammedia, Morocco City Landfill Case

Mechanization is a depollution activity,because it provides an energetic and ecological response to the problem of organic waste treatment.Through burning,biogas from mechanization reduces gas pollution from fermentation by a factor of 20.This study aims to better understand the influence of the seasons on the emitted biogas in the landfill of the city Mohammedia.The composition of the biogas that naturally emanates from the landfill has been continuously analyzed by our intelligent system,from different wells drilled in recent and old waste repositories.During the rainy season,the average production of methane,carbon dioxide,and oxygen and nitrogen are currently 56%,32%,and 1%,respectively,compared to 51%,31%,and 0.8%,respectively,for old waste.Hazards levels,potential fire,and explosion risks associated with biogas are lower than those of natural gases in most cases.For this reason a system is proposed to measure and monitor the biogas production of the landfill site remotely.Measurement results carried out at various sites of the landfill in the city of Mohammedia by the system show that the biogas contents present dangers and sanitary risks which are of another order.

Convergence of computing,communication,and caching in internet of things

Internet of Things(IoTs)is a big world of connected objects,including the small and low-resources devices,like sensors,as well as the full-functional computing devices,such as servers and routers in the core network.With the emerging of new IoT-based applications,such as smart transportation,smart agriculture,healthcare,and others,there is a need for making great efforts to achieve a balance in using the IoT resources,including Computing,Communication,and Caching.This paper provides an overview of the convergence of Computing,Communication,and Caching(CCC)by covering the IoT technology trends.At first,we give a snapshot of technology trends in communication,computing,and caching.As well,we describe the convergence in sensors,devices,and gateways.Addressing the aspect of convergence,we discuss the relationship between CCC technologies in collecting,indexing,processing,and storing data in IoT.Also,we introduce the three dimensions of the IoTs based on CCC.We explore different existing technologies that help to solve bottlenecks caused by a large number of physical devices in IoT.Finally,we propose future research directions and open problems in the convergence of communication,computing,and cashing with sensing and actuating devices.