Intelligent Dynamic Heterogeneous Redundancy Architecture for IoT Systems

The conventional dynamic heterogeneous redundancy(DHR)architecture suffers from the security threats caused by the stability differences and similar vulnerabilities among the executors.To overcome these challenges,we propose an intelligent DHR architecture,which is more feasible by intelligently combining the random distribution based dynamic scheduling algorithm(RD-DS)and information weight and heterogeneity based arbitrament(IWHA)algorithm.In the proposed architecture,the random distribution function and information weight are employed to achieve the optimal selection of executors in the process of RD-DS,which avoids the case that some executors fail to be selected due to their stability difference in the conventional DHR architecture.Then,through introducing the heterogeneity to restrict the information weights in the procedure of the IWHA,the proposed architecture solves the common mode escape issue caused by the existence of multiple identical error output results of similar vulnerabilities.The experimental results characterize that the proposed architecture outperforms in heterogeneity,scheduling times,security,and stability over the conventional DHR architecture under the same conditions.

CL2ES-KDBC:A Novel Covariance Embedded Selection Based on Kernel Distributed Bayes Classifier for Detection of Cyber-Attacks in IoT Systems

The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.

Approach to Simplify the Development of IoT Systems that Interconnect Embedded Devices Using a Single Program

Many Internet of Things(IoT)systems are based on the intercommunication among different devices and centralized systems.Nowadays,there are several commercial and research platforms available to simplify the creation of such IoT systems.However,developing these systems can often be a tedious task.To address this challenge,a proposed solution involves the implementation of a unified program or script that encompasses the entire system,including IoT devices functionality.This approach is based on an abstraction,integrating the control of the devices in a single program through a programmable object.Subsequently,the proposal processes the unified script to generate the centralized system code and a controller for each device.By adopting this approach,developers will be able to create IoT systems with significantly reduced implementation costs,surpassing current platforms by more than 10%.The results demonstrate that the single program approach can significantly accelerate the development of IoT systems relying on device communication.

An End-to-End Authentication Scheme for Healthcare IoT Systems Using WMSN

The healthcare internet of things(IoT)system has dramatically reshaped this important industry sector.This system employs the latest technology of IoT and wireless medical sensor networks to support the reliable connection of patients and healthcare providers.The goal is the remote monitoring of a patient’s physiological data by physicians.Moreover,this system can reduce the number and expenses of healthcare centers,make up for the shortage of healthcare centers in remote areas,enable consultation with expert physicians around the world,and increase the health awareness of communities.The major challenges that affect the rapid deployment and widespread acceptance of such a system are the weaknesses in the authentication process,which should maintain the privacy of patients,and the integrity of remote medical instructions.Current research results indicate the need of a flexible authentication scheme.This study proposes a scheme with enhanced security for healthcare IoT systems,called an end-to-end authentication scheme for healthcare IoT systems,that is,an E2EA.The proposed scheme supports security services such as a strong and flexible authentication process,simultaneous anonymity of the patient and physician,and perfect forward secrecy services.A security analysis based on formal and informal methods demonstrates that the proposed scheme can resist numerous security-related attacks.A comparison with related authentication schemes shows that the proposed scheme is efficient in terms of communication,computation,and storage,and therefore cannot only offer attractive security services but can reasonably be applied to healthcare IoT systems.

An Adaptive Vision Navigation Algorithm in Agricultural IoT System for Smart Agricultural Robots

As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.

HCloud:A Trusted JointCloud Serverless Platform for IoT Systems with Blockchain

Cloud computing has been exploited in managing large-scale IoT systems.IoT cloud servers usually handle a large number of requests from various IoT devices.Due to the fluctuant and heavy workload,the servers require the cloud to provide high scalability,stable performance,low price and necessary functionalities.However,traditional clouds usually offer computing service with the abstraction of virtual machine(VM),which can hardly meet these requirements.Meanwhile,different cloud vendors provide different performance stabilities and price models,which fluctuate according to the dynamic workload.A single cloud cannot satisfy all the requirements of the IoT scenario well.The JointCloud computing model empowers the cooperation among multiple public clouds.However,it is still difficult to dynamically schedule the workload on different clouds based on the VM abstraction.This paper introduces HCloud,a trusted JointCloud platform for IoT systems using serverless computing model.HCloud allows an IoT server to be implemented with multiple serverless functions and schedules these functions on different clouds based on a schedule policy.The policy is specified by the client and includes the required functionalities,execution resources,latency,price and so on.HCloud collects the status of each cloud and dispatches serverless functions to the most suitable cloud based on the schedule policy.By leveraging the blockchain technology,we further enforce that our system can neither fake the cloud status nor wrongly dispatch the target functions.We have implemented a prototype of HCloud and evaluated it by simulating multiple cloud providers.The evaluation results show that HCloud can greatly improve the performance of serverless workloads with negligible costs.

Design and Simulation of IoT Systems Using the Cisco Packet Tracer

Design and implementation of Internet of Things (IoT) systems require platforms with smart things and components. Two dominant architectural approaches for developing IoT systems are mashup-based and model-based approaches. Mashup approaches use existing services and are mainly suitable for less critical, personalized applications. Web development tools are widely used in mashup approaches. Model-based techniques describe a system on a higher level of abstraction, resulting in very expressive modelling of systems. The article uses Cisco packet tracer 7.2 version, which consists of four subcategories of smart things—home, smart city, industrial and power grid, to design an IoT based control system for a fertilizer manufacturing plant. The packet tracer also consists of boards—microcontrollers (MCU-PT), and single boarded computers (SBC-PT), as well as actuators and sensors. The model facilitates flexible communication opportunities among things—machines, databases, and Human Machine Interfaces (HMIs). Implementation of the IoT system brings finer process control as the operating conditions are monitored online and are broadcasted to all stakeholders in real-time for quicker action on deviations. The model developed focuses on three process plants;steam raising, nitric acid, and ammonium nitrate plants. Key process parameters are saturated steam temperature, fuel flowrates, CO and SOx emissions, converter head temperature, NOx emissions, neutralisation temperature, solution temperature, and evaporator steam pressure. The parameters need to be monitored in order to ensure quality, safety, and efficiency. Through the Cisco packet tracer platform, a use case, physical layout, network layout, IoT layout, configuration, and simulation interface were developed.

Quranic Script Optical Text Recognition Using Deep Learning in IoT Systems

Since the worldwide spread of internet-connected devices and rapid advances made in Internet of Things(IoT)systems,much research has been done in using machine learning methods to recognize IoT sensors data.This is particularly the case for optical character recognition of handwritten scripts.Recognizing text in images has several useful applications,including content-based image retrieval,searching and document archiving.The Arabic language is one of the mostly used tongues in the world.However,Arabic text recognition in imagery is still very much in the nascent stage,especially handwritten text.This is mainly due to the language complexities,different writing styles,variations in the shape of characters,diacritics,and connected nature of Arabic text.In this paper,two deep learning models were proposed.The first model was based on a sequence-to-sequence recognition,while the second model was based on a fully convolution network.To measure the performance of these models,a new dataset,called QTID(Quran Text Image Dataset)was devised.This is the first Arabic dataset that includes Arabic diacritics.It consists of 309,720 different 192×64 annotated Arabic word images,which comprise 2,494,428 characters in total taken from the Holy Quran.The annotated images in the dataset were randomly divided into 90%,5%,and 5%sets for training,validation,and testing purposes,respectively.Both models were set up to recognize the Arabic Othmani font in the QTID.Experimental results show that the proposed methods achieve state-of-the-art outcomes.Furthermore,the proposed models surpass expectations in terms of character recognition rate,F1-score,average precision,and recall values.They are superior to the best Arabic text recognition engines like Tesseract and ABBYY FineReader.

Chaos-Based Cryptographic Mechanism for Smart Healthcare IoT Systems

Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things(IoT)in healthcare(HIoT).Due to more and more online security and data hijacking attacks,the confidentiality,integrity and availability of data are considered serious issues in HIoT applications.In this regard,lightweight block ciphers(LBCs)are promising in resourceconstrained environment where security is the primary consideration.The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance,cost,and security.Most of the existing LBCs primarily focus on text data or grayscale images.The main focus of this paper is about securing color images in a cost-effective way.We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones,and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems.In order to reduce computational complexity and simulation time,the proposed Lightweight Symmetric Block Cipher(LSBC)exploits chaos-based confusion-diffusion operations at the inter-block level using a single round.The strength of LSBC is assessed by cryptanalysis,while it is ranked by comparing it to other privacy-preserving schemes.Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks,which proves that our LSBC is a good candidate for image security in HIoT.

Real Time Vehicle Status Monitoring under Moving Conditions Using a Low Power IoT System

In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Encrypted Cyberattack Detection System over Encrypted IoT Traffic Based onStatistical Intelligence

In the early days of IoT’s introduction, it was challenging to introduce encryption communication due to the lackof performance of each component, such as computing resources like CPUs and batteries, to encrypt and decryptdata. Because IoT is applied and utilized in many important fields, a cyberattack on IoT can result in astronomicalfinancial and human casualties. For this reason, the application of encrypted communication to IoT has beenrequired, and the application of encrypted communication to IoT has become possible due to improvements inthe computing performance of IoT devices and the development of lightweight cryptography. The applicationof encrypted communication in IoT has made it possible to use encrypted communication channels to launchcyberattacks. The approach of extracting evidence of an attack based on the primary information of a networkpacket is no longer valid because critical information, such as the payload in a network packet, is encrypted byencrypted communication. For this reason, technology that can detect cyberattacks over encrypted network trafficoccurring in IoT environments is required. Therefore, this research proposes an encrypted cyberattack detectionsystem for the IoT (ECDS-IoT) that derives valid features for cyberattack detection from the cryptographic networktraffic generated in the IoT environment and performs cyberattack detection based on the derived features. ECDSIoT identifies identifiable information from encrypted traffic collected in IoT environments and extracts statisticsbased features through statistical analysis of identifiable information. ECDS-IoT understands information aboutnormal data by learning only statistical features extracted from normal data. ECDS-IoT detects cyberattacks basedonly on the normal data information it has trained. To evaluate the cyberattack detection performance of theproposed ECDS-IoT in this research, ECDS-IoT used CICIoT2023, a dataset containing encrypted traffic generatedby normal and seven categories of cyberattacks in the IoT environment and experimented with cyberattackdetection on encrypted traffic using Autoencoder, RNN, GRU, LSTM, BiLSTM, and AE-LSTM algorithms. Asa result of evaluating the performance of cyberattack detection for encrypted traffic, ECDS-IoT achieved highperformance such as accuracy 0.99739, precision 0.99154, recall 1.0, F1 score 0.99575, and ROC_AUC 0.99822when using the AE-LSTM algorithm. As shown by the cyberattack detection results of ECDS-IoT, it is possibleto detect most cyberattacks through encrypted traffic. By applying ECDS-IoT to IoT, it can effectively detectcyberattacks concealed in encrypted traffic, promoting the efficient operation of IoT and preventing financial andhuman damage caused by cyberattacks.

Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next‑Generation Self‑Powered Remote IoT Fire Warning

Fire warning is vital to human life,economy and ecology.However,the development of effective warning systems faces great challenges of fast response,adjustable threshold and remote detecting.Here,we propose an intelligent self-powered remote IoT fire warning system,by employing single-walled carbon nanotube/titanium carbide thermoelectric composite films.The flexible films,prepared by a convenient solution mixing,display p-type characteristic with excellent high-temperature stability,flame retardancy and TE(power factor of 239.7±15.8μW m^(-1) K^(-2))performances.The comprehensive morphology and structural analyses shed light on the underlying mechanisms.And the assembled TE devices(TEDs)exhibit fast fire warning with adjustable warning threshold voltages(1–10 mV).Excitingly,an ultrafast fire warning response time of~0.1 s at 1 mV threshold voltage is achieved,rivaling many state-of-the-art systems.Furthermore,TE fire warning systems reveal outstanding stability after 50 repeated cycles and desired durability even undergoing 180 days of air exposure.Finally,a TED-based wireless intelligent fire warning system has been developed by coupling an amplifier,analogto-digital converter and Bluetooth module.By combining TE characteristics,high-temperature stability and flame retardancy with wireless IoT signal transmission,TE-based hybrid system developed here is promising for next-generation self-powered remote IoT fire warning applications.

IoT-Enabled Plant Monitoring System with Power Optimization and Secure Authentication

Global food security is a pressing issue that affects the stability and well-being of communities worldwide.While existing Internet of Things(IoT)enabled plant monitoring systems have made significant strides in agricultural monitoring,they often face limitations such as high power consumption,restricted mobility,complex deployment requirements,and inadequate security measures for data access.This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings.Our system strategically combines power efficiency,portability,and secure access capabilities,assisting farmers in monitoring and tracking crop environmental conditions.The proposed system includes a remote camera that captures images of surrounding plants and a sensor module that regularly monitors various environmental factors,including temperature,humidity,and soil moisture.We implement power management strategies to minimize energy consumption compared to existing solutions.Unlike conventional systems,our implementation utilizes the Amazon Web Services(AWS)cloud platform for reliable data storage and processing while incorporating comprehensive security measures,including Two-Factor Authentication(2FA)and JSON Web Tokens(JWT),features often overlooked in current agricultural IoT solutions.Users can access this secure monitoring system via a developed Android application,providing convenient mobile access to the gathered plant data.We validate our system’s advantages by implementing it with two potted garlic plants on Okayama University’s rooftop.Our evaluation demonstrates high sensor reliabil-ity,with strong correlations between sensor readings and reference data,achieving determination coefficients(R2)of 0.979 for temperature and 0.750 for humidity measurements.The implemented power management strategies extend battery life to 10 days on a single charge,significantly outperforming existing systems that typically require daily recharging.Furthermore,our dual-layer security implementation utilizing 2FA and JWT successfully protects sensitive agricultural data from unauthorized access.

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.

GRU Enabled Intrusion Detection System for IoT Environment with Swarm Optimization and Gaussian Random Forest Classification

In recent years,machine learning(ML)and deep learning(DL)have significantly advanced intrusion detection systems,effectively addressing potential malicious attacks across networks.This paper introduces a robust method for detecting and categorizing attacks within the Internet of Things(IoT)environment,leveraging the NSL-KDD dataset.To achieve high accuracy,the authors used the feature extraction technique in combination with an autoencoder,integrated with a gated recurrent unit(GRU).Therefore,the accurate features are selected by using the cuckoo search algorithm integrated particle swarm optimization(PSO),and PSO has been employed for training the features.The final classification of features has been carried out by using the proposed RF-GNB random forest with the Gaussian Naïve Bayes classifier.The proposed model has been evaluated and its performance is verified with some of the standard metrics such as precision,accuracy rate,recall F1-score,etc.,and has been compared with different existing models.The generated results that detected approximately 99.87%of intrusions within the IoT environments,demonstrated the high performance of the proposed method.These results affirmed the efficacy of the proposed method in increasing the accuracy of intrusion detection within IoT network systems.

Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber threats.Among the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of traffic.As IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT serves.In this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS attacks.The CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)methods.The proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in real-time.This involvesemploying the best-performing ML algorithmfor detection and the integration of warning mechanisms.We believe this approach will significantly enhance the field of security research and continue to refine it based on future insights and developments.

A Privacy Preserving Federated Learning System for IoT Devices Using Blockchain and Optimization

In this study, a blockchain based federated learning system using an enhanced weighted mean vector optimization algorithm, known as EINFO, is proposed. The proposed EINFO addresses the limitations of federated averaging during global update and model training, where data is unevenly distributed among devices and there are variations in the number of data samples. Using a well-defined structure and updating the vector positions by local searching, vector combining, and updating rules, the EINFO algorithm maximizes the shared model parameters. In order to increase the exploration and exploitation capabilities, the model convergence rate is improved and new vectors are generated through the use of a weighted mean vector based on the inverse square law. To choose validators, miners, and to propagate new blocks, a delegated proof of stake based on the reliability of blockchain nodes is suggested. Federated learning is included into the blockchain to protect nodes from both external and internal threats. To determine how well the suggested system performs in relation to current models in the literature, extensive simulations are run. The simulation results show that the proposed system outperforms existing schemes in terms of accuracy, sensitivity and specificity.

Design of IoT-based Volatile Organic Compounds Monitoring System

Volatile organic compounds(VOC)gas detection devices based on semiconductor sensors have become a common method due to their low cost,simple principle,and small size.However,with the continuous development of materials science,various new materials have been applied in the fabrication of gas sensors,but these new materials have more stringent requirements for operating temperature,which cannot be met by existing sensor modules on the market.Therefore,this paper proposes a temperature-adjustable sensor module and designs an environmental monitoring system based on the STM32F103RET6 microprocessor.This system primarily utilizes multiple semiconductor gas sensors to monitor and record the concentrations of various harmful gases in different environments.It can also monitor real-time temperature,humidity,and latitude and longitude in the current environment,and upload the data to the Internet of Things via 4G communication.This system has the advantages of small size,portability,and low cost.Experimental results show that the sensor module can achieve precise control of operating temperature to a certain extent,with an average temperature error of approximately 3%.The monitoring system demonstrates a certain level of accuracy in detecting target gases and can promptly upload the data to a cloud platform for storage and processing.A comparison with professional testing equipment shows that the sensitivity curves of each sensor exhibit similarity.This study provides engineering and technical references for the application of VOC gas sensors.

面向NVM的IoT时序数据多态协作压缩策略

压缩策略是影响IoT时序数据存储系统性能的重要因素,而现有压缩策略缺乏针对NVM与IoT时序数据特性的优化机制。因此,提出了面向NVM的IoT时序数据多态协作压缩策略。首先,给出了IoT时序数据的组织结构。然后,针对IoT时序数据在一段时间内较稳定以及在用户态与内核态读写NVM适合的粒度差异较大的情况,设计了分层压缩策略。在用户态接收数据时,采用轻量级的数据压缩算法减少需存储的数据量,也减小了对IoT时序数据的存储效率的影响;针对IoT系统以查询和分析异常时序数据为主的特性,设计了深度压缩算法,在内核态对历史IoT时序数据进行深度压缩。其次,针对深度压缩历史IoT时序数据与存储新接收的IoT时序数据之间对NVM带宽的竞争,提出了写带宽保证的动态调整算法。最后,构建了面向NVM的IoT时序数据多态协作压缩策略原型PCCTSMS,并使用YCSB-TS工具进行测试与分析。实验结果表明,与InfluxDB、OpenTSDB、KairosDB和TVStore相比,PCCTSMS最高能提升161.3%的写吞吐率以及减少14.6%的存储空间。