With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Re...Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established.展开更多
As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an ...As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.展开更多
Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions ...Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions entered.This growth in size is the main obstacle to the blockchain,which makes it unsuitable for resource-constrained IoT environments.Moreover,conventional consensus algorithms such as PoW,PoS are very computationally heavy.This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm.The Multi-Zone Direct Acyclic Graph(DAG)Blockchain(Multizone-DAG-Blockchain)framework is proposed for the fog-based IoT environment.In this context,fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes,thus preserving the energy consumption of the IoT devices.Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validationmechanism(DPUF-VM)inwhichmultiple authentication certificates are verified in the blockchain.Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm.In the cloud,sensitive data is stored as ciphertext.Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes.The fog node first performs a redundancy analysis using the Jaccard Similarity(JS)measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network(N2IN)algorithm.A lightweight proof-of-authentication(PoAh)algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization(BoSo)algorithm for transaction validation.The proposed work is modeled in Network Simulator 3.26(ns-3.26),and the performance is evaluated in terms of energy consumption,storage cost,response time,and throughput.展开更多
为更加全面有效分析影响铁路设备全生命周期的环境因素,完善铁路室外环境监测,针对铁路环境监测的局限性,可采用以太网供电(Power over Ethernet,POE)和窄带物联网(NarrowBand Internet of Things,NB-IoT)的方式部署室内外环境监测系统...为更加全面有效分析影响铁路设备全生命周期的环境因素,完善铁路室外环境监测,针对铁路环境监测的局限性,可采用以太网供电(Power over Ethernet,POE)和窄带物联网(NarrowBand Internet of Things,NB-IoT)的方式部署室内外环境监测系统。POE方式室内环境监测新增粉尘、气压、噪音、二氧化碳参数监测,系统部署简单高效,维护成本、系统故障率低,有利于后期设备的平滑升级。N B-IoT方式室外环境监测创新性地引入路基填土温湿度、导电性及钢轨温度、震动和位移参数监测,室内外环境监测数据按室内3D图加室外设备图形化的形式展示。该系统集成度高、价格低廉、功耗低、覆盖全,适合铁路多站点、环境复杂场景。展开更多
城市地下管廊内布设了大量的管线,如燃气管道、网络通讯线路、电力线路等,由于地下环境复杂多变,存在着气体泄漏、爆炸、火灾等安全风险。针对这些问题,提出一种基于窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的地下管廊...城市地下管廊内布设了大量的管线,如燃气管道、网络通讯线路、电力线路等,由于地下环境复杂多变,存在着气体泄漏、爆炸、火灾等安全风险。针对这些问题,提出一种基于窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的地下管廊环境监测系统。该系统采用先进的传感器技术、NB-IoT技术、软件技术,系统主要分为数据采集模块、物联网云平台、远程监测系统三部分。数据采集模块以STM32作为主控单元连接各个传感器,采集温度、湿度、水位、可燃气体等数据,经过处理后利用NB-IoT网络上传到物联网云平台,远程监测系统调用物联网云平台的数据接口进行远程显示与预警。实验结果表明,系统在降低系统总体功耗的同时,能够实时、稳定地进行地下管廊环境监测,提前预防可能存在的风险。展开更多
农田环境信息是制定农田管理策略的重要依据,为了实时稳定地采集农田环境信息,结合窄带物联网(Narrow Band Internet of Things,NB-IoT)的优势,设计并开发了基于NB-IoT的农田环境信息远程监测系统。该系统利用STM32F103RCT6单片机和传...农田环境信息是制定农田管理策略的重要依据,为了实时稳定地采集农田环境信息,结合窄带物联网(Narrow Band Internet of Things,NB-IoT)的优势,设计并开发了基于NB-IoT的农田环境信息远程监测系统。该系统利用STM32F103RCT6单片机和传感器终端实时采集温度、湿度、光照强度、二氧化碳浓度、土壤湿度等农田环境数据,并通过NB-IoT网络将采集的数据传输至基于OneNET平台的农田环境监测云平台,用户可通过农田环境监测App或PC端访问农田环境监测云平台以获取农田环境监测数据。系统测试结果表明,该系统可实时获取温度、湿度、光照度、二氧化碳浓度、土壤湿度等农田环境信息,温度控制精度保持在±0.2℃,相对误差为0.57%;湿度控制精度保持在±2%RH,相对误差为1.66%;光照度控制精度保持在±63 lx,相对误差为0.24%;二氧化碳浓度控制精度保持在±45.46μmol/L,相对误差为0.34%;土壤湿度控制精度保持在±2%,相对误差为1.44%。该系统运行稳定,数据传输实时、准确,功能实用,操作简单,可大规模部署,为农业监控和物联网应用研究提供有效参考。展开更多
随着科技的发展,环境保护问题越来越受到国家和社会大众的重视,同时越来越多的最新科技发展成果应用到环保领域中。本文结合窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的优势和环境参数监测系统的具体需求,采用STM32L系列...随着科技的发展,环境保护问题越来越受到国家和社会大众的重视,同时越来越多的最新科技发展成果应用到环保领域中。本文结合窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的优势和环境参数监测系统的具体需求,采用STM32L系列单片机、环境监测传感器及BC20通信模组以及阿里云物联网平台设计实现了一套基于NB-IoT的环境参数监测系统。通过实验验证,该系统实现效果良好,可推广应用于环保监测、智慧农业等领域。展开更多
The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the...The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.展开更多
为满足兔舍环境监测调控需求,同时摆脱传统布线网络局限性,缩减网络资费、电路元件和控制系统成本,本研究提出一种基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的兔舍环境实时监测系统。系统基于Arduino开发板,使用移远BC260...为满足兔舍环境监测调控需求,同时摆脱传统布线网络局限性,缩减网络资费、电路元件和控制系统成本,本研究提出一种基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的兔舍环境实时监测系统。系统基于Arduino开发板,使用移远BC260Y模块与消息队列遥测传输协议(Message Queuing Telemetry Transport,MQTT)实现网络连接,利用SGP30、MQ137、5516光敏电阻传感器等多种传感器实现兔舍内部声、光、水、温、气五方面实时监测。数据在本地、云端存储的同时,系统可根据阈值报警,协助创造兔的最佳生存环境。研究中对比了NB-IoT网络与Wi-Fi、LoRa等其他网络的异同,根据物联网三层架构详细介绍了系统搭建技术与过程,并系统分析了元器件价格,经核算,整机成本不超过400元。设备在空舍测试中,检测到CO_(2)浓度为420~440 ppm;MQ系列传感模组电压比值稳定于1;温度处于22~24℃;湿度上下波动10%;日光灯亮灭引起电压差2.6 V。进行了系统的网络与能耗测试,通过不同时间、场地、网络连接方式的对比,验证了本系统传输稳定可靠,能耗合理。系统使用MQTT通信协议的NB-IoT网络,平均每秒消息处理量(Transactions Per Second,TPS)为0.57,每分钟收发34.2条,上下浮动1条。系统运行时,电压约为12.5 V,电流约为0.42 A,平均功率为5.3 W。发生通信时,没有产生额外功耗,适用于实际养殖生产。本研究可为偏远或较大规模的养殖监测设备选取提供设备成本与网络选择参考价值。展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金This research received the support from the Deanship of Scientific Research at King Khalid University for funding this work through Research Group Program under Grant Number RGP.1/58/42.
文摘Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern era.IoT generally refers to a network of gadgets linked via wireless network and communicates via internet.Resource management,especially energy management,is a critical issue when designing IoT devices.Several studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT environment.In this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT environment.The proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance phase.The proposed EECBRMmodel was validated through a series of experiments and the results were verified under several aspects.EECBRM model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network lifetime.When simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant manner.Thus,the efficiency of the proposed model is established.
基金funding from Deanship of Scientific Research in King Faisal University with Grant Number KFU241648.
文摘As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense mechanisms.This paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world settings.Our proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive framework.This integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT systems.We evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated attacks.Our research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive quality.Our model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security paradigms.Through our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of IoT.Our model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat landscape.This achievement marks a significant stride towards protecting the integrity of IoT infrastructure,ensuring operational resilience,and building privacy in this groundbreaking technology.
文摘Blockchain technology is regarded as the emergent security solution for many applications related to the Internet of Things(IoT).In concept,blockchain has a linear structure that grows with the number of transactions entered.This growth in size is the main obstacle to the blockchain,which makes it unsuitable for resource-constrained IoT environments.Moreover,conventional consensus algorithms such as PoW,PoS are very computationally heavy.This paper solves these problems by introducing a new lightweight blockchain structure and lightweight consensus algorithm.The Multi-Zone Direct Acyclic Graph(DAG)Blockchain(Multizone-DAG-Blockchain)framework is proposed for the fog-based IoT environment.In this context,fog computing technology is integrated with the IoT to offload IoT tasks to the fog nodes,thus preserving the energy consumption of the IoT devices.Both IoT and fog nodes are initially authenticated using a non-cloneable physical function-based validationmechanism(DPUF-VM)inwhichmultiple authentication certificates are verified in the blockchain.Each transaction is stored in a hash function in the blockchain using the lightweight CubeHash algorithm and signed by the Four-Q-Curve algorithm.In the cloud,sensitive data is stored as ciphertext.Fog nodes provide data security to avoid the energy consumption and complexity of IoT nodes.The fog node first performs a redundancy analysis using the Jaccard Similarity(JS)measure and sensitivity analysis using the Neutrosophic Neural Intelligent Network(N2IN)algorithm.A lightweight proof-of-authentication(PoAh)algorithm is presented and executed by the optimal consensus node selected by the bi-objective spiral optimization(BoSo)algorithm for transaction validation.The proposed work is modeled in Network Simulator 3.26(ns-3.26),and the performance is evaluated in terms of energy consumption,storage cost,response time,and throughput.
文摘为更加全面有效分析影响铁路设备全生命周期的环境因素,完善铁路室外环境监测,针对铁路环境监测的局限性,可采用以太网供电(Power over Ethernet,POE)和窄带物联网(NarrowBand Internet of Things,NB-IoT)的方式部署室内外环境监测系统。POE方式室内环境监测新增粉尘、气压、噪音、二氧化碳参数监测,系统部署简单高效,维护成本、系统故障率低,有利于后期设备的平滑升级。N B-IoT方式室外环境监测创新性地引入路基填土温湿度、导电性及钢轨温度、震动和位移参数监测,室内外环境监测数据按室内3D图加室外设备图形化的形式展示。该系统集成度高、价格低廉、功耗低、覆盖全,适合铁路多站点、环境复杂场景。
文摘城市地下管廊内布设了大量的管线,如燃气管道、网络通讯线路、电力线路等,由于地下环境复杂多变,存在着气体泄漏、爆炸、火灾等安全风险。针对这些问题,提出一种基于窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的地下管廊环境监测系统。该系统采用先进的传感器技术、NB-IoT技术、软件技术,系统主要分为数据采集模块、物联网云平台、远程监测系统三部分。数据采集模块以STM32作为主控单元连接各个传感器,采集温度、湿度、水位、可燃气体等数据,经过处理后利用NB-IoT网络上传到物联网云平台,远程监测系统调用物联网云平台的数据接口进行远程显示与预警。实验结果表明,系统在降低系统总体功耗的同时,能够实时、稳定地进行地下管廊环境监测,提前预防可能存在的风险。
文摘农田环境信息是制定农田管理策略的重要依据,为了实时稳定地采集农田环境信息,结合窄带物联网(Narrow Band Internet of Things,NB-IoT)的优势,设计并开发了基于NB-IoT的农田环境信息远程监测系统。该系统利用STM32F103RCT6单片机和传感器终端实时采集温度、湿度、光照强度、二氧化碳浓度、土壤湿度等农田环境数据,并通过NB-IoT网络将采集的数据传输至基于OneNET平台的农田环境监测云平台,用户可通过农田环境监测App或PC端访问农田环境监测云平台以获取农田环境监测数据。系统测试结果表明,该系统可实时获取温度、湿度、光照度、二氧化碳浓度、土壤湿度等农田环境信息,温度控制精度保持在±0.2℃,相对误差为0.57%;湿度控制精度保持在±2%RH,相对误差为1.66%;光照度控制精度保持在±63 lx,相对误差为0.24%;二氧化碳浓度控制精度保持在±45.46μmol/L,相对误差为0.34%;土壤湿度控制精度保持在±2%,相对误差为1.44%。该系统运行稳定,数据传输实时、准确,功能实用,操作简单,可大规模部署,为农业监控和物联网应用研究提供有效参考。
文摘随着科技的发展,环境保护问题越来越受到国家和社会大众的重视,同时越来越多的最新科技发展成果应用到环保领域中。本文结合窄带物联网技术(Narrow Band Internet of Things,NB-IoT)的优势和环境参数监测系统的具体需求,采用STM32L系列单片机、环境监测传感器及BC20通信模组以及阿里云物联网平台设计实现了一套基于NB-IoT的环境参数监测系统。通过实验验证,该系统实现效果良好,可推广应用于环保监测、智慧农业等领域。
文摘The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.
文摘为满足兔舍环境监测调控需求,同时摆脱传统布线网络局限性,缩减网络资费、电路元件和控制系统成本,本研究提出一种基于窄带物联网(Narrow Band Internet of Things,NB-IoT)的兔舍环境实时监测系统。系统基于Arduino开发板,使用移远BC260Y模块与消息队列遥测传输协议(Message Queuing Telemetry Transport,MQTT)实现网络连接,利用SGP30、MQ137、5516光敏电阻传感器等多种传感器实现兔舍内部声、光、水、温、气五方面实时监测。数据在本地、云端存储的同时,系统可根据阈值报警,协助创造兔的最佳生存环境。研究中对比了NB-IoT网络与Wi-Fi、LoRa等其他网络的异同,根据物联网三层架构详细介绍了系统搭建技术与过程,并系统分析了元器件价格,经核算,整机成本不超过400元。设备在空舍测试中,检测到CO_(2)浓度为420~440 ppm;MQ系列传感模组电压比值稳定于1;温度处于22~24℃;湿度上下波动10%;日光灯亮灭引起电压差2.6 V。进行了系统的网络与能耗测试,通过不同时间、场地、网络连接方式的对比,验证了本系统传输稳定可靠,能耗合理。系统使用MQTT通信协议的NB-IoT网络,平均每秒消息处理量(Transactions Per Second,TPS)为0.57,每分钟收发34.2条,上下浮动1条。系统运行时,电压约为12.5 V,电流约为0.42 A,平均功率为5.3 W。发生通信时,没有产生额外功耗,适用于实际养殖生产。本研究可为偏远或较大规模的养殖监测设备选取提供设备成本与网络选择参考价值。
