随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖...随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖不均等挑战。这些挑战不仅影响用户体验,还限制了物联网应用的进一步发展。因此,研究面向物联网的NB-IoT信号优化方法具有重要意义。文章深入研究面向物联网的NB-IoT信号优化方法,提出多种有效的优化策略和技术手段。展开更多
由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定...由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。展开更多
The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is w...The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.展开更多
The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the d...The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.展开更多
The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to...The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.展开更多
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 i...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.展开更多
Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the...Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.展开更多
The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which ...The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.展开更多
Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve it...Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.展开更多
Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of thi...Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of things (devices) that can be connected through the internet. The purpose: this paper aims to explore the concept of the Internet of Things (IoT) generally and outline the main definitions of IoT. The paper also aims to examine and discuss the obstacles and potential benefits of IoT in Saudi universities. Methodology: the researchers reviewed the previous literature and focused on several databases to use the recent studies and research related to the IoT. Then, the researchers also used quantitative methodology to examine the factors affecting the obstacles and potential benefits of IoT. The data were collected by using a questionnaire distributed online among academic staff and a total of 150 participants completed the survey. Finding: the result of this study reveals there are twelve factors that affect the potential benefits of using IoT such as reducing human errors, increasing business income and worker’s productivity. It also shows the eighteen factors which affect obstacles the IoT use, for example sensors’ cost, data privacy, and data security. These factors have the most influence on using IoT in Saudi universities.展开更多
探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。...探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。详细介绍基于NB-IoT的智慧水表系统和基于eMTC的车辆跟踪系统的设计与实现,展示这些系统在提高城市管理效率、物流监控等方面的积极作用。针对网络覆盖与信号质量、数据安全与隐私保护、功耗与续航等关键技术挑战,提出相应的解决方案。最后总结NB-IoT和eMTC的广阔应用前景和市场潜力,并对未来技术发展和应用趋势进行展望。展开更多
随着物联网技术的快速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)技术因其低功耗、广覆盖、大容量等特性,成为物联网的重要连接方式。针对NB-IoT网络性能优化问题,提出基于自适应控制算法的优化方案,提高其可靠性、容量...随着物联网技术的快速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)技术因其低功耗、广覆盖、大容量等特性,成为物联网的重要连接方式。针对NB-IoT网络性能优化问题,提出基于自适应控制算法的优化方案,提高其可靠性、容量及能效。通过仿真实验,验证该方案的有效性和性能优势。此外,基于该算法,采用终端感知、网络通信、数据处理以及应用表现4层系统设计架构,设计基于自适应控制算法的NB-IoT物联网系统,满足不断增长的物联网应用需求。展开更多
文章深入研究物联网中的窄带物联网(Narrow Band Internet of Things,NB-IoT)技术,重点探讨基于省电模式(Power Saving Mode,PSM)机制的优化方法,分析NB-IoT的数学原理,研究一种基于PSM机制的动态调整算法,并利用MATLAB平台上的Simulin...文章深入研究物联网中的窄带物联网(Narrow Band Internet of Things,NB-IoT)技术,重点探讨基于省电模式(Power Saving Mode,PSM)机制的优化方法,分析NB-IoT的数学原理,研究一种基于PSM机制的动态调整算法,并利用MATLAB平台上的Simulink工具进行仿真实验。实验结果显示,相比传统方法,文章方法能显著降低平均功耗,在实际应用中具有有效性和稳定性。展开更多
文摘随着物联网技术的飞速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)作为一种低功耗、广覆盖、大连接的无线通信技术,逐渐成为连接物理世界与数字世界的桥梁。然而,在实际应用中,NB-IoT信号面临着诸如信号衰减、干扰、覆盖不均等挑战。这些挑战不仅影响用户体验,还限制了物联网应用的进一步发展。因此,研究面向物联网的NB-IoT信号优化方法具有重要意义。文章深入研究面向物联网的NB-IoT信号优化方法,提出多种有效的优化策略和技术手段。
文摘由于地址跳变是物联网主动防御的一种有效手段,但因跳变资源匮乏、可预见性以及数据包混淆度低已经成为制约物联网地址跳变的主要问题。为此,提出一种基于双模式端址跳变的主动防御方法。该方法设计了双模式端址选择算法,通过动态确定虚拟端址生成策略,以通信时间为阈值,扩大端址跳变空间,从而解决地址池资源受限问题。同时,还构建了双虚拟端址跳变方法,通过动态分配和同步虚拟接收和发送地址,提升数据包混淆度,增强跳变的不可预见性。并且基于SDN(Software Defined Network)设计了流表双向同步机制,实现流表的动态下发和同步,以保证端址跳变的一致性。实验结果表明,该方法能有效提升地址跳变的多样性和不可预测性,显著增强抵御嗅探攻击的能力。
文摘The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R333)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The concept of smart houses has grown in prominence in recent years.Major challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device itself.Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features.This paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in homes.We have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT devices.Our system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing devices.We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server.The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings.It is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation systems.Additionally,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber threats.The trial results support the proposed system and demonstrate its potential for use in everyday life.
基金This research is funded by 2023 Henan Province Science and Technology Research Projects:Key Technology of Rapid Urban Flood Forecasting Based onWater Level Feature Analysis and Spatio-Temporal Deep Learning(No.232102320015)Henan Provincial Higher Education Key Research Project Program(Project No.23B520024)a Multi-Sensor-Based Indoor Environmental Parameters Monitoring and Control System.
文摘The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.
基金The authors extend their appreciation to Prince Sattam bin Abdulaziz University for funding this research work through the Project Number(PSAU/2023/01/27268).
文摘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.
基金supported in part by the National Natural Science Foundation of China (62072248, 62072247)the Jiangsu Agriculture Science and Technology Innovation Fund (CX(21)3060)。
文摘Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.61872289 and 62172266in part by the Henan Key Laboratory of Network Cryptography Technology LNCT2020-A07the Guangxi Key Laboratory of Trusted Software under Grant No.KX202308.
文摘The Internet of Medical Things(IoMT)is an application of the Internet of Things(IoT)in the medical field.It is a cutting-edge technique that connects medical sensors and their applications to healthcare systems,which is essential in smart healthcare.However,Personal Health Records(PHRs)are normally kept in public cloud servers controlled by IoMT service providers,so privacy and security incidents may be frequent.Fortunately,Searchable Encryption(SE),which can be used to execute queries on encrypted data,can address the issue above.Nevertheless,most existing SE schemes cannot solve the vector dominance threshold problem.In response to this,we present a SE scheme called Vector Dominance with Threshold Searchable Encryption(VDTSE)in this study.We use a Lagrangian polynomial technique and convert the vector dominance threshold problem into a constraint that the number of two equal-length vectors’corresponding bits excluding wildcards is not less than a threshold t.Then,we solve the problem using the proposed technique modified in Hidden Vector Encryption(HVE).This technique makes the trapdoor size linear to the number of attributes and thus much smaller than that of other similar SE schemes.A rigorous experimental analysis of a specific application for privacy-preserving diabetes demonstrates the feasibility of the proposed VDTSE scheme.
基金supported in part by National Key Research and Development Program of China under Grant 2021YFB2900404.
文摘Due to the limited computational capability and the diversity of the Internet of Things devices working in different environment,we consider fewshot learning-based automatic modulation classification(AMC)to improve its reliability.A data enhancement module(DEM)is designed by a convolutional layer to supplement frequency-domain information as well as providing nonlinear mapping that is beneficial for AMC.Multimodal network is designed to have multiple residual blocks,where each residual block has multiple convolutional kernels of different sizes for diverse feature extraction.Moreover,a deep supervised loss function is designed to supervise all parts of the network including the hidden layers and the DEM.Since different model may output different results,cooperative classifier is designed to avoid the randomness of single model and improve the reliability.Simulation results show that this few-shot learning-based AMC method can significantly improve the AMC accuracy compared to the existing methods.
文摘Internet of Things (IoT) among of all the technology revolutions has been considered the next evolution of the internet. IoT has become a far more popular area in the computing world. IoT combined a huge number of things (devices) that can be connected through the internet. The purpose: this paper aims to explore the concept of the Internet of Things (IoT) generally and outline the main definitions of IoT. The paper also aims to examine and discuss the obstacles and potential benefits of IoT in Saudi universities. Methodology: the researchers reviewed the previous literature and focused on several databases to use the recent studies and research related to the IoT. Then, the researchers also used quantitative methodology to examine the factors affecting the obstacles and potential benefits of IoT. The data were collected by using a questionnaire distributed online among academic staff and a total of 150 participants completed the survey. Finding: the result of this study reveals there are twelve factors that affect the potential benefits of using IoT such as reducing human errors, increasing business income and worker’s productivity. It also shows the eighteen factors which affect obstacles the IoT use, for example sensors’ cost, data privacy, and data security. These factors have the most influence on using IoT in Saudi universities.
文摘探讨物联网(Internet of Things,IoT)领域的两大关键技术,即窄带物联网(Narrow Band Internet of Things,NB-IoT)和增强型机器类型通信(enhanced Machine-Type Communication,eMTC),分析它们在不同应用场景下的实际应用和面临的挑战。详细介绍基于NB-IoT的智慧水表系统和基于eMTC的车辆跟踪系统的设计与实现,展示这些系统在提高城市管理效率、物流监控等方面的积极作用。针对网络覆盖与信号质量、数据安全与隐私保护、功耗与续航等关键技术挑战,提出相应的解决方案。最后总结NB-IoT和eMTC的广阔应用前景和市场潜力,并对未来技术发展和应用趋势进行展望。
文摘随着物联网技术的快速发展,窄带物联网(Narrow Band Internet of Things,NB-IoT)技术因其低功耗、广覆盖、大容量等特性,成为物联网的重要连接方式。针对NB-IoT网络性能优化问题,提出基于自适应控制算法的优化方案,提高其可靠性、容量及能效。通过仿真实验,验证该方案的有效性和性能优势。此外,基于该算法,采用终端感知、网络通信、数据处理以及应用表现4层系统设计架构,设计基于自适应控制算法的NB-IoT物联网系统,满足不断增长的物联网应用需求。
文摘文章深入研究物联网中的窄带物联网(Narrow Band Internet of Things,NB-IoT)技术,重点探讨基于省电模式(Power Saving Mode,PSM)机制的优化方法,分析NB-IoT的数学原理,研究一种基于PSM机制的动态调整算法,并利用MATLAB平台上的Simulink工具进行仿真实验。实验结果显示,相比传统方法,文章方法能显著降低平均功耗,在实际应用中具有有效性和稳定性。