With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT termi...With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.展开更多
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot...In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.展开更多
The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power syst...The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power system and makes full use of the mobile internet,artificial intelligence,and other advanced information and communication technologies in order to realize the inter-human interaction of all things in all links of the power system.This article systematically presents to the national and international organizations and agencies in charge of UPIoT layer standardization the status quo of the research on the Internet of Things(IoT)-related industry standards system.It briefly describes the generic standard classification methods,layered architecture,conceptual model,and system tables in the UPIoT application layer.Based on the principles of inheritance,innovation,and practicability,this study divides the application layer into customer service,power grid operation,integrated energy,and enterprise operation,emerging business and analyzes the standard requirements of these five fields.This study also proposes a standard plan.Finally,it summarizes the research report and provides suggestions for a follow-up work.展开更多
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall...The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.展开更多
The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business s...The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.展开更多
The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power g...The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.展开更多
Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reli...Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.展开更多
The use of Internet of Things(IoT)applications become dominant in many systems.Its on-chip data processing and computations are also increasing consistently.The battery enabled and low leakage memory system at subthre...The use of Internet of Things(IoT)applications become dominant in many systems.Its on-chip data processing and computations are also increasing consistently.The battery enabled and low leakage memory system at subthreshold regime is a critical requirement for these IoT applications.The cache memory designed on Static Random-Access Memory(SRAM)cell with features such as low power,high speed,and process tolerance are highly important for the IoT memory system.Therefore,a process tolerant SRAM cell with low power,improved delay and better stability is presented in this research paper.The proposed cell comprises 11 transistors designed with symmetric approach for write operations and single ended circuit for read operations that exhibits an average dynamic power saving of 43.55%and 47.75%for write and 35.59%and 36.56%for read operations compared to 6 T and 8 T SRAM cells.The cell shows an improved write delay of 26.46%and 37.16%over 6 T and 8T and read delay is lowered by 50.64%and 72.90%against 6 T and 10 T cells.The symmetric design used in core latch to improve the write noise margin(WNM)by 17.78%and 6.67%whereas the single ended separate read circuit improves the Read Static Noise Margin(RSNM)by 1.88x and 0.33x compared to 6 T and 8T cells.The read power delay product and write power delay product are lower by 1.94x,1.39x and 0.17x,2.02x than 6 T and 8 T cells respectively.The lower variability from 5000 samples validates the robustness of the proposed cell.The simulations are carried out in Cadence virtuoso simulator tool with Generic Process Design Kit(GPDK)45 nm technology file in this work.展开更多
Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),a...Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ecosystems.Rectennas,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT systems.However,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless performance.This paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic rectennas.Their performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT applications.They have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this paper.Battery-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered devices.Moreover,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna system.Finally,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power capture.Example arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.展开更多
Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT gen...Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.展开更多
As the core industry of energy supply,the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety,economy and sustainability of power generation.At present,the tradition...As the core industry of energy supply,the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety,economy and sustainability of power generation.At present,the traditional thermal power plant warehousing and logistics system is faced with decentralized management,information lag,high dependence on manual and insufficient cost control,which cannot meet the needs of modern management.The in-depth application of Internet of Things(IoT)technology provides technical support for the digital upgrade of warehousing and logistics system,but there are still challenges in data integration depth,prediction model accuracy and adaptability to complex environment.The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT,AI,and automation technologies to create a smart management platform that covers full lifecycle tracking of material information,automated warehouse scheduling,intelligent logistics path optimization,and multidimensional data analysis.By deploying RFID tags,smart sensor terminals,and AGV logistics equipment,combined with recursive neural networks and reinforcement learning algorithms,the system achieves real-time material status monitoring,precise inventory demand forecasting,and dynamic optimization of transportation routes.展开更多
The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(...The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.展开更多
With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these d...With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these devices often handle personal information under limited resources,cryptographic algorithms must be executed efficiently.Their computational characteristics strongly affect system performance,making it necessary to analyze resource impact and predict usage under diverse configurations.In this paper,we analyze the phase-level resource usage of AES variants,ChaCha20,ECC,and RSA on an edge device and develop a prediction model.We apply these algorithms under varying parallelism levels and execution strategies across key generation,encryption,and decryption phases.Based on the analysis,we train a unified Random Forest model using execution context and temporal features,achieving R2 values up to 0.994 for power and 0.988 for temperature.Furthermore,the model maintains practical predictive performance even for cryptographic algorithms not included during training,demonstrating its ability to generalize across distinct computational characteristics.Our proposed approach reveals how execution characteristics and resource usage interacts,supporting proactive resource planning and efficient deployment of cryptographic workloads on edge devices.As our approach is grounded in phase-level computational characteristics rather than in any single algorithm,it provides generalizable insights that can be extended to a broader range of cryptographic algorithms that exhibit comparable phase-level execution patterns and to heterogeneous edge architectures.展开更多
As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power pla...As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.展开更多
为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检...为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检测电路,在电平交叉发生后开启NS SAR ADC的转换。二阶无源噪声整形电路积分过程只在事件触发后发生,从而能够根据输入信号的活跃度动态调节整体功耗。在1.8 V 180 nm CMOS工艺、采样率为40 kS/s、过采样率(OSR)为20、带宽为1 kHz下对该ADC进行仿真验证,结果表明信噪失真比(SNDR)达到87 dB,电路功耗为2.70μW,心电图信号输入时功耗仅为0.79μW,相较于传统等间隔奈奎斯特采样ADC,采样点减少了73%,在处理生物医学信号时实现了约5∶1的数据压缩比,Schreier品质因数(FoMs)和Walden品质因数(FoMw)分别为172.6 dB和67.0 fJ/conv.step。展开更多
异常检测旨在识别偏离正常模式的对象,广泛应用于金融、安防、医疗和网络等关键领域。在电力物联网中,异常检测对于应对频谱感知数据篡改攻击尤为重要,有助于保障系统运行稳定,减少潜在经济损失。然而,由于异常样本稀缺、分布不均且模...异常检测旨在识别偏离正常模式的对象,广泛应用于金融、安防、医疗和网络等关键领域。在电力物联网中,异常检测对于应对频谱感知数据篡改攻击尤为重要,有助于保障系统运行稳定,减少潜在经济损失。然而,由于异常样本稀缺、分布不均且模式复杂,传统监督方法难以适应动态变化的实际环境,亟需一种高效且鲁棒的检测方案。为此,本文提出一种基于模糊信息粒球的多尺度异常检测方法(Fuzzy Information Granular-Ball based Outlier Detection,FBOD),通过融合粗粒度与细粒度特征综合评估数据的异常程度。具体工作包含:1)利用粒球模型自适应划分样本集,并结合密度和邻域信息计算粗粒度异常分数,以捕捉全局异常模式;2)构建粒球模糊相似距离矩阵,提升对局部异常的识别能力;3)引入自然邻域异常度量,增强复杂模式的识别效果。实验结果表明,FBOD在多个标准异常检测数据集上AUC均在0.79以上,在电力物联网SSDF攻击数据集上AUC超过0.88,显著优于对比方法。该方法在提升电力系统安全性方面展现出良好适应性与检测性能,同时为其他领域的异常检测任务提供了新思路和技术支持。展开更多
基金supported by National Key R&D Program of China(No.2022YFB3105101).
文摘With more and more IoT terminals being deployed in various power grid business scenarios,terminal reliability has become a practical challenge that threatens the current security protection architecture.Most IoT terminals have security risks and vulnerabilities,and limited resources make it impossible to deploy costly security protection methods on the terminal.In order to cope with these problems,this paper proposes a lightweight trust evaluation model TCL,which combines three network models,TCN,CNN,and LSTM,with stronger feature extraction capability and can score the reliability of the device by periodically analyzing the traffic behavior and activity logs generated by the terminal device,and the trust evaluation of the terminal’s continuous behavior can be achieved by combining the scores of different periods.After experiments,it is proved that TCL can effectively use the traffic behaviors and activity logs of terminal devices for trust evaluation and achieves F1-score of 95.763,94.456,99.923,and 99.195 on HDFS,BGL,N-BaIoT,and KDD99 datasets,respectively,and the size of TCL is only 91KB,which can achieve similar or better performance than CNN-LSTM,RobustLog and other methods with less computational resources and storage space.
基金supported by National Natural Science Foundation of China(12174350)Science and Technology Project of State Grid Henan Electric Power Company(5217Q0240008).
文摘In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity.
基金supported by Science and Technology Foundation of State Grid Corporation of China(Ubiquitous Power Internet of Things Technical Standard System)5442HL 190008National Key Research and Development Program of China(2020YFB0905900)。
文摘The ubiquitous power Internet of Things(UPIoT)is an intelligent service system with comprehensive state perception,efficient processing,and flexible application of information.It focuses on each link of the power system and makes full use of the mobile internet,artificial intelligence,and other advanced information and communication technologies in order to realize the inter-human interaction of all things in all links of the power system.This article systematically presents to the national and international organizations and agencies in charge of UPIoT layer standardization the status quo of the research on the Internet of Things(IoT)-related industry standards system.It briefly describes the generic standard classification methods,layered architecture,conceptual model,and system tables in the UPIoT application layer.Based on the principles of inheritance,innovation,and practicability,this study divides the application layer into customer service,power grid operation,integrated energy,and enterprise operation,emerging business and analyzes the standard requirements of these five fields.This study also proposes a standard plan.Finally,it summarizes the research report and provides suggestions for a follow-up work.
基金supported by the National Key Research and Development Program of China(grant number 2019YFE0123600)。
文摘The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT.
基金National Natural Science Foundation of China,Grant/Award Number:52177085DGRI‐CSG Innovative Project,Grant/Award Number:210000KK52220036。
文摘The integration of electricity technology and information technology,such as the Internet of Things(IoT),enables the construction of new power systems,along with the inno-vation of application scenarios and business scope.The key technologies of power IoT and the data flow process are summarised first.The IoT technology and application scenario requirements of power generation,transmission,loading,and storage of new power systems are studied.Thus,the nature of the collaborative development of the digital power grid and the IoT is demonstrated from the perspective of data processing in power IoT and application requirements in power systems.The key problems and so-lutions faced by the power IoT under the digital transformation are described,and the cross-integration of key technologies and promotion of application scenario innovation are prospected.Finally,the key issues of future technological development were dis-cussed,providing reference ideas for fully leveraging the value of energy and electricity data production factors and promoting the construction of a digital electricity ecosystem.
文摘The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery.
基金supported by Science and Technology Project of China Southern Power Grid Company Limited under Grant Number 036000KK52200058(GDKJXM20202001).
文摘Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.
文摘The use of Internet of Things(IoT)applications become dominant in many systems.Its on-chip data processing and computations are also increasing consistently.The battery enabled and low leakage memory system at subthreshold regime is a critical requirement for these IoT applications.The cache memory designed on Static Random-Access Memory(SRAM)cell with features such as low power,high speed,and process tolerance are highly important for the IoT memory system.Therefore,a process tolerant SRAM cell with low power,improved delay and better stability is presented in this research paper.The proposed cell comprises 11 transistors designed with symmetric approach for write operations and single ended circuit for read operations that exhibits an average dynamic power saving of 43.55%and 47.75%for write and 35.59%and 36.56%for read operations compared to 6 T and 8 T SRAM cells.The cell shows an improved write delay of 26.46%and 37.16%over 6 T and 8T and read delay is lowered by 50.64%and 72.90%against 6 T and 10 T cells.The symmetric design used in core latch to improve the write noise margin(WNM)by 17.78%and 6.67%whereas the single ended separate read circuit improves the Read Static Noise Margin(RSNM)by 1.88x and 0.33x compared to 6 T and 8T cells.The read power delay product and write power delay product are lower by 1.94x,1.39x and 0.17x,2.02x than 6 T and 8 T cells respectively.The lower variability from 5000 samples validates the robustness of the proposed cell.The simulations are carried out in Cadence virtuoso simulator tool with Generic Process Design Kit(GPDK)45 nm technology file in this work.
基金supported by the University of Technology Sydney (UTS) Chancellor’s Postdoctoral Fellowship (PRO18-6147)Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA) (PRO20-9959)
文摘Far-field wireless power transfer(WPT)is a major breakthrough technology that will enable the many anticipated ubiquitous Internet of Things(IoT)applications associated with fifth generation(5G),sixth generation(6G),and beyond wireless ecosystems.Rectennas,which are the combination of rectifying circuits and antennas,are the most critical components in far-field WPT systems.However,compact application devices require even smaller integrated rectennas that simultaneously have large electromagnetic wave capture capabilities,high alternating current(AC)-to-direct current(DC)(AC-to-DC)conversion efficiencies,and facilitate a multifunctional wireless performance.This paper reviews various rectenna miniaturization techniques such as meandered planar inverted-F antenna(PIFA)rectennas;miniaturized monopole-and dipole-based rectennas;fractal loop and patch rectennas;dielectric-loaded rectennas;and electrically small near-field resonant parasitic rectennas.Their performance characteristics are summarized and then compared with our previously developed electrically small Huygens rectennas that are proven to be more suitable for IoT applications.They have been tailored,for example,to achieve batteryfree IoT sensors as is demonstrated in this paper.Battery-free,wirelessly powered devices are smaller and lighter in weight in comparison to battery-powered devices.Moreover,they are environmentally friendly and,hence,have a significant societal benefit.A series of high-performance electrically small Huygens rectennas are presented including Huygens linearly-polarized(HLP)and circularly-polarized(HCP)rectennas;wirelessly powered IoT sensors based on these designs;and a dual-functional HLP rectenna and antenna system.Finally,two linear uniform HLP rectenna array systems are considered for significantly larger wireless power capture.Example arrays illustrate how they can be integrated advantageously with DC or radio frequency(RF)power-combining schemes for practical IoT applications.
基金supported in part by National Key R&D Program of China under Grant 2016YFE0200900part by Scientific Research Program of Beijing Municipal Commission of Education under Grant KM201910853003part by Major projects of Beijing Municipal Science and Technology Commission under Grant Z181100003218010
文摘Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.
文摘As the core industry of energy supply,the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety,economy and sustainability of power generation.At present,the traditional thermal power plant warehousing and logistics system is faced with decentralized management,information lag,high dependence on manual and insufficient cost control,which cannot meet the needs of modern management.The in-depth application of Internet of Things(IoT)technology provides technical support for the digital upgrade of warehousing and logistics system,but there are still challenges in data integration depth,prediction model accuracy and adaptability to complex environment.The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT,AI,and automation technologies to create a smart management platform that covers full lifecycle tracking of material information,automated warehouse scheduling,intelligent logistics path optimization,and multidimensional data analysis.By deploying RFID tags,smart sensor terminals,and AGV logistics equipment,combined with recursive neural networks and reinforcement learning algorithms,the system achieves real-time material status monitoring,precise inventory demand forecasting,and dynamic optimization of transportation routes.
基金funded by the Deanship of Scientific Research at Najran University for this research through a Grant(NU/RG/SERC/12/50)under the Research Groups at Najran University,Saudi Arabia.
文摘The Internet of Things(IoT)consists of interconnected smart devices communicating and collecting data.The Routing Protocol for Low-Power and Lossy Networks(RPL)is the standard protocol for Internet Protocol Version 6(IPv6)in the IoT.However,RPL is vulnerable to various attacks,including the sinkhole attack,which disrupts the network by manipulating routing information.This paper proposes the Unweighted Voting Method(UVM)for sinkhole node identification,utilizing three key behavioral indicators:DODAG Information Object(DIO)Transaction Frequency,Rank Harmony,and Power Consumption.These indicators have been carefully selected based on their contribution to sinkhole attack detection and other relevant features used in previous research.The UVM method employs an unweighted voting mechanism,where each voter or rule holds equal weight in detecting the presence of a sinkhole attack based on the proposed indicators.The effectiveness of the UVM method is evaluated using the COOJA simulator and compared with existing approaches.Notably,the proposed approach fulfills power consumption requirements for constrained nodes without increasing consumption due to the deployment design.In terms of detection accuracy,simulation results demonstrate a high detection rate ranging from 90%to 100%,with a low false-positive rate of 0%to 0.2%.Consequently,the proposed approach surpasses Ensemble Learning Intrusion Detection Systems by leveraging three indicators and three supporting rules.
基金supported in part by the National Research Foundation of Korea(NRF)(No.RS-2025-00554650)supported by the Chung-Ang University research grant in 2024。
文摘With the accelerated growth of the Internet of Things(IoT),real-time data processing on edge devices is increasingly important for reducing overhead and enhancing security by keeping sensitive data local.Since these devices often handle personal information under limited resources,cryptographic algorithms must be executed efficiently.Their computational characteristics strongly affect system performance,making it necessary to analyze resource impact and predict usage under diverse configurations.In this paper,we analyze the phase-level resource usage of AES variants,ChaCha20,ECC,and RSA on an edge device and develop a prediction model.We apply these algorithms under varying parallelism levels and execution strategies across key generation,encryption,and decryption phases.Based on the analysis,we train a unified Random Forest model using execution context and temporal features,achieving R2 values up to 0.994 for power and 0.988 for temperature.Furthermore,the model maintains practical predictive performance even for cryptographic algorithms not included during training,demonstrating its ability to generalize across distinct computational characteristics.Our proposed approach reveals how execution characteristics and resource usage interacts,supporting proactive resource planning and efficient deployment of cryptographic workloads on edge devices.As our approach is grounded in phase-level computational characteristics rather than in any single algorithm,it provides generalizable insights that can be extended to a broader range of cryptographic algorithms that exhibit comparable phase-level execution patterns and to heterogeneous edge architectures.
文摘As global climate change intensifies,the power industry-a major source of carbon emissions-plays a pivotal role in achieving carbon peaking and neutrality goals through its low-carbon transition.Traditional power plants’carbon management systems can no longer meet the demands of high-precision,real-time monitoring.Smart power plants now offer innovative solutions for carbon emission tracking and intelligent analysis by integrating IoT,big data,and AI technologies.Current research predominantly focuses on optimizing individual processes,lacking systematic exploration of comprehensive dynamic monitoring and intelligent decision-making across the entire workflow.To address this gap,we propose a smart carbon emission monitoring and analysis platform for power plants that integrates IoT sensing,multimodal data analytics,and AI-driven decision-making.The platform establishes a multi-source sensor network to collect emissions data throughout the fuel combustion,auxiliary equipment operation,and waste treatment processes.Combining carbon emission factor analysis with machine learning models enables real-time emission calculations and utilizes long short-term memory networks to predict future emission trends.
文摘为解决传统电平交叉模数转换器(LC ADC)精度较低和噪声整形逐次逼近寄存器(NS SAR)ADC功耗较大的问题,提出了一种应用于移动物联网(IoT)随机稀疏信号采集的LC-NS SAR ADC。在NS SAR ADC前端插入8 bit的LC ADC作为输入信号活跃度的预检测电路,在电平交叉发生后开启NS SAR ADC的转换。二阶无源噪声整形电路积分过程只在事件触发后发生,从而能够根据输入信号的活跃度动态调节整体功耗。在1.8 V 180 nm CMOS工艺、采样率为40 kS/s、过采样率(OSR)为20、带宽为1 kHz下对该ADC进行仿真验证,结果表明信噪失真比(SNDR)达到87 dB,电路功耗为2.70μW,心电图信号输入时功耗仅为0.79μW,相较于传统等间隔奈奎斯特采样ADC,采样点减少了73%,在处理生物医学信号时实现了约5∶1的数据压缩比,Schreier品质因数(FoMs)和Walden品质因数(FoMw)分别为172.6 dB和67.0 fJ/conv.step。
文摘异常检测旨在识别偏离正常模式的对象,广泛应用于金融、安防、医疗和网络等关键领域。在电力物联网中,异常检测对于应对频谱感知数据篡改攻击尤为重要,有助于保障系统运行稳定,减少潜在经济损失。然而,由于异常样本稀缺、分布不均且模式复杂,传统监督方法难以适应动态变化的实际环境,亟需一种高效且鲁棒的检测方案。为此,本文提出一种基于模糊信息粒球的多尺度异常检测方法(Fuzzy Information Granular-Ball based Outlier Detection,FBOD),通过融合粗粒度与细粒度特征综合评估数据的异常程度。具体工作包含:1)利用粒球模型自适应划分样本集,并结合密度和邻域信息计算粗粒度异常分数,以捕捉全局异常模式;2)构建粒球模糊相似距离矩阵,提升对局部异常的识别能力;3)引入自然邻域异常度量,增强复杂模式的识别效果。实验结果表明,FBOD在多个标准异常检测数据集上AUC均在0.79以上,在电力物联网SSDF攻击数据集上AUC超过0.88,显著优于对比方法。该方法在提升电力系统安全性方面展现出良好适应性与检测性能,同时为其他领域的异常检测任务提供了新思路和技术支持。
